Particle Swarm Optimization for tuning the parameters of super twisting sliding mode controllers

Abstract

This research focuses on the efficient tuning of optimal parameters of Super-Twisting Sliding newlineMode Controllers (ST-SMC) using a modified Particle Swarm Optimization (PSO) with newlinedynamic inertia weight. This enables the ST-SMC systems to be used as effective and newlineoptimal controllers for dynamic uncertainty systems, particularly for highly nonlinear control newlineproblems like Maximum Power Point Tracking (MPPT) of Photo-Voltaic (PV) systems. The newlineresearch investigates the tuning of ST-SMC parameters using modified PSO based on newlinedifferent error indices such as IAE (Integral of Absolute Error), ITAE (Integral of Timeweighted newlineAbsolute Error), ISE (Integral of Squared Error), ITSE (Integral of Time-weighted newlineSquared Error) as the objective functions. These parametrically optimized systems based on newlinedifferent error indices are tested on a Photo-Voltaic system for the Maximum Power Point newlineTracking. The results show that the ST-SMC controllers tuned using different error indices as newlineobjective functions can meet the control objectives to suggest that the controllers are newlineoptimally tuned. The optimized ST-SMC using modified PSO is then compared with the newlineconventional Sliding Mode Controller (SMC) and the results suggest that the ST-SMC is newlinefound to be offering less chattering compared to the conventional Sliding Mode Controller newlinewhich is the inherent property of ST-SMC scheme. Hence, Super-Twisting Sliding Mode newlineControllers tuned using different error indices can exhibit the robust and optimal newlineperformance with reduced chattering. newline