Deep neural network-integrated finite-time fault-tolerant control for upper limb rehabilitation robots under actuator constraints

This paper introduces a hybrid fault-tolerant control framework for nonlinear upper-limb rehabilitation robots subject to actuator saturation and time-varying uncertainties. The approach combines a deep neural network (DNN)–based state-space model to capture nonlinear rehabilitation dynamics, a finite-time disturbance observer to address unmodeled effects and actuator degradation, and a finite-time sliding-mode controller that enforces actuator limits. Established finite-time Lyapunov tools are used to guarantee convergence in the presence of modeling errors, faults, and input constraints. Simulation studies under ideal, input-constrained, and actuator-fault conditions show substantial improvements in tracking accuracy, up to 58 % faster convergence, and smoother, more energy-efficient control inputs compared to PID and classical SMC baselines. The use of fixed-size matrix–vector computations supports real-time execution on embedded platforms. This framework effectively integrates data-driven modeling with robust finite-time control, providing a practical and reliable solution for human-in-the-loop rehabilitation systems.