Deep Learning Technology-Based Exoskeleton Robot Controller Development

Model-based control is preferred for robotics applications due to its systematic approach to linearize and control the robot's nonlinear dynamics. The fundamental challenge involved in implementing a model-based controller for robotics applications is the time delay associated with the real-time computation of the robot dynamics. Due to the sequential structure of the robot's dynamic equation of motion, the multicore CPU cannot reduce the control algorithm execution time. A high-speed processor is required to maintain a higher sampling rate. Neural network-based modeling offers an excellent solution for developing a parallel structured equivalent model of the sequential model that is suitable for parallel processing. In this paper, a Deep neural network-based parallel structured 7 degrees of freedom human lower extremity exoskeleton robot controller is developed. Forty-nine densely connected neurons are arranged in four layers to estimate joint torque requirements for tracking trajectories. For training, the deep neural network, an analytical model-based data generation technique is presented. A trained deep neural network is used for real-time joint torque prediction and a PD controller is incorporated to mitigate the prediction errors. Simulation results show high trajectory tracking performances. The developed controller's stability analysis is proved. The robustness of the controller against the parameter variation is analyzed with the help of the analysis of variance (ANOVA). A comparative study between the developed controller and the Computed Torque Controller, Model Reference Computed Torque Controller, Sliding Mode Controller, Adaptive controller, and Linear Quadratic Regulator are presented while keeping the same robot dynamics.