Optimization of sliding mode montroller parameters for planar parallel robot

Abstract

In this paper, we present a method for optimizing the parameters of a Sliding Mode Controller (SMC) using the Particle Swarm Optimization (PSO) algorithm. The SMC is well-known for its robust performance in handling system uncertainties and external disturbances. However, selecting the optimal parameters for the SMC plays a crucial role in achieving high performance in trajectory tracking tasks. Manual tuning often leads to suboptimal solutions, especially for complex nonlinear systems. To address this issue, we apply the PSO algorithm, a powerful parameter optimization technique inspired by the swarm behavior of birds or fish, to automatically search for the optimal control parameters. The optimized SMC parameters are then applied to the trajectory tracking control of a planar parallel robot. Simulation results, verified using Matlab Simulink, demonstrate high performance in trajectory tracking for the planar parallel robot.