Adaptive control of lower-limb rehabilitation robots is challenging due to the nonlinear dynamics of coupled joints, patient-specific uncertainties, and external disturbances during human–robot interaction. This study presents a hybrid quantum-inspired adaptive intelligent fuzzy control framework for a 3-DOF lower-limb rehabilitation robot. The quantum approximate optimization algorithm (QAOA) is used to tune fuzzy membership function parameters online, while the fuzzy logic controller uses joint position error and its derivative in a closed-loop configuration. Triangular membership functions are adjusted in real time to minimize the integral of squared error. The proposed controller is evaluated through simulations on a nonlinear dynamic model of the robot, including coupled joint interactions and typical disturbances. The results indicate the improvements in trajectory tracking accuracy, disturbance rejection, and energy dissipation compared to conventional fuzzy logic controllers and recent adaptive or reinforcement-learning-based methods. An energy-based analysis combined with Lyapunov stability assessment to confirm the enhanced closed-loop stability, showing faster energy dissipation for the QAOA-optimized fuzzy controller. The results ensure that integrating quantum optimization with fuzzy control can improve robustness and accuracy of rehabilitation robots, effectively handling nonlinearities and patient-specific uncertainties. The study employed Hardware-in-the-Loop testing to prove that the proposed controller achieves its intended performance during actual operation although the system requires ongoing monitoring for any performance-related issues. Results demonstrate that the QAOA-optimized fuzzy controller reduces the integral squared error (ISE) by approximately 96–99%, settling time by 62–63%, and limiting peak overshoot by nearly 75–80% across all considered test systems relative to previously reported controllers.
Abd-Elhaleem et al. (Thu,) studied this question.