Multi-fuzzy Sarsa learning-based sit-to-stand motion control for walking-support assistive robot

Abstract

Sit-to-stand transfer is a very common and critical movement of daily life in elderly individuals, especially independent elderly individuals. However, most assistive robots do not have a sit-to-stand transfer function. In this article, a multi-fuzzy Sarsa learning-based sit-to-stand motion control method for walking-support assistive robot was proposed. First, the mechanical design of walking-support assistive and sit-to-stand transfer motion control problems were introduced. Then, the fuzzy Sarsa learning method, which is a model-free algorithm, was used to design the motion control algorithm for the human–robot system. To realize natural and intuitive sit-to-stand transfer movement for a human–robot system, the interactive force between the robot and human and the error position between the real-time center of mass and reference center of mass were state variables of the proposed fuzzy Sarsa learning-based sit-to-stand motion control algorithm. Considering the computing efficiency of the controller, a multi-fuzzy Sarsa learning -based motion control algorithm was developed to realize natural sit-to-stand transfer motion. Finally, the experimental results verify the effectiveness of the proposed algorithm.