Trajectory Tracking Control of Lower Limb Rehabilitation Exoskeleton Robot Based on Adaptive-Weight MPC

In this paper, an adaptive-weight model predictive control (AW-MPC) strategy is proposed to address the trajectory tracking problem of a lower-limb rehabilitation exoskeleton robot. First, based on human motion analysis, the dynamics of the lower-limb rehabilitation exoskeleton are established, and the nonlinear dynamic model is transformed into a linear model. Second, a MPC objective function is formulated to minimize the tracking error, yielding the optimal control input. Then, on the basis of conventional MPC, a weight-tuning scheme is developed: a weighting function is constructed according to the evolution of the tracking error to adaptively adjust the MPC weighting coefficients, and the closed-loop stability of the control system is proven via a Lyapunov-based analysis. Finally, the proposed method is validated on a lower-limb rehabilitation exoskeleton experimental platform, with a PID controller designed as a baseline for comparison. The experimental results demonstrate that, compared with the PID controller, the proposed AW-MPC achieves faster convergence of the tracking error, higher tracking accuracy, and enhanced robustness.