Integrated Co-Simulation and Control Framework for Intelligent Bionic Hands: System Validation with Human Subjects Using MATLAB and ADAMS

This paper presents a novel mechatronic framework for the design, co-simulation, and adaptive control of an intelligent bionic hand with 15 degrees of freedom (DoF). Building on prior work in prosthetic control, we integrate machine learning (ML)-driven gesture recognition with real-time adaptive PID control and tactile feedback to achieve high-fidelity motion and robust object manipulation. The mechanical model, developed in MSC ADAMS, employs tendon-driven actuation with dynamic friction and damping, while the control system—implemented in MATLAB/Simulink—combines: Low-level adaptive PID control for joint-angle tracking, with gains dynamically tuned via rule-based optimization to reduce overshoot by 23% compared to static PID.High-level EMG-based intent recognition using SVM classification (95.1% accuracy) of time-domain features (MAV, ZC, SSC) from a Myo armband’s-adaptive Q-learning to refine grip force through tactile sensor feedback. The MATLAB-ADAMS co-simulation framework enables synchronized testing of physical and control dynamics, demonstrating an average joint error of <1.5° and 92% success in object manipulation tasks. Key contributions include: A hybrid control architecture bridging ML-based intent detection and mechatronic execution’s-simulation validation of adaptive strategies under dynamic loads. Tactile-enabled reinforcement learning for continuous user adaptation. This work advances the state of the art in prosthetic mechatronics by unifying data-driven control, high-DoF simulation, and human-in-the-loop learning—critical for deployable bionic systems