Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm
By definition, a mobile robot is a type of robot that has capability to move in a certain kind of environment and generally used to accomplish certain tasks with some degrees of freedom (DoF). Applications of mobile robots cover both industrial and domestic area. It may help to reduce risk to human...
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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Language: | English |
| Published: |
Penerbit Universiti Malaysia Pahang
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/101895/1/application%20of%20optimization%20for%20control.pdf http://irep.iium.edu.my/101895/ |
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| Summary: | By definition, a mobile robot is a type of robot that has capability to move in a certain kind of environment and generally used to accomplish certain tasks with some degrees of freedom (DoF). Applications of mobile robots cover both industrial and domestic area. It may help to reduce risk to human being and to the environment. Mobile robot is expected to operate safely where it must stay away from hazards such as obstacles. Therefore, a controller needs to be designed to make the system robust and adaptive. In this study, PID controller is chosen to control a wheel mobile robot. PID is considered as simple yet powerful controller for many kinds of applications. In designing PID, user needs to set appropriate controller gain to achieve a desired performance of the control system, in terms of time response and its steady state error. In this study, a new proposed hybrid optimization algorithm, called Extended Bat Algorithm (EBA) is used for optimizing the PID controller for the wheel mobile robot. Three different optimization algorithms which are Bat Algorithm (BA), Bat Algorithm with Mutation (BAM) and Extended Bat Algorithm (EBA) are implemented to optimize the value of PID controller gain for wheel mobile robot. The performance of these algorithms will be compared with a well-known optimization algorithm, Particle Swarm Optimization (PSO). The result shows that BAM has better performance compared to PSO in term of overshoot percentage and steady state error. BAM gives 2.29% of overshoot and 2.94% of steady state error. Meanwhile, PSO gives 3.07% of overshoot and 3.72% of steady state error. |
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