An Improved MPC-PSO controller for the performance enhancement of a mobile robot in tracking a dynamic target

A mobile robot tracking a moving target is a challenging research area for real-time implementation. The proposed work uses a hybrid controller with an error dynamic model to provide improved error convergence. The Model Predictive Controller – Particle Swarm Optimization (IMPC-PSO)-based controller with Deb’s rule makes the robot more suitable for target tracking with quick and smooth error convergence by considering feasible optimal solutions without constraint violations. Performance investigations among conventional controller designs reveal the appropriateness of the proposed model for a real robot working with unavoidable system disturbances. The comparison shows the proposed controller is suitable for tracking applications, demanding short travel distances and smooth transit towards a dynamic target without jerk and vibration. Finally, the effectiveness of the controller is experimentally verified on QBOT 2e to validate simulation results in a confined workspace.