Design of dynamic adaptive control framework for exoskeleton robot driven by neuromuscular signal based on deep...

To enhance the dynamic adaptability and robustness of neuromuscular signal-driven exoskeleton robots in complex environments. This study develops a Deep Reinforcement Learning (DRL)-based exoskeleton control framework. A module for neuromuscular signal processing and motion intention modeling is designed, encompassing band-pass filtering, local normalization, time-frequency feature extraction, and Bidirectional Long Short-Term Memory (BiLSTM) time-series encoding. Additionally, a DRL-based dynamically adaptive control framework is established. Trajectory tracking error, torque smoothness, energy consumption agent, joint safety constraint and intention consistency are comprehensively introduced into the reward function to achieve the multi-objective balance of "accuracy-comfort-energy consumption-safety." Comparative experiments demonstrate that the joint trajectory tracking errors of the proposed optimized system are 2.37°, 2.64°, and 2.92°, respectively. These values are significantly lower than those of comparative systems, including the Deep Reinforcement Learning-based Robust Controller for Lower-Limb Rehabilitation Exoskeletons (DRL-RC-LLRE) and the Learning-in-Simulation Exoskeleton Assistance Framework (LiS-EXO). The corresponding torque smoothness indicators are 0.62, 0.68, and 0.71. The mechanical energy consumption proxy indicators are 10.48, 11.39, and 11.67, indicating that while improving the tracking accuracy, the torque oscillation is effectively suppressed and the energy consumption is reduced. The ablation experiments further show that when any of the neuromuscular intention embedding, intention consistency reward or safety constraint modules are removed, the average trajectory error, torque smoothness and energy consumption indicators all deteriorate to varying degrees. The complete model achieves 2.63°, 0.66, and 11.17, respectively, in the three indicators, verifying the collaborative contribution of the three key modules to the overall performance. Overall, this study has constructed and verified a reproducible "neuromuscular signal-motion intention-reinforcement learning control" integrated framework, and thus having certain contributions to the field of intelligent exoskeleton control.