To address adaptive trajectory control under external disturbances and state constraints, this paper proposes a PSO–FAS–MPC control framework that integrates a fully actuated system, model predictive control, and particle swarm optimization. FAS is used to linearize the system and simplify the MPC design; MPC optimizes the tracking performance under constraints, while PSO tunes the MPC parameters to enhance the adaptability to diverse scenarios. Comprehensive experimental validation on both 2‐DOF planar and 6‐DOF spatial manipulators demonstrates that the proposed framework reduces convergence time by up to 87% and tracking error by over 40% compared to conventional methods, while achieving significantly smoother torque profiles. These results validate the enhanced adaptability, scalability, and robustness of the integrated approach in handling complex nonlinear dynamics under various constraints and disturbances.
Zhang et al. (Thu,) studied this question.