Near-Optimal Control of Motor Drives via Approximate Dynamic Programming

Data-driven methods for learning near-optimal control policies through approximate dynamic programming (ADP) have garnered widespread attention. In this paper, we investigate how data-driven control methods can be leveraged to imbue near-optimal performance in a core component in modern factory systems: the electric motor drive. We apply policy iteration-based ADP to an induction motor model in order to construct a state feedback control policy for a given cost functional. Approximate error convergence properties of policy iteration methods imply that the learned control policy is near-optimal. We demonstrate that carefully selecting a cost functional and initial control policy yields a near-optimal control policy that outperforms both a baseline nonlinear control policy based on backstepping, as well as the initial control policy.