Direct Adaptive Control of a Flexible Robot using Reinforcement Learning

Abstract

This paper proposes a new adaptive control using the concept of reinforcement learning to address adaptivity for varied payload conditions for a two-link flexible manipulator (TLFM). The application of reinforcement learning has been implemented using a method called adaptive dynamic programming. Decentralized controllers for the decoupled system have been also designed using LQR technique. Then the reinforcement learning is used to tune the gains of the optimal control to adapt in terms of different payload to the manipulator end effecter. Simulation results show that proposed controller provides better end point tracking then LQR fixed gain controller.