Affiliation:
1. School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China
2. College of Sciences Shandong University of Aeronautics Binzhou Shandong China
Abstract
AbstractThis article is to develop an optimized leader‐follower consensus control for multiple robot manipulator system by combining sliding mode control (SMC) and reinforcement learning (RL). The SMC mechanism aims to steer both position and velocity states of multiple manipulator reaching the predefined trajectory. And the RL is designed and performed under identifier‐critic‐actor architecture for the achievement of optimized control performance. Compared to traditional optimal control, this proposed method has two main advantages: (i) the RL updating laws for training both the actor and critic networks are simpler; (ii) the optimized control can not require the complete dynamic knowledge because the adaptive identifier is designed into the RL learning. Consequently, this optimized control method can smoothly steer the multiple robot manipulator system to achieve the leader‐follower consensus. Finally, feasibility of this control method is verified through both theory and simulation.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shandong Province