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.

This paper proposes a passive fault-tolerant control strategy for a multi-caliper disc elevator brake system subject to unknown external disturbances and multiple actuator faults. Initially, a detailed analysis of the dynamic equations of the actuator in a multi-caliper disc elevator brake system with actuator faults is conducted. Subsequently, a nonsingular terminal sliding mode fault-tolerant control scheme with rapid fixed-time convergence is proposed, where the settling time is independent of the system’s initial state and can be preset through design parameters. The upper bound of the convergence time is derived using Lyapunov theory, ensuring that the faulty elevator brake control system converges within a predetermined fixed time. Ultimately, theoretical analysis and numerical simulation results confirm that the proposed controller can effectively handle the effects of actuator faults, parametric uncertainties, and external disturbances, ensuring satisfactory tracking accuracy.