Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation and Pressure

*Seiki Chiba and Mikio Waki*

## **Abstract**

Most of the conventional sensors used for measuring deformation, pressure, etc., use metal, ceramics, piezo, or the like. Many of them are very rigid, and when the object is deformed or when the pressure on the object changes currently, it is necessary to arrange a large number of sensors with different conditions side by side. However, it is still difficult to measure all changes over time. With the newly developed dielectric elastomer sensor, even a very thin (0.1–0.2 mm) elastomer thickness could be deformed in difficult environments (e.g., places with large temperature changes or large vibrations), and it would be possible to measure any pressure changes due to its deformation. By applying this sensor, it can be used as a position sensor (including a three-dimensional sensor) or an acceleration sensor, so that it could be applied to the control of the arms and legs of a robot, smart shoes, and the like.

**Keywords:** dielectric elastomer, sensor, pressure, electrodes, CNT, carbon black, carbon grease, load cells, resin block, flange, sheet, SS curve

## **1. Introduction**

Materials consisting of new metal materials, high-performance polymer materials, fine ceramics, and composite materials are expected to be used in cutting-edge technologies that support various industries and economies in the twenty-first century, along with electronics and biotechnology [1].

Piezoelectric composite materials [2], conductive polymer materials [3], bimetals [4], etc., are known as composite materials that can be applied as sensor materials. The former two are excellent materials that combine the advantages of both organic materials with inorganic materials in a matrix. The latter combines dissimilar metals to enable temperature sensors.

Engineering sensors are extremely simple devices at the present stage. As seen in partially integrated pressure sensors and magnetic sensors, they are becoming more intelligent, but they have not yet reached the level of intelligent sensors. As an example of the development of an integrated pressure sensor, T. Sarutani et al.

prototyped an integrated pressure sensor using a shear gauge as a piezo resistance gauge [2]. The temperature of the pressure sensor is corrected on the same chip. As a feature of this sensor, the pressure that can be measured is only the pressure at the time when the pressure is applied, and accurate measurement cannot be performed unless the temperature is corrected.

As a sensor for measuring elongation, a strain sensor can be mentioned. The type using a piezo is well known [5]. However, the measurement of elongation is quite limited. Stretch sensors that support greater elongation using elastomers have recently emerged. Electrodes were attached to the top and bottom of the elastomer, and the elements were stacked in several stages for use [6]. It is an application of the so-called dielectric elastomer (DE). However, since a plurality of these elements are laminated, it is considered that the flexibility is not so high, so it is considered that the element is not sufficiently deformed unless the force required for deformation is large.

In this experiment, carbon grease, carbon black, and single-wall carbon nanotubes (SWCNTs) were applied to thin elastomer films of silicon, acrylic, and hydrogenated nitrile rubber (HNBR) as electrodes, and each film was used as a pressure sensor. Then, how each film behaved as a pressure sensor was observed. We also verified the performance as a stretch sensor by using the same combination of film and electrodes as above. In this way, we discussed whether a film with the same structure could be used as an intelligent sensor capable of two different types of sensing.

## **2. Background of dielectric elastomers (DEs)**

DE was first created in 1990 by S. Chiba, R. Pelrine et al. of the Stanford Research Institute in the United States. After that, various researchers started their own development [7–28]. Currently, that development is spreading to DE actuators (DEA) and DE power generators (DEGs) that generate electricity by reversing the drive [28]. The concept of the DE sensor was introduced in the latter half of the 1990s, and research and development are currently underway in this field as well [8–25].

The structure and principle of the DE sensor (see **Figure 1**) are the same as those of the DEA and DEG, and the driving principle is that the output of DEA and its elongation are in inverse proportion to each other. Also, the change in capacitance of the DE sensor and DEA and its elongation are in direct proportional relationship [9] (see **Figure 2**).

**Figure 1.** *Structure and principle of DE sensor.*

**Figure 2.** *Relationship between the elongation of the DE sensor and capacitance.*

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

The relationship between DEA and capacitance can be expressed by the following equation;

$$\mathbf{C} = \varepsilon \frac{\mathbf{S}}{d} \tag{1}$$

Here, C is capacity (F), ε is the dielectric constant of polymer film (F/m), S is area of Electrode (m<sup>2</sup> ), and d is distance between electrodes (m).

So far, a wide range of research and development have been carried out for various applications. This research also includes the examination of material types and DE sensor shapes [10–24]. First, as given an example of the material, H. Sun et al. claimed that SiR-Fe containing 8% FeCl3 could be effectively used as a medical DE sensor [10]. Perhaps this polymer is hard and unsuitable for precision sensors. B. O'brien et al. made a DE sensor by using two layers of thick silicone elastomer with a size of 20 mm x 70 mm and applying carbon powder to the electrodes [11]. M. Han et al. proposed a pressure sensor in which the medical wearable sensor consisted of a carbon nanotubepolydimethylsiloxane (CNT-PDMS) composite electrode and a porous polymer dielectric layer [12]. The concept is interesting, but this wearable sensor is unlikely to work well because the electrodes were not flexible enough and polymers used were hard.

H. Liebscher et al. tried to increase the dielectric constant of polyurethane by adding barium titanate to the polyurethane elastomer [13]. J. Bae et al. tried to control the robot by feeding back the information [14]. C. Briggs et al. tried to develop a tactile sensor for a robot's hand by attaching a dome-shaped DE sensor or DE sensor mounted on a cylindrical pad to a robot gripper [15]. L. Agostini et al. also conducted preliminary experimental studies seeking the most influential elements in order to optimize the performance of hemispherical anti-collision sensors [16].

As an example of circuit studies, K. Jung et al. used modulation technology to realize DEA operation and its detection and used a system that mixed a low-frequency signal for operation and a high-frequency signal with small amplitude for detection [17]. H. Bose et al. tried to create a thicker mat-shaped pressure sensor because the sensor signal was too small to detect in the sheet-shaped DE [18]. Finally, as an application example, J. Bae et al. also explained applications using PVDF-based materials, silicones, and acrylic materials [14]. M. Rosenthal et al. discussed diagnostic tools for industrial equipment and applicable sensors for system monitoring [19].

R. Walker et al. conducted a unique study of attaching a DE sensor to a wetsuit for divers as an underwater application for DE. [20]. C. Larson et al. also arranged a large number of DE sensors on participants' bodies and tried to apply them to a virtual reality game that would enable them to play the game by moving their bodies [21]. As another system research example, a DEA system could be developed that combines a DE sensor and an actuator to assist the movement of a patient's fingers, hands, feet, etc., and to accurately evaluate their rehabilitation progress [22]. R Venkatraman et al. tried to measure blood pressure using a DEA cuff device [23].

In this way, DEA can be used simultaneously an actuator and a pressure sensor and/or a position sensor [24]. In the near future, it is expected that the arms and legs of intelligent robots, nursing care equipment, and the like will become possible. In addition, a DEA/sensor system that assists the movement of the patient's fingers, hands, feet, etc., and accurately evaluates the rehabilitation situation will be possible [25]. In developing such devices and devices, it is necessary to improve the performance of DE sensors. The major factors will be to improve the conductivity of the electrode and the flexibility of the electrode [26, 27].

## **3. Experimental procedure**

In this experiment, carbon grease, carbon black, and SWCNT (ZEONANO®-SG101, Zeon Corp., Tokyo, Japan) were used as electrodes for four types of elastomers to create DE pressure sensors, and the performance of each was compared. If necessary, soft resin blocks were laid under the elastomer films to further increase the sensitivity of the DE sensor. Similarly, for the stretch sensor, carbon grease, carbon black, and SWCNT electrodes were used for each, and the performance was compared.

### **3.1 Test materials**

In this study, we used a US-made acrylic material (3 M/4905), a US-made acrylic distortion-corrected film, and a silicon material (ELASTOSIL FILM 2030250). In addition, hydrogenated acrylonitrile butadiene rubber (HNBR) film synthesized by Zeon Corp. was used. The thickness of the acrylic material and silicon of the test piece was 0.5 mm, and the thickness of HNBR was 0.2 mm. HNBR is a material that absorbs the vibrations of automobile engines and is too hard as it is, so we optimized the amount of cross-linking agent added and cut a part of the double bond to improve the elongation [26]. Using the above films and resin blocks, a tensile test and dynamic viscoelasticity test were also performed to correlate sensor performance with material differences.

## *3.1.1 Tensile test*

A tensile test of the dumbbell-shaped test piece as shown in **Figure 3** was performed using the Orientech tabletop material tester STA-1150. The displacement velocities were set to 100 mm/min, 200 mm/min, 300 mm/min, and 500 mm/min in order to evaluate the effect of the displacement velocities on the mechanical properties.

## *3.1.2 Dynamic viscoelasticity test*

The dynamic viscoelasticity tester used was an MCR302 rheometer manufactured by Anton Paar, and the viscoelastic behavior of acrylic DE and silicone DE in frequency dependence was investigated. The measurement conditions were a frequency of 0.1–20 Hz, a shear strain of 1%, and a room temperature of 20° C, and the size of the test piece was 25 mm in diameter.

### *3.1.3 Elastomer hardness measurements*

In this study, the hardness of the acrylic film (3 M/4905), the strain-corrected film of the acrylic film, silicon (ELASTOSIL FILM 2030250), HNBR film, and resin block

**Figure 3.** *A tensile test of the dumbbell-shaped test piece.*

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

materials were measured using Askar C method. The details of the resin blocks are shown in Section 3.3.

Askar C is a durometer C (spring type hardness tester) specified in SRIS0101 (Japan Rubber Association standard) and is a measuring instrument for measuring hardness. If Asker C is described as 20 in the physical properties table, it means that the value measured by the Asker C hardness tester is 20, and contrary to the needle insertion degree and consistency test, the larger the number, the harder the material.

#### **3.2 How to make a donut-shaped sheet DE sensor**

Each elastomer was cut into a circle and molded to an outer diameter of approximately 8 cm using 100% prestrain. A carbon grease electrode, a carbon black electrode, and an SWCNT electrode were attached to the upper and lower parts of these films to form a donut-shaped sheet DE sensor (see **Figure 4**). Carbon grease was applied as electrodes with a small brush. As the carbon black electrode, a carbon black spray (carbon black dissolved in a solvent, mixed with a small amount of binder, and packed in a spray can) was used. In the case of SWCNT electrodes, the electrodes were attached using SWCNT spray (ZEONANO®-SG101/SWCNT dissolved in a solvent, mixed with a small amount of binder, and packed in a spray can) [25]. The thickness of the carbon grease electrode was 100 μm, and the thickness was confirmed using a double-scan high-precision laser measuring instrument (LT-9500 & LT-9010 M) manufactured by Keyence. The thickness of carbon black was 70 μm and that of SWCNT was 50 μm, and the thickness was similarly confirmed using a double-scan high-precision laser measuring instrument.

## **3.3 Pressure measurement/evaluation methods using donut-shaped sheet DE sensors**

As an evaluation method for the various DE pressure sensors described above, pressure was applied using the evaluation system shown in **Figure 5** with a resin block as a cushioning material (Blue Forest Trading LLC: KG-01) laid under the DE sensor. It was measured using a small precision vise attached to the Z-axis precision stage, and a load is applied from the top of the donut-shaped DE sensor to deform it. The experimental donut-shaped DE sensor is installed at the top of the load cell. The load was measured using a load cell capable of measuring up to 20 kg. The analog signal output from the load cell was taken into the CPU via a dedicated AD conversion IC (HX711). After that, the calibration process was performed by the CPU, and then the measured value was displayed on the LDC. The experimental equipment was calibrated using weights of 10 g, 500 g, 1 kg, 5 kg, and 15 kg, and it was confirmed that the error at each measurement point was within 1%. The electrostatic capacity of the

**Figure 4.** *An example of a donut-shaped sheet DE sensor (using SWCNT electrode).*

**Figure 5.**

*Overview of electrostatic capacity measuring device for donut-shaped DE sensor.*

#### **Figure 6.**

*Donut-shaped DE sensor installed so that the central part rises about 5 mm.*

#### **Figure 7.** *Shapes of resin blocks used.*

donut-shaped DE sensor was measured using LCR METER (ZM2372) manufactured by NF corporation.

As described above, in the donut-shaped DE sensor, a conical resin block made of an acrylic gel sheet was installed in the central part, and the central part was installed so as to be raised by about 5 mm. **Figure 6** shows a donut-shaped DE sensor with a resin block installed in order to rise the central part by about 5 mm. The shape of the resin block is shown in **Figure 7**. The height of the resin block is 6 mm, but this block is quite soft, and when a donut-shaped DE sensor is placed on it, it sinks 1 mm and becomes 5 mm. There are two reasons for inserting this resin block:


For the acrylic and its strain-removed film, the block shown in **Figure 6a** was used, but since silicon and HNBR are hard, it was necessary to enlarge the block as shown in **Figure 6b**. The reason is that with a hard film, the block sinks more than necessary, making it difficult to measure the electrostatic capacity. So, it is necessary to increase the area that receives pressure (see Eq. (1) above).

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

The response speed of the DE sensor was verified by measuring the time from the time when the load was applied to the DE sensor until the actual capacitance of the DE sensor changed. The presence or absence of a load on the DE sensor was determined by monitoring the amount of deformation of the DE sensor using an ultrahighprecision laser displacement meter (controller: LC-2400, laser sensor: LC-2440) manufactured by Keyence Co., Ltd.

#### **3.4 DE sheet-type sensors without cushioning materials**

In addition to the above experiments, experiments without cushioning material in acrylic sheet sensors, HNBR, and silicon sheet sensors using SWCNT electrodes were executed. The loads were increased from 10 kg to 120 kg at 10 kg intervals, and the capacitance at each load was measured. The electrodes of sensors used were the SWCNT spray described above, and the shape was a circle with a diameter of 20 mm. **Figure 8** shows a sheet DE sensor as the experimental prototype.

**Figure 9** shows an outline of the experimental equipment. By sandwiching each prototype sensor in a precision vise and tightening the precision vise, a load is applied to the experimental sensor to deform it. The experimental sensor was attached to a precision vise by sandwiching it between acrylic blocks for insulation. For the load measurement, a load cell capable of measuring up to 120 kg was used. The analog signal from the load cell was taken into the CPU via a dedicated AD conversion IC (HX711). After that, after performing the calibration process with the CPU, the measured value was displayed on the LDC. The measurement accuracy was confirmed at three points of 10 kg, 30 kg, and 50 kg using a weight whose mass was measured in advance, and it was confirmed that the error was 5% or less at each point. The electrostatic capacity that changes due to the deformation of the experimental sensor was measured using LCR METER (ZM2372) manufactured by NF corporation.

#### **Figure 8.**

*An experimental sensor with a diameter of 20 mm.*

**Figure 9.** *Outline of the experimental device for sheet-type sensors.*

Next, using a DE sheet sensor with a SWCNT electrode attached to a US-made acrylic distortion-corrected film, the pressure was applied up to 120 kgf by sandwiching it between flanges, and its behavior was observed. The meaning of this experiment is that when bolting the flange, it can be confirmed by the pressure sensor whether or not the bolts are tightened evenly. **Figure 10** shows the installation of DE sensors on the flange. **Figure 11** shows sensor bases with four load cells. **Figure 12** shows a pressure change measurement system by tightening bolts using DE sensors.

Acrylic DE sensors (strain removal type) using SWCNT electrodes were installed at four locations on the flange. Capacitance was measured while tightening only the lower right bolt (No. 1) (see **Figure 12**) by 1/2 or 1 turn compared with the others. A calibration was performed in advance by comparing with the value of the DE sensor using a load cell with a maximum measured value of 120 kg (see **Figure 13**). The flange was fixed with four M6 (φ6 mm) bolts attached at 90° intervals. The DE seat sensor was attached to the top of the aluminum plate so that it was located right next to the flange fixing bolt. A glass epoxy plate having a thickness of 0.3 mm is installed on the surface of the aluminum plate for the purpose of electrically insulating the DE sensor and the aluminum block (see **Figure 12**).

**Figure 10.**

*Installation of DE sensors on the flange and flange fixed with M6 bolts.*

**Figure 11.** *Sensor bases with four load cells.*

**Figure 12.** *Pressure change measurement system by tightening bolts using DE sensor.*

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

**Figure 13.**

*Examples of weighing scales using various load cells capable of measuring up to 120 kg.*

**Figure 15.**

*Sample photograph of a stretch sensor (using carbon grease electrode): Measure the change in conductivity while increasing the stretch by 0.5 cm in the right direction of this sample.*

As a circuit to take the data from the DE sensor into the PC, four sets of circuits in which the DE sensor was connected to the CV conversion and the voltage adjustment circuit were prepared. The output from each amplifier was AD-converted and taken into the PC.

**Figure 13** shows examples of weighing scales using various load cells capable of measuring up to 120 kg. The load cell alone cannot be used, and a mounting base, weighted part, etc., are required. For example, the weight of a load cell that can weigh up to 120 kg is 200 g, but when the mounting base and weighted parts are added, it becomes about 600 g (see **Figure 14**). In addition to the above, the load cell for measuring several kg has a size of 80 x 12.7 x 12.7 mm and weighs about 30 g. Moreover, in order to use the load cell, a structure that supports the fulcrum with strength that does not deform even with the maximum load is required, which makes it even larger and heavier.

#### **3.5 Manufacturing method of stretch sensor**

The elastomer, which had the best performance with the pressure sensor, was cut into a rectangle, molded into a width of 3 cm and a length of 5 cm using 100% prestrain, and an electrode with a width of 2 cm and a length of 3 cm was applied to the center (see **Figure 15**). In order to confirm the difference in performance due to the difference in electrodes, general carbon grease, carbon black, and SWCNT were used as electrodes**.** For the method of making carbon grease, carbon black, and SWCNT electrodes and the methods of checking those thicknesses, refer to 3.2: How to make a donut-shaped sheet DE sensor.

#### **3.6 Performance comparison by stretch sensor elongation**

The change in conductivity was measured while increasing the elongation by 0.5 cm for the sensor using carbon grease, the sensor using carbon black, and the sensor using SWCNT (see **Figure 15**).

## **4. Results**

The above various experiments were performed, and the following results were obtained.

#### **4.1 Pressure measurement result by donut-type sheet DE sensor**

The pressure measurement results of the donut-shaped sheet DE sensors prepared using three types of electrodes and four types of elastomer materials are shown in **Figure 16** (carbon grease electrodes), **Figure 17** (carbon black electrodes), **Figure 18** (SWCNT electrodes), and those are summarized in **Table 1**. As shown in **Table 1a**, the silicon material (ELASTOSIL FILM 2030250) has a larger maximum measurable pressure than others. The acrylics have a smaller minimum measurable pressure value than silicon and HNBR. The acrylic with removed distortion and the acrylic with remaining distortion, the measuring minimum pressure value of the removing distortion was about 2 g smaller.

The difference in electrodes did not significantly affect the measurement range. Furthermore, as in the above, there was not much difference in the detection speed due to the difference in the electrodes. Silicon is relatively faster than other membranes (see **Table 1d**). The DE sensor using the SWCNT electrode gave slightly better results than the others. The DE sensor using the SWCNT electrode gave slightly better results than the others. The circuit used for the experiment was fixed to the circuit shown in **Figure 5** for the experiment.

**Figure 16.** *Changes in electrostatic capacities due to load when using carbon grease electrodes.*

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

**Figure 17.** *Changes in electrostatic capacities due to load when using carbon black electrodes.*

#### **Figure 18.**

*Changes in electrostatic capacities due to load when using SWCNT electrodes.*

**Figure 19** shows the results of SS curves and the results of viscoelasticity tests (storage and loss modulus) of four different elastomers. **Table 2** shows the hardness of the elastomer films and the hardness of the resin blocks used in the experiment.

#### *4.1.1 Experiment conducted without resin cushion material*

**Figure 20** shows the results of measuring the capacitance by increasing the load from 10 kg to 120 kg at 10 kg intervals. Furthermore, in **Figure 20**, "Change in electrostatic capacity due to load" shows the measured values from 10 kgf to 120 kgf, but this is a value limited by the measurement range of the 120 kg load cell used for the measurement. It has been confirmed that the measurement range of the sheet-type DE sensor used in this experiment can be measured from about 4 kgf (using acrylic


#### **Table 1.**

*Pressure measurement results of donut-type sheet DE sensor.*

film with corrected distortion) by changing the load cell used for measurement. However, in order to unify the experimental conditions, only the values measured with a 120 kg load cell are shown in **Figure 20**.

It can be seen that the capacitance of acrylic changes almost linearly from 10 kg to the measurement limit of 120 kg. On the other hand, silicon changed linearly from 30 kg, but the amount of change decreased from 100 kg. In the case of silicon, the change in capacitance stabilizes in about 10 seconds, but in the case of acrylic, it took about 20–25 seconds. Since the silicon used in this experiment is harder than acrylic, the region that changes linearly is narrow, but the response speed is considered to be fast. HNBR had intermediate values between silicon and acrylic.

#### *4.1.2 Demonstration experiment to see if bolts are tightened evenly*

As explained above, capacitance was measured while tightening only the lower right bolt (No. 1) (see **Figure 12**) by 1/2 or 1 turn compared with the others. As a

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

#### **Figure 19.**

*The results of SS curves and the results of viscoelasticity tests (storage and loss modulus) of 4 different elastomers. (a) Tensile test results for 4 types of elastomers. (b) 4 types of Dynamic viscoelasticity test results (upper figure: Storage, lower figure: Loss modulus).*

result, it was confirmed that the pressure near the bolt at the lower right was higher than the others. **Figure 21** is a graph showing the change in capacitance when bolts are tightened. When only the lower right screw (CH1) was rotated 1/2, the capacitance


*Note: This time, the experiment was conducted using only the acrylic gel sheet. Next time, we plan to perform comparative verification using a softer urethane gel sheet.*

**Table 2.**

*The hardness of the elastomer films and the hardness of the resin blocks used in the experiment.*

**Figure 20.** *Change in capacitance due to load.*

was about 22.42 pF. This is equivalent to 8.46 kg when converted to load. In addition, the capacitance when only the lower right screw is rotated once is about 24.15 pF, which is equivalent to 30.89 kg.

## **4.2 Stretch sensor elongation comparison due to differences in the performance of different types of electrodes**

**Figure 22** shows the change in conductivity when a DE sensor with carbon grease, carbon black, and SWCNT electrodes attached to the acrylic that has been subjected to *Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

#### **Figure 21.**

*Change in capacitance due to measured load: \* the load was calculated from the capacitance based on Figure 13. \* CH1: Lower right, CH2: Upper right, CH3: Upper left, CH4: Lower left.*

#### **Figure 22.**

*The change in conductivity when a DE sensor with carbon grease, carbon black, and SWCNT electrodes attached to the acrylic that has been subjected to strain removal treatment is stretched by 5 mm.*

strain removal treatment is stretched by 5 mm. As mentioned in above, the thickness of the carbon grease electrode was 100 μm.

The thickness of the carbon black was 70 μm and that of the SWCNT was 50 μm.

## **5. Discussion**

Section 5.1 discusses the results of the experiment in this time, and Section 5.2 discusses the possibility of applying DE sensors to robotics and human-robot interaction (HRI).

#### **5.1 Discussion of experimental results**

First, the values obtained with all the data in **Table 1a**, **b** and **c** and the sensors with the four SWCNT electrodes used in the flanges were compared with the values obtained with the load cell in each case. The values of both types of sensors were almost the same. With the donut-shaped DE sensor, there was no significant difference in the measured pressure range due to the difference in the electrodes. The case where SWCNT was used for the electrode had the widest measurement range. The reason for this will be described in the stretch sensor section below. When the SWCNT electrode was used for the acrylic from which the strain had been removed, a change in capacitance appeared from the time the load reached 5 g, and a change in capacitance was confirmed up to 10,100 g. With this electrode, however, it is considered that the change in capacitance was small because the deformation of the resin block reached the limit at 10,000 g or more. With this electrode, it is considered that the change in capacitance was small because the deformation of the resin block reached the limit at 10,000 g or more. Moreover, it changes almost linearly between 500 g and 10,000 g. In the case of the sensors used the other electrodes, however, the boundary that changes linearly is narrower than that. In this sensor, the capacitance changes by about 5% by changing the test frequency from 120 Hz to 1 kHz. The reason is considered to be the influence of the frequency characteristics of carbon grease. When this electrode is used, the frequency characteristic of AC is worse than expected, and the capacitance changes more than the deformation of the elastomer.

We also conducted a demonstration experiment to see if it was possible to detect abnormal bolt tightening on the flange. After tightening the bolt in the lower right of the flange more than the other three places, we measured the pressures in four places (including where the bolt was tightened more) with the load cells and DE sensors using SWCNT electrodes, and each was compared. As a result, it was confirmed that the pressure of the tightened bolt was higher than the others. In the experiment above, the DE sensor was placed on the left side of the bolt, but for confirmation, the DE sensor was placed again on the right side of the bolt, the pressure was checked, and it was confirmed that the values were the same. When actually using these sensors, it is better to make a hole in the DE sensor and put it though in the bolt. It seems that this can be applied to various joints in the future, for example, by tightening pipes and tires that allow dangerous substances to flow, to support safer operation. The reason why the capacitance was measured by tightening the lower right bolt (No. 1) with two steps (1/2 or one turn) is that if those were suddenly tightened with a large force, they might be damaged. Therefore, two-stage measurement was employed.

In the case of the DE sensor without cushion, in the preliminary study, the film thickness of the silicon was as thin as 0.5 mm, and it was quite hard, so the range of change was estimated to be narrow. However, even with silicon, it was confirmed that the load changed from 10 kg to 100 kg linearly from 30 kg to 100 kg. The sheet-type sensor using acrylic shows a change in capacitance from a load of several kg, and changes almost linearly from 10 kg to the measurement limit of 120 kg. HNBR showed an intermediate value between them. In any case, with a load of 10 kg or less, linear displacement did not occur, and with acrylic, the limit was about 4 kg. The silicon weighed 9 kg and the HNBR weighed around 7.5 kg. When measuring a load lower than this value, it seems possible to measure it sufficiently by adding a cushion material (this discussion will be described in detail below).

When measuring cases with large pressures, the above three types of DE sensors could measure in a relatively wide range and could be effective in various applications. For example, as mentioned above, these sensors have a thin elastomer of 0.5 mm or less and seem to be effective as a sensor for checking the tightened condition of the screws in the flange part, which is often used for pipelines and plant piping. It may also be used as a sensor to detect failures in the flange due to aging or vibration. The

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

silicon might be better than the acrylic or HNBR for applications that require faster measurement speeds.

Next, looking at the four elastomer materials individually, from the SS curve measurement results (**Figure 19a**) and (**Table 1a** and **b**), the silicon material (ELASTOSIL FILM 2030250) is harder than other films. So, the film seems to have a relatively large measurable pressure change range. That is, there is a drag against pressure, and the film cannot be easily crushed by pressure. The measurement results of dynamic viscoelasticity support this (**Figure 19b**) [26]. In addition, the hardness of the four types of elastomers above was measured using Askar C, and the results were in the order of silicon, HNBR, acrylic with strain, and acrylic with strain removed. Thus, evidences support that the explanation above is correct (See **Table 2**).

As acrylic deforms greatly, it can be detected from the smallest value and can be measured from 5 g. In other words, acrylic is so soft that it can quickly detect even the slightest pressure.

That is, as described in eq. (1), when the elastomer is crushed by pressure or some forces and the electrodes are closer to each other, a larger capacitance can be obtained. On the other hand, with the hardest silicon, no change was observed at about several g, and a force of about 30 g was required.

The difference in electrodes did not significantly affect the measurement range, but in terms of measurement speed, silicon was slightly faster than other films. The reason is that the silicon film is harder than others, so even if it is pushed by pressure, it returns quickly to its original state [26]. The speed of acrylic is a little slower than that of silicon, but it seems to be sufficient for practical use. As shown in **Table 1c**, SWCNTs with good conductivity showed slightly better results. The reason for this is the same as above, and the better the conductivity, the larger the Coulomb force that can be generated, and as a result, the film is distorted quickly, and the reaction is quick to return. There was little difference in measurement speed depending on the electrodes, but it seems that even a small difference in speed could be a key factor for sensors used in smaller devices such as mm size.

By using a very soft resin cushioning material installed in the center of the donutshaped sheet DE sensor used, it was possible to measure even with a slight pressure, which led to the success of this experiment. Considering the hardness and elongation of the abovementioned elastomer, it can be inferred that the measurement range can be changed by changing the material of the cushioning material installed on the elastomer film [29]. As mentioned in the Background of dielectric elastomers (DEs) above, if the shape is semicircular, it could come into contact with the object to be measured faster. In such a form, as the pressure or pushing force increases, the ground plane of the elastomer becomes larger by that amount, and it is considered that a sufficient effect in terms of capacitance is produced. In this experiment, a small cushion was sufficient without having to make such a semicircular sensor shape. In this experiment, we did not conduct an experiment to change the shape of the cushion material. If the shape of the cushion material is such that the convex lens is viewed from the side, it is considered that the pressure range width can be increased while maintaining the small shape.

Furthermore, we will conduct the experiment again using the ultra-soft modeling resin: H5-100/H5-600J] manufactured by EXCEL Co., Ltd., as the new cushioning material. This material is softer than the resin used this time, so it seems that it would be possible to detect even with a smaller pressure (see **Table 2b**). The minimum measurement depends on the hardness of the elastomer used in the donut-shaped sheet DE sensor, so it is necessary to use a cushion to stretch the elastomer as

described above. However, in this respect, acrylic, which has a small minimum measurement value, could be an effective material.

As an interesting result, the pressure measurement curve shape from the sheet type and the donut-shaped DE sensor are opposite of each other. The reason is that in the case of a donut-shaped DE sensor, the elastomer is stretched by the resin block, and then an external force is applied, the resin block shrinks, the elongation of the elastomer is attenuated, and the capacitance is reduced (See **Figures 16**–**18**). In the sheet type, an external force is directly applied to the sheet, and the entire sheet is stretched, whereby the amount of electrostatic charge increases (see **Figure 19a**). The reason for conducting the donut-type and sheet-type experiments was that we wanted to prove that the donut type could handle the pressure of the gram order to the 10 kg order, and the sheet type could handle the pressure of the 10 kg order to the 100 kg order. As shown in the results of this experiment, this is because that it might be able to serve as a guideline for proper development within industries.

As shown in **Figure 19a**, one more interesting finding is that when comparing the SS curves of a film from which the strain of the film has been removed with 3 M acrylic and the SS curve of the film without removing the strain, the curves are almost the same to some extent, and the breaking stress is also almost the same. However, it turned out that the final elongation was different. This is thought to have caused a difference in the pressure measurement range and measurement speed in this experiment. It is presumed that the film was more uniform and therefore more easily stretched (see **Figure 19a**). In addition, since 100% pre-stretching was applied, the hardness of the acrylic became moderately hard, and dynamic viscoelasticity was also moderately present (see **Figure 19b**).

The various DE sensors in this experiment can measure any pressure changes from deformation instantaneously within the measurement range. It might depend more on the circuit design. The circuit used this experiment was the same to the circuit mentioned in Section 3.3. If a circuit having a higher degree of amplification is used for this circuit, even a smaller change can be measured, so that the measurement range can be expanded. As mentioned above, the selection of the elastomer material and cushioning material used for the sensor is important for improving the measurement speed, but the tune-up of the circuit is also important for achieving a faster measurement speed.

In 2011, the authors of this paper, in collaboration with the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), examined a DEA that can be driven even on a 100,00 m deep seabed [30, 31]. Using a pressure chamber that could reproduce the pressure of the seabed of 10,000 m, a roll-type DEA was manufactured by rolling a sheet-type DE using an acrylic material, and when an experiment was conducted, it was able to be sufficiently driven even under a pressure of 10,000 m. We believe that the DE sensor can be sufficiently driven as a sensor even under a pressure of 10,000 m if an elastomer with sufficient thickness is used and a cushion material with an appropriate thickness is selected.

Next, regarding the stretch sensor, the silicon material is harder than other films, and as a sensor, the operation speed is fast. However, since the elongation width is small, it seems that the intended use could be limited. Materials with low dynamic viscoelasticity, such as silicone, might not be well suited for DEA/sensor materials [26]. In comparison, acrylic is softer and more stretchable than other films. It could be suitable for DEA/sensor materials. The reaction speed is also sufficient for practical use, and it seems to be optimal as a sensor that matches human movements. In addition, the pressure change range that can be measured is large.

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

**Figure 23.** *Rubber glove with DE sheet sensor.*

Acrylic's upward-sloping dynamic viscoelasticity supports this evidence (see **Figure 19b**).

The difference in conductivity between carbon grease, carbon black, and SWCNT electrodes is clear, and SWCNT is the most suitable for stretch sensors (see **Figure 22**). The SWCNTs are more conductive, but not only that, SWCNTs are packed in a spray and sprayed to achieve a uniform, thinner electrode [25]. If you look at a commercial basis, however, the cost of carbon black is much cheaper than SWCNTs and multi-walled CNTs (MWCNTs). So, this choice should not be disregarded.

In 2012, we created a system to check how much a finger bends by attaching a DE sensor with a carbon black electrode on a non-distorted acrylic film to a rubber glove. (See **Figure 23**) [31–33].

However, due to the carbon black, the stretch sensor that follows the movement of the finger did not stretch sufficiently. So, we would like to verify it again with a system that reflects these data.

### **5.2 Application to robotics and human robot interaction (HRI)**

The best use of the DE pressure sensor is to apply it to finger pressure sensors in robots or magic hands. This type of sensor is thought to be able to measure pressure ranging from 1 gram to about 150 kg. Additionally, it is extremely flexible, thin, and small, so dexterity rivaling that of human fingers could be realized. When working with humans, it goes without saying that a robot with humans-like abilities would be easier to work with. S. Chiba and M. Waki used a DEA to create a model in which a robot finger is driven by tendons similar to those found in a human finger. Next time, we plan to incorporate DE sensors into this model to create a finger that is more like a human finger and has senses [34].

If this stretch sensor is used well, it can be used as a three-dimensional position sensor for the arms and legs of robots and the like. The principle of 3D position sensor will be explained step by step below.

A stretch sensor is attached to the upper and lower sides of the robot's arm to move the arm upward. The upper sensor is extended and the lower sensor is the contracted. As a result, it is possible to two-dimensionally determine at what angle the arm is bent. Furthermore, by deploying another pair of sensors on the side of the arm, it is possible to sense diagonal movement. That is, the three-dimensional position can be easily determined from the difference between the upper and lower capacitance and the difference in the capacitance on the side surfaces. For this, a calculation table might be helpful. In the next experiment, we plan to attach such a sensor to the robot. As it is now possible to lift an 8 kgf weight by 1 mm or more at 88mms with 0.15 g (0.96 g

including reinforcement material) of acrylic [25, 35, 36], we will use it for this system. If you created a finger model that incorporates DEA and DE sensors, it seems that you could make a human-like finger. In addition, it seems that the arm with three pairs of the above sensors could detect the speed of movement of the arm. The expansion and contraction speed of DEA moving in the three-dimensional direction can be calculated from the change in capacitance and the position of the arm (based on the information from the position sensor). Alternatively, each value could be imported as a variable into such a calculated table. Environmental resistance is also an important factor for the actual use of robots. Regarding the environmental resistance of DE sensors, silicon with SWCNT electrodes and HNBR sensors can be used at temperatures 150–200°C [37]. At low temperatures, silicon is said to be usable down to around �100°C [38]. If it could be used in this temperature range, it might be expected to be used as a robot sensor for space environments, deep seas, and extremely cold regions.

## **6. Conclusion**

The difference in the type of elastomers, the thicknesses and shapes of the cushioning materials placed under the elastomer films, the difference in the presence or absence of distortion of the films, the effect of the difference in the type of electrodes were examined, and the following conclusions are obtained:


## **Acknowledgements**

We would like to thank Mr. M. Uejima, Mr. H. Uchida, and Mr. M. Takeshita of ZEON Corporation for providing SWCNT (ZEONANO® -SG101) and HNBR free of charge for carrying out our experiment.

*Perspective Chapter: Dielectric Elastomer Sensor Capable of Measuring Large Deformation… DOI: http://dx.doi.org/10.5772/intechopen.108622*

## **Author details**

Seiki Chiba1 \* and Mikio Waki<sup>2</sup>

1 Chiba Science Institute, Tokyo, Japan

2 Wits Inc., Sakura, Tochigi, Japan

\*Address all correspondence to: epam@hyperdrive-web.com

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 7**

## Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm

*Falih Salih Mahdi Alkhafaji*

## **Abstract**

This chapter focuses on the design of a handling 5 Degree of freedom (DOF) robot arm model for industrial application. Optimal trajectory planning of industrial robots in the assembly line is a key topic to boost productivity in a variety of manufacturing activities. The aim is to improve the speed performance using multi techniques starting from estimating the transfer function of each manipulated joint, then designing the controller for each DOF reached to modeling arm motion. The designed model has been developed the structural design and testing motion characteristics by using SolidWorks and Simscape toolbox. To enhance the speed performance, it is proposed a High-Speed Proportional Integral Derivative controller (HSPID) based on an improved GA. The comparison response time between uncontrolled and controlled systems proves that the proposed controller produced extreme reduction responses to be measured within the Microsecond unit. Based on trajectory motion, the efficiency of the proposed method is assured by case study motions. The innovative design offers the best solution to rise accurate performance and productivity.

**Keywords:** industrial robot arm, TF, Simscape, trajectory planning, HSPID, response time

## **1. Introduction**

For decades, polymorphic robots are one of the most widely used mechatronic systems in the industry, which has generated the need to constantly create industrial robots with more power, speed, and precision for a wide variety of tasks to improve automation systems precisely offering lower-cost production [1–4]. The manufacturing sector is moving towards industry 4.0 and demands high-end automation in the process [5]. Due to their great usefulness in the industry, industrial robot arms are widely used to move material, parts, and tools as well as the welding and painting of parts [6], offers a powerful performance in terms speed, repetitive and seemingly intelligent decisions [7–10]. Structurally, a robot arm is constructed from several parts such as manipulator links, actuators, controller, and sensors where the controller acts as the brain to manipulate the mechanical parts [11–13]. A robot arm manipulator is constructed from links and joints to control the robot's trajectory. The links of such a manipulator are connected by joints allowing either rotational motion such as in an articulated robot or translational (linear) displacement. Usually, the end effector is attached to the end joint of the structure [14–17]. Precise control at each DOF of a

robotic arm is considered a competitive key to improving performance through the implementation of industrial robotic arms [18, 19]. The most remarkable controller's strategies are impedance control, force control, motion control, and hybrid motionforce control [20]. On the one side, optimizing the estimation TF model is the most important criterion to enhance controller's design [21]. On the other side, the basis for developing a control system for a robot manipulator is a feedback loop. Therefore, it is necessary to define input signals such as torque and drive input voltage to achieve the desired operation [22, 23]. For several decades, the Proportional Integral Derivative (PID) algorithm is one of the most widely used in the industry for controlling closedloop feedback systems, because of the less complexity with the ability to meet desired controller's functions for wide scope plant models [24–26]. It stands for three proportional gains: proportional (Kp), integral (Ki), and derivative (Kd), where they should be jointly tuned to get better performance [27, 28]. Despite having only three parameters, the classical PID controller has been unable to meet the sophisticated requirements [29–31]. The problem with PID has been identified as poor tuning, which means that most of the controllers currently in operation have been poorly tuned. This results in a biased judgment against the PID controllers themselves [32]. The controller tuning greatly affects the control system properties, such as robustness to disturbances and noise [33].

Over 50 years, massive tuning strategies have been suggested to realize satisfied response time characteristics in terms of peak overshoot (Pos), rise time(tr), and settling time (ts). Optimizing PID controller performance can be achieved by considering systematic proportional gain adjustments, otherwise, the tuning will be inadequate and the process testing will take longer [34]. Tuning and optimizing PID gains improves the convergence speed and the global optimization by reduces the overshooting and transition time of the plant system [35]. In fact, an evolutionary algorithm (EA) presents the best solution to optimize PID gains by adapting to the system's nonlinearity [36]. GAs are the first-class category of EA that is commonly used to generate high-quality solutions to optimize PID parameters, by relying on bioinspired operators such as mutation, crossover, and selection [37, 38]. Usually, GA is applied to optimize a function called fitness function (FF). The FF assists GA algorithms in measuring the quality of the solution of the problem under study and how effective the solution of the problem [39]. In designing the controller, it is related to performance indices such as settling time, integral error, and so on, and might be addressed as a multiobjective function to improve the controller's response [40].

GAs are satisfied in solving multi-objective optimization problems. A possible solution to a problem is considered individual. A group of individuals is called a population. The current population produces a new generation, eventually ceases when it reaches an individual who represents the optimal solution to the problem [41, 42]. A conventional GA requires two variables to be determined, a FF composed of a performance index (*P indices*) and a genetic representation of the solution domain. To formulate the performance index for PID controllers, several related researchers use the following equations to formulate cost functions such as:

integral squared error(ISE); integral absolute error (IAE); integral time squared error (ITSE); integral time absolute error (ITAE); and mean squared error(MSE) as illustrated from Eq. (1) to (5) respectively. Besides that, as it is needed to reduce the error, the FF equation is taken as an inverse of the performance indices as Eq. (6).

$$ISE = \int\_0^T |e(t)|^2 \, d(t) \tag{1}$$

*Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm DOI: http://dx.doi.org/10.5772/intechopen.108755*

$$IAE = \int\_0^T |e(t)| \, d(t) \tag{2}$$

$$ITSE = \int\_{0}^{T} t|e(t)|^{2} \, d(t) \tag{3}$$

$$ITAE = \int\_{0}^{T} t|e(t)| \, d(t) \tag{4}$$

$$MSE = \frac{1}{t} \int\_{0}^{T} (e(t))^2 \, d(t) \tag{5}$$

$$FF = \frac{1}{P \text{ indices}}\tag{6}$$

Basically, GA problems rely on three operators: selection, mutation, and crossover [43–48]. In fact, traditional GA generates random population that might produce poor fitness and low-quality of individuals, leading to consume more time to converge through optimized solutions. Therefore, the quality of an initial population of individuals reflected considerably on the GA's performance to produce an optimal solution. Most previous techniques concentrate on the quality of the initial population seeding, such as random initialization, nearest neighbor, and K-means clustering [49–51]. Some researchers used GA to optimize PID, for instance, Guan and Jau [52], Swati K. et al. [53], Tanvir ert et al. [54], Gun B. S. [55], and Apriaskar et al. [56]. On the other side, PSO is one of the EA's that was developed by James Kennedy and Russell Eberhart in 1995, for solving practical issues related to optimization, inspired by the behavior of living things. It has several benefits such as being simple implementation, featuring a simple concept, efficacious computation, and more costeffective, flexible, and balanced mechanism to improve a global and local exploration abilities [57–60]. Recently, several studies using PSO to optimize PID controllers such as M. I. M. Zakki et al. [61], V. Bagyaveereswaran et al. [62], Y. Xie and J. Meng [63], B. A. Arain et al. [64], S. Howimanporn et al. [65].

Regarding previous works for optimizing PID controllers, the PSO is faster than GA when looking closely at idealistic solutions in case of does not require a detailed mathematical description of the process for formulating the Objective Function (OF) to optimize proportional gains, where the drawbacks of PSO are the lack of certainty that an optimal solution will be found and even the high computational costs associated with FF [66–68]. Consequently, standard PSOs often fail to solve these complex problems because they easily get stuck in local optima and converge slowly [69]. One of the main differences between PSO and GA is the mechanism of perturbing the solution from the old population to create a new population. These different mechanisms generate a population of solutions with different balances of enrichment and diversification. For GA, the solutions are arranged based on their fitness values [70]. Based on survey, classical GA is not the best solution with respect to PSO. A serious downside of GA comes from the way new generations are computed after the first. It contains a random component that causes generated values to be corrupted during the early stages of global search. Where proposed methods for initial seeding of populations are limited. The limited number of this approach motivates this study because there is room for improvement and finding a better starting population. To improve GA's performance, it was proposed to apply a new technique to GA to raise precise searching constraints by introducing a new Modified Initialization Fitness

Function (MIFF) technique. The proposed was applied to optimize the PID controller for each manipulated joint to enhance velocity performance. Model-Based Design Approach (MBDA) is an advanced simulation technique that is widely used to improve system design, providing explicit models to define activities in the product design and development lifecycle [71]. Additionally, rapid technology using computer-aided design (CAD) enables free-form fabrication of parts with complex geometries directly from CAD models on CNC machines without the need for special fixtures as in the material removal process, provides the best tools for developing products, faster, lower cost and more Competitive Global Market [72, 73]. The collaboration between MathWorks and SolidWorks is one of the best solutions for designing and simulating robot motion, optimizing system parameters, analyzing results in the Simulink environment, analyzing forces due to torque on mechanical joints, provides a nice tool for plotting acceleration due to displacement of arbitrary parts, visualize the motion of CAD assemblies and simplify the physics of mechanical systems without the need to derive equations of motion [74]. The concept and design of robotic arms is not a new concept but still, much work and development are required to enable robots to perform complex tasks. The challenge for robotic technology is to make it compatible with human tasks and hand movements, such as grabbing, swapping, and completing critical tasks. In this way, when we were able to precisely control the movement of the robot, we succeeded in developing the robot arm [75, 76]. In reality, the precise control of each degree of freedom of a robotic arm is a great challenge in implementing industrial work [77]. The robot simulation is used to know the robot torque that will improve and optimize the movement of the arm robot so the robot can help the industry to produce effectively and efficiently [78]. Nowadays, the modeling and control of mechatronic and robotic systems is an open and challenging field of investigation in both industry and academia. The mathematical model of a mechanical system is indeed fundamental for the development of experimental prototypes [79]. On the other side, optimal trajectory planning of industrial robots in the assembly line is a key topic to boost productivity in a variety of manufacturing tasks [80]. The main focus of this work is the design of a 5-DOF industrial robotic arm that further enhanced speed performance and optimized trajectory planning by modeling an HSPID controller based on improved GA (IGA). The GA is implemented based on MIFF as demonstrated in [81]. This provides an opportunity to maximize significantly response time.

The goal is to achieve high-speed performance in designs composed through motion components, which can be concluded as follows: 1) easily create geometric robot parts to assemble; 2) modeling and simulation of the robot arm; 3) Minimize the response time characteristics in terms of tr, ts, and PoS. This can increase the speed of moving the arm from the initial state point to the final state point and significantly reduce the cycle time. SolidWorks was used to design the mechanical structure of the robotic arm. Then perform plugin-based model integration to design controller models within Simscape compatible models, leads to export (XML) files including the part's structure of each component for the base, shoulder, upper and lower arm, and wrist end effector. All the parts are assembled and a HSPID controller is implemented in each DOF of the robot using the Simscape blockset to construct the whole system. Finally, each manipulated joint was examined for in terms of tr, ts, and PoS. This chapter is organized as follows: Section 2 presents the design methodology, including modeling the Simscape configuration, estimating TFs of each manipulated joint, designing the HSPID controller, and a motion detection strategy. Section 3 presents the simulation results approach for estimating TFs, the response time of the designed

model with and without a controller, and the simulation results approach for estimating motion based on three different trajectory signals. Section 4 discusses of the results. Section 5 summarizes the results and provides recommendations for future research.

## **2. Procedure design methodology**

This section describes process design techniques including modeling, simulation, and HSPID controllers. Basically, a robotic arm consists of 5 limbs and 5 joints. To verify system-level design and simulation models, we need to define the structure of the system and its behavior. In this work, models related to Solidworks and Simscape were semantically defined using plugin-based model integration to obtain compatible structural and simulatable models leads to export of the models in XML file. The organizational charting procedure design methodology shown in **Figure 1** includes various blocks as shown in the following diagram.


**Figure 1.** *Scheme of design procedure.*

## **2.1 3D CAD component design and assembly**

It is possible to simulate the mechanical behavior of the robot components using axis material during the motion study of the robot, by using SolidWorks in conjunction with Matlab to design high-quality control systems, providing the ability to transform all the component's parameters into XML files, to be imported a dynamic parameter of the physical structure into MATLAB environment, including the inertia matrix calculations for each manipulated joint [82], offers an amazing solution that could be allowed to perform the drawing of a mechanical model to be applied in as a real geometric and mass measurements. In this study, the components (rigid bodies) are built individually and assembled links serially. **Figure 2** presents all the designed rigid bodies of the robotic arm labeled from base to tip: base, shoulder, lower arm, upper arm, connector, Wrist, and end effector. First, we need to attach the base to the robot frame, connect the shoulders to the forearms, connect the upper arms to the forearms via connectors, and connect the wrists to two rigid upper arm bodies. Also, the end effector must be attached to the object for manipulation. In addition to determining the size parameters of the system such as body, height, and weight, the rotation of the rotary joints should be installed. A motion analysis is then presented to verify the effectiveness of the model configuration in terms of velocity and distance. Consequently, different parameters with different settings will lead to various results. **Figure 3** shows the whole system constructed based on 5 joints and 5 links.

#### **Figure 2.**

*Three-dimensional solid rigid parts, (a) lower arm, (b) upper arm, (c) connector, (d) shoulder, (e) wrist, (f) end effector, (g) base.*

## **2.2 Modeling Simscape**

The Simscape Multibody is used to perform the simulation of links, joints, and dynamic analysis motion. The 3D solid model contains the robot body and a manipulated joint is being used for assembling the whole system. Further, employ SimMechanics tools to produce the XML file for each rigid body. The floor blockset was used as a world frame reference for rigid transformation to set the gravitational force direction. To investigate the motion parameters and response time characteristics, it was used a scope simulator for this purpose. **Figure 4** illustrates the Simscape model of the whole system, representing the joint revolutions and rigid parts as follows; base, shoulder, lower arm, connector, upper arm, wrist, and the end effector.

## **2.3 Estimate TF of the manipulator's joint**

To design a motion controller, we need to estimate the TF for each manipulator joint. This is considered a significant problem in most previous studies, where poor estimation accuracy leads to poor controller design [83]. In this subsection, we present the procedure of estimation TF form utilizing the linear analysis tool for each manipulator's joint, which represented a nonlinearity plant system. Nine steps were performed in the estimation as follows:


**Figure 3.** *Designed robot model.*

**Figure 4.** *Simscape model of the whole system.*


## **2.4 Proposed IGA**

The most important feature of the GA is how it transforms the system output into a fitness value to depress the errors in the reference trajectories of the plant modelbased PID controller. Therefore, the chosen IAE evolution or FF should be used to compute the total error between the reference and the system output for each set of generated PID gains. By proposing two techniques the GA could be significantly improved. Firstly, using optimization-based tune (OBT) to find the best optimal

solution as explained in [31]. Secondly, initialized FF based on the best optimal constraints to initialize the constraints of GA chromosomes as demonstrated in [73], to modify initialization FF (MIFF) based on the best optimal constraints using CHR tuning method to improve constraint's level of GA chromosomes. This method represented as a *CHROBT*, where the proportional gains which resulted by *CHROBT* are *KpCHROBT* � �, *KiCHROBT* ð Þ and *KdCHROBT* ð Þ. The mathematical expression of the cost function can be formulated as in Eq. (7).

$$\text{Cost Function} = \sum\_{n=1}^{m} \frac{|rn - \gamma n|}{m} \tag{7}$$

The initial controller gains *Kp i*ð Þ , *j* , *Ki i*ð Þ , *j* , *Kd i*ð Þ , *j* based on the results of *CHROBT* can be modified regarding to generated constraints, which are represented as the following equations:

$$K p\_{\rm MIFF}(i, j) = \varkappa \mathbf{1}(\mathbf{0}) + K p\_{\rm CIRCBT} \tag{8}$$

$$Ki\_{MIFF}(i,j) = \varkappa \mathcal{2}(\mathbf{0}) + Ki\_{CHROBT} \tag{9}$$

$$Kd\_{MIF}(i,j) = \varkappa \mathbf{3}(\mathbf{0}) + Ki\_{CHROBT} \tag{10}$$

The population in each generation is represented by a 100 x 4 population (P) matrix as expressed in Eq. (11), to produce chromosomes number in the population. Each row represents one chromosome-based MIFF that compromise *KpMIFF*ð Þ *i*, *j* , *KiMIFF*ð Þ *i*, *j* , *KidMIFF*ð Þ *i*, *j* and fitness values *FFMIFF*ð Þ *i*, *j* of the corresponding chromosomes.

$$\mathbf{P\_{-}MIFF} = \begin{vmatrix} \operatorname{Kp}(\mathbf{1},\mathbf{1})\_{\text{MIFF}} & \operatorname{Ki}(\mathbf{1},\mathbf{2})\_{\text{MIFF}} & \operatorname{Kd}(\mathbf{1},\mathbf{3})\_{\text{MIFF}} & F(\mathbf{1},\mathbf{4})\_{\text{MIFF}} \\ \operatorname{Kp}(\mathbf{2},\mathbf{1})\_{\text{MIFF}} & \operatorname{Ki}(\mathbf{2},\mathbf{2})\_{\text{MIFF}} & \operatorname{Kd}(\mathbf{2},\mathbf{3})\_{\text{MIFF}} & F(\mathbf{2},\mathbf{4})\_{\text{MIFF}} \\ & \cdots & \cdots & \cdots & \cdots \\ \cdots & \cdots & \cdots & \cdots \\ \operatorname{Kp}(\mathbf{i},\mathbf{j})\_{\text{MIFF}} & \operatorname{Ki}(\mathbf{i},\mathbf{j})\_{\text{MIFF}} & \operatorname{Kd}(\mathbf{i},\mathbf{j})\_{\text{MIFF}} & F(\mathbf{i},\mathbf{j})\_{\text{MIFF}} \end{vmatrix} \tag{11}$$

The cost function has been minimized subjected to *Kp i*ð Þ , *j* , *Ki i*ð Þ , *j* , *Kd i*ð Þ , *j* as Eq. (12), Eq. (13), Eq. (14), respectively:

$$\text{K}p \mid \min\_{\text{MIFF}} \le \text{K}p(i, j) \le \text{K}p \mid \max\_{\text{MIFF}} \tag{12}$$

$$\text{Ki } \min\_{MIFF} \le \text{Ki}(i, j) \le \text{Ki } \max\_{MIFF} \tag{13}$$

$$\text{Kd } \min\_{\text{MIFF}} \le \text{Kd}(i, j) \le \text{Kd } \max\_{\text{MIFF}} \tag{14}$$

Where *Kp i*ð Þ , *j* , *Ki I*ð Þ , *j* , and *Kd i*ð Þ , *j* are the optimized proportional gains in the *j* th area. The error (*eMIFF*) and the modified cost function-based MIFF might be formulated as in Eq. (15) and Eq. (16), respectively.

$$\varepsilon\_{\rm MIFF} = 1 - \frac{Gp(s) \* C(s)}{1 + Gp(s) \* C(s)} \tag{15}$$

$$\text{Cost function}\_{\text{MIFF}} = \sum\_{1}^{n} \frac{|\mathbf{c}\_{\text{MIFF}(n)}|}{m} \tag{16}$$

*Where*:

(n) is the order of data depending on sampling time.

(m) is the total number of data.

The cost function was written in m-code for each estimated TF, prepared to be imported into GA toolbox, to be run the GA-based MIFF (*GAMIFF*). The boundaries setting (parameters and operators) were settled as the following:

Iteration: 100; Mutation rate: 0.1; Population size: 100; Arithmetic Crossover; FF: IAE.

### **2.5 Designing HSPID controller for joint's manipulators**

The control topology relies on the injected signals and the feedback position of the end-effector for the demands of the robot application. **Figure 5** shows the Simscape model of the robot arm based on the designed HSPID controller which is constructed from five controllers that are connected with each manipulator's joint, allowing the joints to reach the required angles. It was modeled HSPID controllers regarding each revolution joint IGA to maximize responses with minimal loop interaction and sufficient. The HSPID controller was linked with each revolution joint through motion input with sample time(Ts) of 0.1 s. The proposed HSPID controller model (*C s*ð Þ*MIFF*) and the plant model (*Gp*Þ can be represented in s -domain as Eq. (17) and Eq. (18), respectively.

$$Gp(s) = \frac{\sum\_{i=0}^{m} bi \, S^i}{\sum\_{j=0}^{n} S^j + aj} = \frac{b \, \mathbf{0} \, \mathbf{S}^m + b \mathbf{1} \, \mathbf{S}^{m-1} + \dots + bm - \mathbf{1} \, \mathbf{S}^m}{a o S^n + a \mathbf{1} \mathbf{S}^{n-1} + \dots + a n - \mathbf{1} \mathbf{S}^n + a n} \tag{17}$$

$$\mathbf{C}(\mathbf{s})\_{\rm MIFF} = \mathbf{K}p\_{\rm MIFF} + \frac{\mathbf{K}i\_{\rm MIFF}}{\mathfrak{s}} + \mathbf{K}d\_{\rm MIFF} \ast \mathfrak{s} \tag{18}$$

#### **2.6 Mimic trajectory planning**

The purpose of the robot controller is to send control signals to the joints so that the robot follows a particular path [84]. A trajectory is the robot's position as a

**Figure 5.** *The proposed HSPID controller based on the whole system.*

## *Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm DOI: http://dx.doi.org/10.5772/intechopen.108755*

function of time, and a path is a geometric description of the motion. Simulation software can be used to examine the motion of the arm robot and confirm that the robot follows the path [10, 85]. A trajectory planning of a robot allows us to determine the continuous position paths that will guide the end-effector of the robot [86]. The robot arm is designed for various applications, among which are those where the endeffector to reduce risks industrial risks, in which case a position control is required. The position control of the robot can be approached in two ways, one referring to the joint space and the other to the task space. To verify the trajectory planning, it was utilized Simscape model to simulate the motion while it is in operation bypassing the exact rigid parameters of the whole system into Matlab environment, besides building the trajectory block signals containing a position for five different signals, that the

**Figure 7.** *Robot arm motion simulation-based HSPID controller under three case studies, (a)case 1, (b) case 2, (c) case 3.*

robot arm should follow during the motion time. All of the above signals use one of three motion types: Point-To-Point (PTP), in which the robot positions of the trajectory move between them, or Continuous Path (CP), in which the signals move the manipulator tip along a given trajectory and the robot then accurately reproduces it. For displacements, the joint varies from 90° to 90°, and the displacement speed is modified by manipulating the angular frequency at 1 (rad/s).

As shown in **Figure 6**, we prepare three trajectory signals to execute three motion cases over a 12-second time trial to evaluate the maximum admissible torque at the end effector joint (joint 5), beginning from the initial state to the final state as presented in **Figure 7a–c**. The trajectory planning is inspected through multi-shape signals including straight lines, circles, and parabolic curves, according to the robot's sequence to simulate its movement. Therefore, we will run the Simscape model to simulate the model's angular trajectory based on the HSPID and display a 3D animation. Where the joint angles start in a fully extended vertical position at 0 degrees except the shoulder joint angle is fixed at 90 degrees. Finally, the end configuration was determined by the angular positions: shoulder = 60 deg., lower arm = 80 deg., upper arm = 60 deg., Wrist = 90 deg., end effector = 90 deg.

## **3. Simulation results**

In robot simulation, system analysis needs to be done [87]. The simulation results covered four subsections: manipulator joint estimation TF, optimization controller gain, motion results, and response time comparison between proposed controller and without controller.

## **3.1 Estimation TF of the manipulator joint**

**Figure 8** illustrates the best estimation TFs for the manipulator's joints for the following components: shoulder, lower arm, upper arm, wrist, and end effector, which was created by a Linear Analysis application. It was noticed that all resulted TFs form has estimated in the fourth order system.

## **3.2 Response time without controller**

The response time curves of the uncontrolled system are presented in **Figure 9**. As shown in **Table 1**, it was noticed that the response time parameters measured in the second unit, that mean the system is very poor responses.

## **3.3 Optimization proportional-based HSPID**

**Figure 10** shows the best objectives achieved by IGA. These convergence results reflected positively to optimize PID gains significantly as illustrated in **Table 2**, to be applied on each HSPID controller individually for each manipulator's joint.

## **3.4 Responses based on the proposed controller**

**Figure 11** shows the response time results based on each common component HSPID controller with a step signal. The results show a series of system responses to the robot components' orientation angle at a set point. The HSPID controller is a clear reduction response time, but there is some overshoot as illustrated in **Table 3**. The tuned linear responses look satisfactory regarding PID gains based on IGA.

#### **Figure 8.**

*Resulted estimation TFs of each revolution joint compnents, (a) shoulder, (b) lower arm, (c) upper arm, (d) wrist, (e) end effector.*

**Figure 9.** *Response time without controller for each joint components, (a) shoulder, (b) lower arm, (c) upper arm, (d) wrist.*


**Table 1.**

*Uncontrolled system responses for each joint component.*

## **3.5 Trajectory planning results**

As shown in **Figure 12**, three different trajectory signals were injected into the five joint angles to investigate motion case study in x-y plane. The input values to the joints are assorted and the angle motion results were obtained. With the orientation and position vectors as input, the joint angles are obtained as output. A simulated position of the end effector is introduced to be measured the maximum torque for each case on scope simulator. The recorded cases 1,2,3 are 0.06,0.038,0.042 N.m, respectively, as shown in **Figure 13**. Based on the results, it is proven that changing

### *Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm DOI: http://dx.doi.org/10.5772/intechopen.108755*

#### **Figure 10.**

*The effectivness of the IGA on FF for each revolution joint, (a) shoulder, (b) lower arm, (c) upper arm, (d) wrist, (e) end effector.*


#### **Table 2.**

*The optimized proportional gains and FF-based IGA for each revolution joint.*

#### **Figure 11.**

*Case 1 response time reduction based HSPID controller for each revolution joint component.*


**Table 3.**

*Case 1 response time reduction based HSPID controller for each revolution joint.*

the angle of any joint would result in a different end-effector position, and the 3D animation confirms that the arm moves quickly and precisely to the desired configuration.

## **4. Discussion**

To demonstrate the validity of the HSPID controller, we compare the response time characteristics of controlled and uncontrolled systems. The first response comparison includes a robot arm model without a controller, and the second one includes the model-based proposed HSPID controller simulation results **illustrate** that the use

*Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm DOI: http://dx.doi.org/10.5772/intechopen.108755*

#### **Figure 12.**

*Trajectory motions of each joint in X-Y plane for three cases, (a) case 1, (b) case 2, (c) case 3.*

**Figure 13.** *The torque results of the end effector joint for three case, (a) case 1, (b) case 2, (c) case 3.*

of HSPID provides significant reduction. **Table 4** refers to the improved reduction response time ratio (IRRTR) for the tr and ts of the joint manipulators. It can be seen that the maximum IRRTR for the tr occurs at the upper arm and the lowest at the shoulder. The ts results show that the greatest IRRTR occurs at the wrist and lowest at the shoulder. For comparison, the controller can efficiently compensate for orientation errors and quickly settle to an acceptable target value, providing advantages to


#### **Table 4.**

*Improved reduction response time ratio (IRRTR).*

augmenting the precision of the robot arm. In contrast, researchers can manipulate other parameters in the system to get more analytical results.

## **5. Conclusion**

In this chapter, the innovation 5 DOF robot arm has been designed by the application of SolidWorks side by side with and MATLAB Simscape toolbox for motion analysis and measuring the dynamics of the proposed model. The organigram of the design procedure is divided into five steps:(1) Mechanical configuration and assembly components, (2) Modeling Simscape, (3) Estimation TFs of the manipulator's joints, (4) Designing HSPID controller for joint manipulators (5), Mimic trajectory planning, then analyze response time characteristics and motion result. It is proposed a novel HSPID controller based on IGA offers a best solution to optimize trajectory planning and significant effectiveness of joint's torque.

The motion of the joint angle combinations through various angles and coordinates is controlled by applying three trajectory signals to manipulate the whole system and to compare the paths-based proposed controller with uncontrolled in terms of response time characteristics. For the 12 s trial-tested period, three case studies are and measure the distance, investigate the dynamic motion simulations, and confirm the efficiency of the design. It is observed that the HSPID enhanced the IRRTR significantly for the shoulder, lower arm, upper arm, and wrist in terms of tr by 1850%, 17,280%, 20,701%, and 13,753% respectively, and ts by121%, 755%, 879%, 950%. Thus, the efficiency of the robot arm is confirmed by the case studies, which is relating to trajectory planning. It is observed that the robot arm utilizes torque more effectively. Remarkably, the tr simulation results show that the lowest IRRTR appears into shoulder and the highest into upper arm, where the simulation ts results illustrate that the maximum IRRTR obtains in the wrist and the lowest in the shoulder. The main added value of the study is the elaboration and implementation of the new design method, which offers flexible design and higher-speed motion with less consuming energy. Finally, future manufacturing arm could be greatly improved by applying the proposed innovative design offering the best solution to increase accuracy, speed performance, and boost productivity for wide range of a various tasks.

## **Conflict of interest**

The authors declare that there is no conflict of interest.

*Perspective Chapter: Fabulous Design Speed Industrial Robotic Arm DOI: http://dx.doi.org/10.5772/intechopen.108755*

## **Author details**

Falih Salih Mahdi Alkhafaji Ministry of Industry and Minerals, State Company for Electrical and Electronic Industry, Iraq

\*Address all correspondence to: falih\_alkafaji1@yahoo.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## **Chapter 8**
