Reinforcement learning approach to the control of heavy material handling manipulators for agricultural robots

Abstract

In this paper, we consider the optimal control problem of heavy material handling manipulators for agricultural robots. Unlike the existing results on agricultural robots, the robot parameters may be unknown for the designer in this paper. To learn the linear quadratic control gain under unknown robot parameters, two reinforcement learning algorithms, i.e., policy iteration (PI) algorithm and value iteration (VI) algorithm, are proposed. Then, through combining the advantages of PI algorithm and VI algorithm, i.e., satisfactory convergence rate and without the restriction on feasibility initial control policy, respectively, a hybrid iteration (HI) algorithm is proposed, which can both achieve a satisfactory convergence rate and remove restrictions on feasibility initial control policy. It is shown that the convergence of the proposed HI algorithm can be achieved in theory. Finally, a simulation example is given to show that our designed HI algorithm can achieve a satisfactory simulation time.