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 non-linear 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.
Multi-Level Planning and Scheduling for Parallel PCB Assembly Lines Using Hybrid Spider Monkey Optimization Approach
