Steady-State Error Reduction of Reinforcement Learning based Indirect Current Control of Permanent Magnet Synchronous Machines

Abstract

Deep reinforcement learning (DRL) can achieve favorable dynamic performance compared to conventional control methods. However, steady-state errors are often present. This paper investigates the reduction of steady-state error in DRL-based current control of permanent magnet synchronous machines (PMSMs) by augmenting the integrated tracking error to the observation vector. More specifically, this paper assesses the performance of a DRL-based method under nominal and adverse operating conditions by considering PMSMs with linear and nonlinear magnetic circuits, which exhibit saturation, cross-coupling, and spatial harmonics. The latter include parameter mismatches between the training model and the physical system and misalignment of the dq-frame with respect to the identified position of the d-axis. As shown with the presented experimental results, the DRL-based control method can successfully operate the drive system under all operating conditions, with the steady-state and dynamic performance being similar to that of field-oriented control.