Optimal Torque Control of Permanent Magnet Synchronous Motors Using Adaptive Dynamic Programming

TL;DR

This paper introduces an adaptive dynamic programming-based control method for PMSMs that improves torque and speed regulation under uncertain conditions, outperforming traditional control methods in simulations and experiments.

Contribution

The paper presents a novel ADP-based control approach for PMSMs that uses neural networks trained offline for real-time optimal torque control, handling uncertainties effectively.

Findings

ADP achieves faster transient response than FOC.

ADP reduces torque ripples and steady-state error.

ADP outperforms FOC under parameter uncertainties.

Abstract

In this study, a new approach based on adaptive dynamic programming (ADP) is proposed to control permanent magnet synchronous motors (PMSMs). The objective of this paper is to control the torque and consequently the speed of a PMSM when an unknown load torque is applied to it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilized to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field oriented control (FOC) and direct torque control based on space vector modulation (DTC-SVM). Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modeled dynamics. Although the performance of FOC method is comparable with ADP under nominal conditions, the torque and speed response of ADP is better than FOC under realistic scenarios, that is, when parameter uncertainties exist.