Finite-Set Direct Torque Control via Edge-Computing-Assisted Safe Reinforcement Learning for a Permanent-Magnet Synchronous Motor

Abstract

Advances in the field of reinforcement learning (RL)-based drive control allow formulation of holistic optimization goals for the data-driven training phase. The resulting controllers feature efficient drive operation without the necessity of an a priori known plant model but, so far, conduction of the corresponding training phase in real-world drive systems has been applied only sparsely due to safety concerns. This contribution targets the challenging problem of self-learning torque control for a permanent-magnet synchronous motor assuming a finite control set, i.e., the direct selection of switching actions instead of a modulator-based setup. In order to allow a secure and effective online training with real-world drive systems, the RL controller is monitored by a safeguarding algorithm that prevents application of unsafe switching actions, e.g., such that result in overcurrent. The accruing amount of measurement data is handled with the use of an edge-computing pipeline to outsource the RL training from the embedded control hardware. The inference of the utilized artificial neural network in hard real time is realized with the use of a reconfigurable field-programmable gate array architecture. The resulting RL-based algorithm is able to learn a torque control policy in just 10 min, which has been validated during comprehensive real-world experiments.