Trajectory Tracking of Robotic Manipulators: A Fully-Actuated System Combined with Model Predictive Control Method

To address the challenges of external disturbances and state constraints in trajectory tracking systems, this paper proposes a novel control strategy (FAS-MPC) combining Fully-Actuated System (FAS) and Model Predictive Control (MPC). First, a FAS-based controller is designed to compensate for nonlinear dynamics, achieving a linear closed-loop system with enhanced design flexibility. Then, the MPC algorithm is integrated to optimize the controller under system and state constraints, transforming the optimization problem into a quadratic programming framework. Simulation results demonstrate that the FAS-MPC controller significantly improves control accuracy and stability in complex nonlinear systems, particularly in high-dynamic and multi-degree-of-freedom scenarios. Compared to traditional FAS methods, FAS-MPC exhibits superior trajectory tracking performance, reduced torque fluctuations, and faster convergence, making it highly suitable for high-precision robotic applications.