Control by interconnection of a manipulator arm using reinforcement learning

Control by interconnection (CbI) is a dynamic output-feedback approach used to control port-Hamiltonian (PH) systems. Here, both the plant and the controller are modelled in PH form, in terms of their own Hamiltonians. However, obtaining an appropriate controller Hamiltonian is generally difficult. In this paper, we address this issue by using reinforcement learning (RL). Additionally due to the semi-supervised optimization nature of the RL algorithms, a performance criterion can be readily included in CbI. We demonstrate the usefulness of the proposed learning algorithm for stabilization of a manipulator arm.