**5.1. Experiments**

from 0 to 20 s. **Figure 7(b)** is an enlarged view of control performances from 19 to 20s. It can be seen that the system output well tracks the input command. **Figure 8** further exhibits the superiority of the CMAC: the tracking errors are tiny, mostly between −0.2 and 0.2 lbs. Since there exists a modeling error between identified system and actual controlled plant, the controller's performances will be further validated in the real material-strength testing

(a) 0~20s (b) 19~20s





Force(lb)




19 19.1 19.2 19.3 19.4 19.5 19.6 19.7 19.8 19.9 20

Simulation Result(19-20s)

Output Input

Time(s)

Tracking error(20s)

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>12</sup> <sup>14</sup> <sup>16</sup> <sup>18</sup> <sup>20</sup> -1

Time(s)

experiments.

**Figure 7.** Simulation results.

266 Adaptive Robust Control Systems


Force(lb)

**Figure 8.** Tracking error.


Force(lb)

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>12</sup> <sup>14</sup> <sup>16</sup> <sup>18</sup> <sup>20</sup> -200

Time(s)

Simulation Result(0-20s)

For verifying the controller's adaption to modeling error in the real-time control experiments, we implement above control law in C language and download compiled files into the embedded system. The central processing unit is an ARM7-based processor LPC2294. The LPC2294 is a 32-bit reduced instruction set computer (RISC) processor with low power consumption and high performance. Although there is no Float Point Unit (FPU) in this processor, the 70 million instructions per second (MIPS) processing speed makes it ideal for the real-time control system. The control step size is set as 50 ms.

The actual force tracking control diagram is shown in **Figure 9**. The force is applied to the glass sheet specimen by a loader which is connected to a linear motor actuator through a steel bar. A three point bending test is utilized under this configuration. The load force followed a ramp function with time as the independent variable. The slope of the ramp function can be programmed through the LCD touch screen. The force range can be from 0 to 300 lbs. Displacement of the motor, which also reflected the deformation of the glass sheet specimen, is recorded during the test. The signal flow of the control system is as follows. The DAC generates the control outputs as a voltage signal, which is amplified by a power amplifier and exerted on the linear motor. The linear motor then transforms the voltage signal into rotation, and generates linear displacements. The force sensor and conditioner measure the displacements and generates charges, which are transformed back to a voltage signal and fed back through ADC to the microcontroller.

**Figure 9.** Actual force tracking control diagram.

The input command and control parameters are the same as those of simulations. **Figure 10(a)** shows that the output force basically tracks the input command well and the controller implemented on the embedded system can perform the control task in real time within the control step size. From **Figure 10(b)**, it can be seen that tracking errors of the experiment are mostly between −2.5 and 2.5 lbs, which are larger than those of the simulation. The reason is that uncertainties and disturbances always exist in the real world, which mainly reflects in modeling error. However, the maximum absolute tracking error is 3.79, and the variance of tracking errors is 1.69, which are still acceptable in the actual real-time control environment. Moreover, it verifies that the controller tuned by simulations is also available for actual experiments, and indirectly proves the effectiveness of identified system model.

#### **5.2. Comparisons**

As shown in **Figure 11**, an inverse model feedforward control is compared with the CMAC feedforward control by embedded-system-based experiments. Its basic idea is to directly employ the inverse model 1/G(s) of the plant's identified transfer function G(s) in Eq. (26) to be the feedforward part. Obviously, this control scheme is also suitable for real-time implementations; even its computation cost is less than that of the CMAC scheme.

**Figure 12(a)** shows overall performances of the tracking control with the inverse model scheme. It can be seen that the output force basically tracks the input command and the controller implemented on the embedded system can perform the control task in real time within the control step size; however, the tracking control is not performed very well. As shown in **Figure 12(b)**, tracking errors are mostly between −5 and −2 lbs, which are larger than those of the CMAC scheme. **Table 2** also shows that the maximum absolute tracking error and variance of tracking errors of the inverse model scheme are both larger than those of the CMAC scheme, so that performances of the CMAC scheme are superior to those of the inverse model

(a) Input command and output force (b) Tracking error

scheme. Although the CMAC scheme and inverse model scheme both utilize inverse model idea, the difference is that the latter directly employs the unchanged inverse model of the identified transfer function and the modeling error always exists, but the inverse model

A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading…

http://dx.doi.org/10.5772/intechopen.71420

269

Compared with the traditional PID control method, the learning behavior and adaptive feature of the proposed CMAC control algorithm are embodied in the freedom of tuning control

The PID control method has been widely used because of its simpleness, but the tuning problem of PID parameters (proportional, integral and differential) is difficult. At present, PID parameter-tuning optimization depends on the experiences of technical staffs and needs a

(a) Input command and output force (b) Tracking error


Error (lb)

0

5

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>12</sup> <sup>14</sup> <sup>16</sup> <sup>18</sup> <sup>20</sup> -10

Time (s)

Tracking error

approximated by the CMAC is dynamical and adaptive.

0 2 4 6 8 10 12 14 16 18 20

Experimental Tracking Result

Time (s)

**Figure 12.** Experimental results (inverse model feedforward control).

**Figure 11.** Block diagram of the inverse model feedforward control.

parameters and the robustness to the disturbances in the real world.

Output Force Input command

**5.3. Results analysis**




Force (lb) -100


0

**Figure 10.** Experimental results.

A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading… http://dx.doi.org/10.5772/intechopen.71420 269

**Figure 11.** Block diagram of the inverse model feedforward control.

scheme. Although the CMAC scheme and inverse model scheme both utilize inverse model idea, the difference is that the latter directly employs the unchanged inverse model of the identified transfer function and the modeling error always exists, but the inverse model approximated by the CMAC is dynamical and adaptive.

#### **5.3. Results analysis**

The input command and control parameters are the same as those of simulations. **Figure 10(a)** shows that the output force basically tracks the input command well and the controller implemented on the embedded system can perform the control task in real time within the control step size. From **Figure 10(b)**, it can be seen that tracking errors of the experiment are mostly between −2.5 and 2.5 lbs, which are larger than those of the simulation. The reason is that uncertainties and disturbances always exist in the real world, which mainly reflects in modeling error. However, the maximum absolute tracking error is 3.79, and the variance of tracking errors is 1.69, which are still acceptable in the actual real-time control environment. Moreover, it verifies that the controller tuned by simulations is also available for actual experiments, and

As shown in **Figure 11**, an inverse model feedforward control is compared with the CMAC feedforward control by embedded-system-based experiments. Its basic idea is to directly employ the inverse model 1/G(s) of the plant's identified transfer function G(s) in Eq. (26) to be the feedforward part. Obviously, this control scheme is also suitable for real-time imple-

**Figure 12(a)** shows overall performances of the tracking control with the inverse model scheme. It can be seen that the output force basically tracks the input command and the controller implemented on the embedded system can perform the control task in real time within the control step size; however, the tracking control is not performed very well. As shown in **Figure 12(b)**, tracking errors are mostly between −5 and −2 lbs, which are larger than those of the CMAC scheme. **Table 2** also shows that the maximum absolute tracking error and variance of tracking errors of the inverse model scheme are both larger than those of the CMAC scheme, so that performances of the CMAC scheme are superior to those of the inverse model

(a) Input command and output force (b) Tracking error


Error (lb)

0

5

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>12</sup> <sup>14</sup> <sup>16</sup> <sup>18</sup> <sup>20</sup> -10

Time (s)

Tracking error

0 2 4 6 8 10 12 14 16 18 20

Experimental Tracking Result

Time (s)

mentations; even its computation cost is less than that of the CMAC scheme.

Output Force Input command

indirectly proves the effectiveness of identified system model.

**5.2. Comparisons**

268 Adaptive Robust Control Systems



**Figure 10.** Experimental results.



Force (lb) -50

0

50

Compared with the traditional PID control method, the learning behavior and adaptive feature of the proposed CMAC control algorithm are embodied in the freedom of tuning control parameters and the robustness to the disturbances in the real world.

The PID control method has been widely used because of its simpleness, but the tuning problem of PID parameters (proportional, integral and differential) is difficult. At present, PID parameter-tuning optimization depends on the experiences of technical staffs and needs a

(a) Input command and output force (b) Tracking error

**Figure 12.** Experimental results (inverse model feedforward control).


constant. In this case, the problems how to identify the parameters of the system model accurately for simulation-based controller design and how to adjust the CMAC plus PD controller

This work is supported by National Key R&D Program of China under Grant Nos. 2016YFD0200700 and 2017YFD0701000, and Chinese Universities Scientific Fund under Grant

, will be under consideration in our future work.

A CMAC-Based Systematic Design Approach of an Adaptive Embedded Control Force Loading…

http://dx.doi.org/10.5772/intechopen.71420

271

, Shubo Wang1,2, Zichao Zhang1,2, Guangqi Wang1,2,

to compensate the varying *KF*

Nos. 2017QC139 and 2017GX001.

Yu Tan1,2 and Yongjun Zheng1,2

, Gangbing Song<sup>3</sup>

Photovoltaics (BiPV). New York: Routledge; 2014

the Institute of Measurement and Control. 2008;**30**(5):427-450

Materials & Solar Cells. 2015;**132**:455-459

Measurement and Control. 2013;**35**(3):342-352

1 College of Engineering, China Agricultural University, Beijing, China

3 Department of Mechanical Engineering, University of Houston, TX, USA

2 Key Laboratory of Soil-Machine-Plant System Technology, Ministry of Agriculture,

[1] Prasad D, Snow M. Designing with Solar Power: A Source Book for Building Integrated

[2] Dhere NG, Raravikar NR. Adhesional shear strength and surface analysis of a PV module deployed in harsh coastal climate. Solar Energy Materials & Solar Cells. 2001;**67**(1):363-367

[3] Burrows K, Fthenakis V. Glass needs for a growing photovoltaics industry. Solar Energy

[4] Hughes ZM, Pont MJ.Reducing the impact of task overruns in resource-constrained embedded systems in which a time-triggered software architecture is employed. Transactions of

[5] Huang SJ, Yu CK, Lin JY. Intelligent robotic impedance control using embedded system structure. Transactions of the Institute of Measurement and Control. 2012;**35**(5):561-573

[6] Moallem P, Zargari A, Kiyoumarsi A. Improving IEC flickermeter for implementation by an ARM microcontroller-based digital system. Transactions of the Institute of

\*Address all correspondence to: jchen@cau.edu.cn

**Acknowledgements**

**Author details**

Beijing, China

**References**

Jian Chen1,2\*, Peng Li<sup>3</sup>

**Table 2.** Comparisons between the CMAC and the inverse model.

lot of manpower and time, which means that the optimal PID parameters are difficult to be obtained by people's tuning, and the inappropriate parameters cannot guarantee the control performances to meet the control requirements. In addition, the PID control law is a kind of the linear control law which owns few robustness to the disturbances. It means that even though the PID parameters are tuned optimally and perfectly in the simulations, the tuned parameters may perform poor in the real world due to disturbances and uncertainties.

Thus, combining the adaptive CMAC and the traditional PID to construct an intelligent neural network PID controller, can automatically identify the controlled plant and adaptively adapt the control parameters of the CMAC, which can solve the difficult problem of tuning parameters of the traditional PID controller. As shown in Sections 4 and 5, the PID parameters of the proposed CMAC algorithm in the experiments are same as those in the simulations, which verifies control performances of the proposed CMAC algorithm are independent of tuning PID parameters. Experimental results in the real world in Section 5 also demonstrate the robustness of the proposed algorithm owing to the CMAC neural network, while the traditional PID controller does not have this capacity.
