控制理论与应用2025，Vol.42Issue(9)：1838-1848,11.DOI:10.7641/CTA.2024.30355
基于长短时记忆神经网络的降压变换器自适应控制
Adaptive control for buck converter based on long short-term memory neural network
摘要
Abstract
The model-free control method based on deep reinforcement learning avoids the complex process of system modeling and addresses the challenges of nonlinear system control as well as captures excellent robustness.In this paper,a model-free adaptive control strategy is proposed for a DC-DC buck converter system with constant power load using long short-term memory neural network.Firstly,a state space composed of continuous voltage error signals is defined,transforming the error signals into input states for the control algorithm.Subsequently,a discrete action space is constructed based on the reference voltage,and a reward function is designed.The action space converts the algorithm's output into duty cycles,and a reward signal is assigned based on the controlled system's next-state evaluation to assess the algorithm's control effectiveness.The long short-term memory neural network serves as a state-action value function estimator for the double deep Q network,calculating the Q-values for various decisions under the input state and selecting the decision with the highest Q-value as the optimal output.Finally,simulation and experimental studies are conducted on the DC-DC buck converter system with a constant power load under the control of the proposed method.Experimental results demonstrate the excellent tracking performance of the control strategy,and in the presence of external disturbances,the system under this control strategy exhibits robust behavior.关键词
恒功率负载/直流降压变换器/长短时记忆神经网络/双深度Q网络/深度强化学习Key words
constant power load/DC-DC buck converter/long short-term memory neural network/double deep Q network/deep reinforcement learning引用本文复制引用
贺伟,严佳成,周旺平,李洪杰..基于长短时记忆神经网络的降压变换器自适应控制[J].控制理论与应用,2025,42(9):1838-1848,11.基金项目
国家自然科学基金项目(62373195,62173205,52077105,62073169)资助.Supported by the National Natural Science Foundation of China(62373195,62173205,52077105,62073169). （62373195,62173205,52077105,62073169）
