Predictive Control of Voltage Source Inverter: An Online Reinforcement Learning Solution
Xing Liu, Lin Qiu, Youtong Fang, Kui Wang, Yunge Li, José Rodríguez
Abstract
The focus of this article is to introduce the concept of an online reinforcement learning (RL) solution and to propose a novel finite control-set model predictive control framework subject to system uncertainties, which possesses the excellent applicative potential for power converter systems with unknown perturbations. In this framework, the control task is performed by incorporating an adaptive neural network approximation-based RL and neural predictor-based predictive current control solution. To be more precise, a critic neural network is responsible for learning a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strategic</i> utility function online, and an actor network is developed to derive control behaviors by approximating the unknown model dynamics and optimizing the learned utility function obtained from the critic network. Compared to previous works, it not only attenuates the inherent issues of system uncertainties and unknown disturbances, but also provides a flexible framework and allows the enhancement of control property. Furthermore, by deploying the Lyapunov approach, it shows that all signals in the closed-loop system are uniformly ultimately bounded. Finally, numerical simulation and experiments validate our theoretical findings.