The purpose of this study is to develop a computationally efficient deep learning-based control framework for high-degree-of-freedom exoskeleton robots to address the real-time computational limitations associated with conventional model-based control. A parallel-structured deep neural network was designed for a seven-degree-of-freedom human lower extremity exoskeleton robot. The network consists of four layers with 49 densely connected neurons and was trained using physics-based data generated from the analytical dynamic model. During real-time implementation, the trained neural network predicts joint torque commands required for trajectory tracking, while a proportional derivative controller compensates for residual prediction errors. Stability of the proposed control scheme was analytically established, and robustness to parameter variations was evaluated using analysis of variance. Comparative simulations were conducted against computed torque, model reference computed torque, sliding mode, adaptive, and linear quadratic controllers under identical robot dynamics. Results demonstrate accurate trajectory tracking with torque profiles comparable to conventional nonlinear controllers while reducing computational burden. These findings suggest that the proposed deep learning-based hybrid controller offers an efficient and robust alternative for controlling multi-degree-of-freedom exoskeleton robots.
S. K. Hasan (Sat,) studied this question.