A Predictive Control Method Based on Neural Predictor and Soft Actor–Critic for Power Converters

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Abstract
This article focuses on introducing soft reinforcement learning (RL) techniques into the finite controlset model predictive control (FCS-MPC) framework to enhance robust performance. More precisely, building upon a neural predictor, an intelligent agent trained using the soft actor–critic algorithm is developed to explore the optimal control input embedded within the MPC framework. Meanwhile, during training, a constraint based on a Lyapunov function is introduced, and a corresponding update law for the weights is provided. Furthermore, this proposed method guarantees the stability of the system integrated with the RL intelligent agent. Finally, both simulation and experimental results validate the superiority of this approach compared to the existing FCS-MPC methods.
Funding Information
  • Key R&D Plan Projects at Zhejiang Province (2023C01243)
  • Joint Science Foundation of Education Ministry of China (8091B022201)
  • State Key Laboratory of High-speed Maglev Transportation Technology (SKLM-SFCF-2023-020)

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