Data-driven learning-based control methods, such as reinforcement learning (RL), have become increasingly popular with recent proliferation of the machine learning paradigm. These methods address the parameter sensitiveness and unmodeled dynamics in model-based controllers, such as finite control-set model predictive control. RL agents are typically utilized in simulation environments, where they are allowed to explore multiple "unsafe" actions during the learning process. However, this type of learning is not applicable to online self-learning of controllers in physical power converters, because unsafe actions would damage them. To address this, this letter proposes a safe online RL-based control framework to autonomously find the optimal switching strategy for the power converters, while ensuring system safety during the entire self-learning process. The proposed safe online RL-based control is validated in a practical testbed on a two-level voltage source converter system, and the results confirm the effectiveness of the proposed method.

Yihao Wan

Qianwen Xu

Tomislav Dragičević

IEEE Transactions on Industrial Electronics

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 每月出版一次。其范围包括电子、控制和通信、仪器仪表和计算智能的应用，以增强工业和制造系统和流程。包括电力电子和驱动控制技术、系统控制和信号处理、故障检测和诊断、电力系统、仪器仪表、测量和测试、建模和仿真、运动控制、机器人、传感器和执行器、神经网络的实现、模糊逻辑、工业系统、工厂自动化、通信和计算机网络中的人工智能。

The IEEE Transactions on Industrial Electronics is a monthly journal that covers the application of electronics, control and communication, instrumentation, and computational intelligence to enhance industrial and manufacturing systems and processes. The journal includes topics such as power electronics and drive control technologies, system control and signal processing, fault detection and diagnosis, power systems, instrumentation, measurement and testing, modeling and simulation, motion control, robotics, sensors and actuators, implementation of neural networks, fuzzy logic, industrial systems, factory automation, and artificial intelligence in communication and computer networks.

《IEEE Transactions on Industrial Electronics》是一本每月出版的期刊，涵盖电子、控制与通信、仪器仪表及计算智能在工业和制造系统中的应用。期刊内容包括电力电子与驱动控制技术、系统控制与信号处理、故障检测与诊断、电力系统、仪器仪表、测量与测试、建模与仿真、运动控制、机器人、传感器与执行器、神经网络实现、模糊逻辑、工业系统、工厂自动化以及通信与计算机网络中的人工智能等领域。

Category	Quartile
工程技术	1
工程技术, 自动化与控制系统	1
工程技术, 工程电子与电气	1
工程技术, 仪器仪表	1

Safety-Enhanced Self-Learning for Optimal Power Converter Control

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Journal Info

Category	Quartile
INSTRUMENTS & INSTRUMENTATION	1