Affiliation:
1. Department of Electronics and Communication, GLA University, Mathura, Uttar Pradesh, India
2. Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India
Abstract
Background:
Robotic manipulator system has been useful in many areas like chemical
industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling
the end position of a robotic armeffectively. However, with the increasing non-linearity and
the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative
controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network
and optimization algorithms has emerged as an efficient tool for controlling the highly complex nonlinear
functions with uncertain dynamics.
Objective:
To implement an efficient and robustcontroller using Fuzzy Logic to effectively control
the end position of Single link Robotic Manipulator to follow the desired trajectory.
Methods:
In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose
parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute
Error as an objective function.
Results:
Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-
Derivative controller have been shown in this paper and the superiority of the implemented controller
has also been described by comparing itwith the conventional Proportional-Integral-Derivative
controller and Genetic Algorithm optimization technique.
Conclusion:
From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with
the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-
Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm
techniques.Also, by comparing the integral absolute error values of all the controllers, it has
been found that the controller optimized with the Spider Monkey Optimization technique shows
99% better efficacy than the genetic algorithm technique.
Publisher
Bentham Science Publishers Ltd.
Cited by
4 articles.
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