Control of Flexible Manipulator Based on Reinforcement Learning

摘要

Most researches about control of flexible manipulators are all based on the dynamic model, which is difficult to establish because of their flexibility and the tedious process of measuring flexible link's parameters. In this paper, the goal is to design a controller which is able to control the flexible manipulator to track a given position in joint space and suppress vibration without knowing the dynamic model. For the problem of tracking a given position, a tracking controller is designed based on sliding mode control, and for the purpose of vibration suppression, a vibration suppression controller is designed as a deep neural network. Because the input of the flexible manipulator, torques at each joint, is a high dimensional and continuous space, Deep Deterministic Policy Gradient Algorithm (DDPG) is adopted to train the neural network in the vibration suppression controller. The effectiveness of the proposed controller to track a given position and suppress vibration is demonstrated by numerical simulation.