Comparative Analysis of NMPC and Fuzzy PID Controllers for Trajectory Tracking in Omni-Drive Robots: Design, Simulation, and Performance Evaluation

Trajectory tracking for an Omni-drive robot presents a challenging task that demands an efficient controller design. This paper introduces a self-optimizing controller, Type-1 fuzzyPID, which leverages dynamic and static system response analysis to overcome the limitations of manual tuning. To account for system uncertainties, an Interval Type-2 fuzzyPID controller is also developed. Both controllers are designed using Matlab/Simulink and tested through trajectory tracking simulations in the CoppeliaSim environment. Additionally, a non-linear model predictive controller(NMPC) is proposed and compared against the fuzzyPID controllers. The impact of tunable parameters on NMPC tracking accuracy is thoroughly examined. We also present plots of the step-response characteristics and noise rejection experiments for each controller. Simulation results validate the precision and effectiveness of NMPC over fuzzyPID controllers while trading computational complexity. Access to code and simulation environment is available in the following link: https://github.com/love481/Omni-drive-robot-Simulation.git.