**4.2 Mechanism**

A mobile robot needs locomotion mechanisms that enable it to move unbounded throughout its environment. There is a large variety of possible ways to move which makes the selection of a robot's approach to locomotion an important aspect of mobile robot design. Most of these locomotion mechanisms have been inspired by their biological counterparts which are adapted to different environments and purposes.[9],[10] Many biologically inspired robots walk, crawl, slither, and hop.

In mobile robotics the terms omnidirectional, holonomic and non holonomic are often used, a discussion of their use will be helpful.[9]

The terms holonomic and omnidirectional are sometimes used redundantly, often to the confusion of both. Omnidirectional is a poorly defined term which simply means the ability to move in any direction. Because of the planar nature of mobile robots, the operational space they occupy contains only three dimensions which are most commonly thought of as the x, y global position of a point on the robot and the global orientation, θ, of the robot. Whether a robot is omnidirectional is not generally agreed upon whether this is a twodimensional direction, x, y or a three-dimensional direction, x, y, θ. In this context a non holonomic mobile robot has the following properties:


Fuzzy Logic Controller for Mechatronics and Automation 131

using the fuzzy algorithm. Fuzzy logic is becoming more popular among the control method in designing the complex system as it is approved in many researches that it can simplify a complex model. Furthermore, fuzzy logic drives the controller to think like human in optimizing the performance. In this application where the robot needs details output such as how right it should turn or how fast it should accelerate, fuzzy approach could overcome

Hairol Nizam[6] in his work explained the design of fuzzy algorithm for robot in

The figure shows two angles assigned as linguistic variables. The ߠ௦and ߠ will be compared with ߠ to obtain the linguistic values. The following table shows how the rules are

> Large Equal Small Large Right Right Left Equal Right Forward Left Small Right Left Left

௦ߠ

these constraints.

constructed.

ߠ

approaching the desired destination.

Fig. 23. Autonomous Robot with parameters defined

Table 10. Fuzzy Rules for Autonomous Robot

From the table, the rules can be constructed as listed below Rule 1; IF θs is Large and θ*r* is Large, Then direction is *Right*  Rule 2; IF θs is Large and θ*r* is Equal, Then direction is *Right*  Rule 3; IF θs is Large and θ*r* is Small, Then direction is *Right*  Rule 4; IF θs is Equal and θ*r* is Large, Then direction is *Right*  Rule 5; IF θ*s* is Equal and θr is Equal, Then direction is *forward*  Rule 6; IF θ*s* is Equal and θr is Small, Then direction is *Right*  Rule 7; IF θ*s* is Small and θr is Large, Then direction is *Left*  Rule 8; IF θ*s* is Small and θr is Equal, Then direction is *Left*  Rule 9; IF θ*s* is Small and θr is Small, Then direction is *Left* 

degrees of freedom, there are certain motions they cannot perform. This creates difficult problems for motion planning and implementation of reactive behaviours.

 Holonomic however, offer full mobility with the same number of degrees of freedom as the environment. This makes path planning easier because there aren't constraints that need to be integrated. Implementing reactive behaviours is easy because there are no constraints which limit the directions in which the robot can accelerate.

In general, the mobile robot deals with the environment that is not certain and unknown. The environment may consist of several obstacles and paths which the robot has to go through. At the same time, the robot has to maintain its stability when changing direction if it faces the obstacles. This would definitely require the good control of the robot to make it reach to the desired points. With the limited information obtained during its operational, the fuzzy control is the best method to optimize its time consuming and energy consumption while reaching its goal. Harmeet Singh [4] in his work mentioned that the fuzzy control technique is the most suitable method to deal with the variability and unknown parameters in the given system. He also listed the constraints of the mobile robots that can be fulfilled with the fuzzy control techniques. There are:-


Generally, a mobile robot is built with wheels, motors and controller. The controller is designed in two types of control method. The open loop control and closed loop feedback control types [5]. In open loop control, the inputs are provided beforehand to make the robot reach the goal. This involves certain known parameters such as the acceleration or torque of the motor, the turning point and stop point, and time operation where the robot needs to stop or make a turning, and the angle of turning. This type of methods would only suitable for the known environment and paths. For the closed loop strategies, the sensors are equipped with the robots and the robot will respond to the certain data obtained through sensors. The robot will act in a way that the controller was designed beforehand. For instant, the robot is programmed to move to the right direction if it faces any obstacle in front.

#### **4.3 Controller design**

In the real system, the obstacles or paths are not the ideal and vary in shapes and locations. If the robot is set to turn right when facing the obstacles, it does not mean the robot must turn 90 degree to the right. The controller should be designed in a way that it still approaching the desired target. Otherwise, it may turn back to its original point and that would not only consume time but waste energy as well. Thus, a good control must have very details conditions and settings to optimize the performance of the robot. The conventional type of control that is already designed is P, PI and PID control. However, the model becomes more complicated and difficult to implement with. Another alternative is by

 Holonomic however, offer full mobility with the same number of degrees of freedom as the environment. This makes path planning easier because there aren't constraints that need to be integrated. Implementing reactive behaviours is easy because there are no

In general, the mobile robot deals with the environment that is not certain and unknown. The environment may consist of several obstacles and paths which the robot has to go through. At the same time, the robot has to maintain its stability when changing direction if it faces the obstacles. This would definitely require the good control of the robot to make it reach to the desired points. With the limited information obtained during its operational, the fuzzy control is the best method to optimize its time consuming and energy consumption while reaching its goal. Harmeet Singh [4] in his work mentioned that the fuzzy control technique is the most suitable method to deal with the variability and unknown parameters in the given system. He also listed the constraints of the mobile robots that can be fulfilled

1. Difficulty in deriving the mathematical model of the environment. Even if it is simplified, the model could be very complex to be implemented with the hardware. 2. Sensors that are equipped on the mobile robot could be different and varies. The model of the conventional controller may not considering certain data obtained from the

3. Need of the real-time operation. Thus, the robot requires fast responds and operations.

Generally, a mobile robot is built with wheels, motors and controller. The controller is designed in two types of control method. The open loop control and closed loop feedback control types [5]. In open loop control, the inputs are provided beforehand to make the robot reach the goal. This involves certain known parameters such as the acceleration or torque of the motor, the turning point and stop point, and time operation where the robot needs to stop or make a turning, and the angle of turning. This type of methods would only suitable for the known environment and paths. For the closed loop strategies, the sensors are equipped with the robots and the robot will respond to the certain data obtained through sensors. The robot will act in a way that the controller was designed beforehand. For instant, the robot is programmed to move to the right direction if it faces any obstacle in

In the real system, the obstacles or paths are not the ideal and vary in shapes and locations. If the robot is set to turn right when facing the obstacles, it does not mean the robot must turn 90 degree to the right. The controller should be designed in a way that it still approaching the desired target. Otherwise, it may turn back to its original point and that would not only consume time but waste energy as well. Thus, a good control must have very details conditions and settings to optimize the performance of the robot. The conventional type of control that is already designed is P, PI and PID control. However, the model becomes more complicated and difficult to implement with. Another alternative is by

Complicated robot controller will lead to slow and undesired performance.

problems for motion planning and implementation of reactive behaviours.

constraints which limit the directions in which the robot can accelerate.

with the fuzzy control techniques. There are:-

front.

**4.3 Controller design** 

sensors which might lead to the instability of the robot.

degrees of freedom, there are certain motions they cannot perform. This creates difficult

using the fuzzy algorithm. Fuzzy logic is becoming more popular among the control method in designing the complex system as it is approved in many researches that it can simplify a complex model. Furthermore, fuzzy logic drives the controller to think like human in optimizing the performance. In this application where the robot needs details output such as how right it should turn or how fast it should accelerate, fuzzy approach could overcome these constraints.

Hairol Nizam[6] in his work explained the design of fuzzy algorithm for robot in approaching the desired destination.

Fig. 23. Autonomous Robot with parameters defined

The figure shows two angles assigned as linguistic variables. The ߠ௦and ߠ will be compared with ߠ to obtain the linguistic values. The following table shows how the rules are constructed.


Table 10. Fuzzy Rules for Autonomous Robot

From the table, the rules can be constructed as listed below

Rule 1; IF θs is Large and θ*r* is Large, Then direction is *Right*  Rule 2; IF θs is Large and θ*r* is Equal, Then direction is *Right*  Rule 3; IF θs is Large and θ*r* is Small, Then direction is *Right*  Rule 4; IF θs is Equal and θ*r* is Large, Then direction is *Right*  Rule 5; IF θ*s* is Equal and θr is Equal, Then direction is *forward*  Rule 6; IF θ*s* is Equal and θr is Small, Then direction is *Right*  Rule 7; IF θ*s* is Small and θr is Large, Then direction is *Left*  Rule 8; IF θ*s* is Small and θr is Equal, Then direction is *Left*  Rule 9; IF θ*s* is Small and θr is Small, Then direction is *Left* 

Fuzzy Logic Controller for Mechatronics and Automation 133

In this case, the distance between the destination point and the robot is determined with the

With the rules constructed, the mobile robot could be guided to the direction of the

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[2] Kelly Cohen, Tanchum Weller, Joseph Z, Ben-Esher, (2000). "*Active Control of Flexible* 

[3] Lynch P.J. and Banda S.S., (1988). "*Active Control for Vibration Damping*, *Large Space* 

[4] Harmeet Singh, Sanchit Arora, Aparna Mehra, (2008)."*Using Fuzzy Logic for Mobile Robot Control*", Mathematics Department, Indian Institute of Technology Delhi. [5] Vamsi Mohan Peri, Dan Simon, "*Fuzzy Logic Control For an Autonomous Robot*", Department of Electrical and Computer Engineering, Cleveland State University. [6] Razif Rashid, I. Elamvazuthi, Mumtaj Begam, M. Arrofiq "Fuzzy-based Navigation and

[7] Hairol Nizam Mohd Shah, Marizan Sulaiman, Syed Najib Syed Salim, (2007)."*Fuzzy* 

[8] V. K. Banga, R. Kumar, Y. Singh, (2009). "*Fuzzy-Genetic Optimal Control for Four Degree*",

[9] H. Eskandar, Pouya Salehi, M.H.Sabour (2010). "*Fuzzy Logic Tracking Control for a Three* 

[10] Robert Holmberg, "Design and Development of Powered-Castor Holonomic Mobile

[11] Roland Siegwart, Illah R. Nourbakhsh "Introduction to Autonomous Mobile Robots",

[12] Mohd Ashraf Ahmad "European Journal of Scientific Research" Vol.27 No.3 (2009),

[13] Wahyudi and J. Jalani "Intelligent Gantry Crane System" Proceedings of the 2nd

World Academy of Science, Engineering and Technology 60.

*Flexible Joint Manipulator*", Department of Mechatronics Engineering, Kocaeli

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*Structures: Dynamics and Control"*, Ed. Atluri S.N and Amos A.K., Springer-Verlag

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The MIT Press, Massachusetts Institute of Technology, Cambridge, Massachusetts,

International Conference on Mechatronics, ICOM'05 10-12 May *2005, Kuala Lumpur,* 

, and destination angle, ߠ ൌ ߠௗ െ ߠ.

<sup>ଶ</sup> ݀௬ ଶ

destination position, ݀ ൌ ට݀௫

**5. References** 

destination point with minimum movement.

Technion City, ICAS Congress.

Berlin Heidelberg, pp. 239-262.

3, march 2010, issn 2151-9617

Robots", Stanford University, 2000

pp.322-333 EuroJournals Publishing, Inc. 2009

Malaysia, Vol. 68, No. 4.

11(3), pp. 321-326.

2004

*Malaysia*

University, Turkey.

Fig. 24. Parameter defined for mobile robot

Fig. 25. Robot following path with obstacle avoidance criteria

In this case, the distance between the destination point and the robot is determined with the destination position, ݀ ൌ ට݀௫ <sup>ଶ</sup> ݀௬ ଶ , and destination angle, ߠ ൌ ߠௗ െ ߠ.

With the rules constructed, the mobile robot could be guided to the direction of the destination point with minimum movement.

#### **5. References**

132 Fuzzy Inference System – Theory and Applications

Fig. 24. Parameter defined for mobile robot

Fig. 25. Robot following path with obstacle avoidance criteria


**7** 

*Cinvestav Mexico* 

**Fuzzy Inference Systems Applied to** 

Within industry, the concept of maintenance can be handled in different ways. It can be done periodically at predefined times, according to the type of machine, and according to the manufacturers' recommendations. In this case, it is referred to as scheduled preventive maintenance. Maintenance done when there is faulty equipment is commonly called corrective maintenance. Employing electrical machines' operating signals may be useful for

Three-phase electrical machines such as induction motors or generators are used in a wide variety of applications. In order to increase the productivity and to reduce maintenance costs, condition monitoring and diagnosis is often desired. A wide variety of conditioning monitoring techniques has been introduced over the last decade. These include the electric current signature and stator vibrations analysis (Cusido & Romeral & Ortega & Espinoza, 2008; Blodt & Granjon & Raison & Rostaing, 2008; Blodt & Regnier & Faucher, 2009; Riera &

Nowadays, industry demands solutions to provide more flexible alternatives for maintenance, avoiding waste of time in case of major requirements to unforeseen failures, as well as time of scheduled maintenance. This creates the necessity to propose and implement predictive technologies, which ensure that machinery receive attention only when they present some evidence of their mechanical properties deterioration (Taylor, 2003).

1. Different problems can be apparent with the same frequency. For example, the unbalance, the one-axis flexion, the misalignment or some resonances, all can be apparent within the same frequency interval. Likewise, a machine may vibrate due to

2. Models do not precisely represent the machine's behavior, since frequently studies assume that the constituent parts and load mechanics are perfectly symmetric. Likewise, in the electrical motor's case, normally it is assumed that electrical sources

Vibrations have been one of the usual machinery's physical state indicators.

problems related with another machine to which it is coupled.

Some issues related to failures in machinery are as follows:

**1. Introduction** 

diagnosis purposes.

Daviu & Fulch, 2008).

are balanced.

**the Analysis of Vibrations in** 

Fredy Sanz, Juan Ramírez and Rosa Correa

**Electrical Machines** 

[14] Jamaludin J., Wahyudi, Iswaini and Suhaimi A, "Development of Automatic Gantry Crane Part2:Controller Design and Implementation", in Proc. The 5th Industrial Electronics Seminar 2004, Surabaya, 11 October 2004.
