Enhancing Trajectory Tracking Performance of Wheeled Mobile Robot Using Backstepping Fuzzy Sliding Mode Control

Abstract

The rise in robotics technology has led to increased interest in three-wheeled mobile robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraint, which restricts their independent movement in all directions. Additionally, factors like sensor noise, nonlinear system dynamics, and uncertain system parameters add to the complexity controlling of TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between BFSMC and BSMC across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improved performance compared to BSMC across various trajectory types, quantified by calculating the percentage improvement in trajectory tracking using Integral Absolute Error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories.. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. The integration of the Extended Kalman Filter further improve accuracy, particularly in noisy conditions. Simulation results conducted using MATLAB/Simulink software validate the effectiveness of this approach in achieving superior trajectory tracking accuracy in TWMRs.