Comparative study of controller optimization techniques for a robotic manipulator

Abstract

A robotic manipulator is a highly nonlinear, highly coupled, Multi-Input Multi-Output (MIMO) and dynamic system. Being complex in nature conventional Proportional-Integral-Derivative (PID) controller tuning methods, in general, are not applicable to these systems. For the past two decades, with the advent of efficient and high speed computing power, optimization techniques are being applied for complex non-linear processes for tuning of controllers. These techniques yield optimized performance for customised performance indexes. This paper presents a detailed comparative study of performances of three optimization techniques for the tuning of PID controllers for a two-link planar rigid robotic manipulator. In this work tuning of PID controllers has been carried out using three techniques namely Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithm (GA). Two performance indices namely Integral of Absolute Error (IAE) and Integral of Absolute Change in Control Output (IACCO) with equal weightage for both the links are considered for optimization. The robustness testing of optimized controllers is done for the trajectory tracking, model uncertainties and disturbance rejection. The simulation results clearly indicate that the PSO outperforms both SA and GA.