Real-Time Adaptive Backstepping Control Enhanced by Fuzzy Approximation for Multi-(DOF) Prosthetic Hands

This research presents a novel fuzzy logic–based adaptive backstepping control (FLBC) strategy for real-time motion control of a multi-degree-of-freedom (multi-(DOF)) robotic prosthetic hand. The primary objective is to achieve smooth and accurate finger movements by adaptively tuning control parameters online in response to position and velocity tracking errors. The integration of fuzzy logic with adaptive backstepping control effectively addresses nonlinearities, uncertainties, and external disturbances commonly encountered in prosthetic hand systems, thereby ensuring stability and high tracking accuracy during diverse manipulation tasks. The proposed FLBC approach demonstrates superior performance compared to conventional control techniques, including proportional–integral–derivative (PID) control and sliding mode control (SMC), which often suffer from chattering effects or delayed adaptation. The controller exhibits faster convergence (within 0.3 s) and precise trajectory tracking. The control framework is evaluated on a robotic hand model comprising a 3-(DOF) thumb and 4-(DOF) fingers, successfully replicating essential hand motions such as flexion, extension, and grasping. Simulation results confirm that the proposed method significantly enhances dynamic performance, adaptability, robustness, and real-time responsiveness. Overall, this study contributes to the advancement of prosthetic hand technology by providing a flexible, accurate, and stable control solution for complex multi-(DOF) robotic hands.