10.25394/PGS.10791269.v1
Hao Xiong
Hao
Xiong
Development of Learning Control Strategies for a Cable-Driven Device Assisting a Human Joint
Purdue University Graduate School
2019
cable-driven parallel robots
assistive devices
machine learning applied
robot control
Adaptive Agents and Intelligent Robotics
Control Systems, Robotics and Automation
Mechanical Engineering
Rehabilitation Engineering
2019-11-25 13:08:27
Thesis
https://hammer.purdue.edu/articles/thesis/Development_of_Learning_Control_Strategies_for_a_Cable-Driven_Device_Assisting_a_Human_Joint/10791269
<div>There are millions of individuals in the world who currently experience limited mobility as a result of aging, stroke, injuries to the brain or spinal cord, and certain neurological diseases. Robotic Assistive Devices (RADs) have shown superiority in helping people with limited mobility by providing physical movement assistance. However, RADs currently existing on the market for people with limited mobility are still far from intelligent.</div><div><br></div><div>Learning control strategies are developed in this study to make a Cable-Driven Assistive Device (CDAD) intelligent in assisting a human joint (e.g., a knee joint, an ankle joint, or a wrist joint). CDADs are a type of RADs designed based on Cable-Driven Parallel Robots (CDPRs). A PID–FNN control strategy and DDPG-based strategies are proposed to allow a CDAD to learn physical human-robot interactions when controlling the pose of the human joint. Both pose-tracking and trajectory-tracking tasks are designed to evaluate the PID–FNN control strategy and the DDPG-based strategies through simulations. Simulations are conducted in the Gazebo simulator using an example CDAD with three degrees of freedom and four cables. Simulation results show that the proposed PID–FNN control strategy and DDPG-based strategies work in controlling a CDAD with proper learning.</div>