Adaptive Learning Based Motor Control of an Unknown Robot Manipulator

This paper focuses on motor joint control using learning based Adaptive Dynamic Programming (ADP). The dynamics of a 2-link robot manipulator is presented. The system is regarded as a linear system and the optimal control is developed using linear quadratic regulator (LQR) and ADP methods. An off-line, off-policy controller iteration approach is used to find two optimal controllers, one for each motor joint of the robot. The optimal controller is developed to control the joint position without knowing the dynamic model. Simulation results show that two independent optimal linear control policies are learned simultaneously under a common exploration strategy, and the efficiency of ADP is verified.