Adaptive Fractional-Order Sliding Mode Control for Mobile Robots: A PSO-Based Optimization Framework

This study presents an adaptive fractional sliding mode control (AFSMC) strategy for finite-time trajectory tracking of differential drive mobile robots (DDMR) in the presence of system uncertainties and external disturbances. The proposed control strategy employs a fractional-order sliding surface and an optimized reaching law to ensure finite-time convergence of tracking errors while retaining robustness against external disturbances. Particle Swarm Optimization (PSO) is employed for offline parameter tuning, where its capability to efficiently handle a large parameter space makes it an ideal choice for optimizing the control system, ensuring both enhanced performance and computational feasibility. Simulation tests comparing the AFSMC with a conventional SMC show that the AFSMC exhibits higher tracking accuracy, robustness, and precise linear and angular velocity tracking. The results demonstrate the AFSMC's resilience to disturbances and minimal steady-state error, even under challenging conditions.