Adaptive control for buck converter based on long short-term memory neural network
HE Wei
YAN Jia-cheng
ZHOU Wang-ping
LI Hong-jie
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.
Keywords：constant power loadDC-DC buck converterlong short-term memory neural networkdouble deep Q networkdeep reinforcement learning
Publication Date：2025-09-30
Online Publishing Date：2025-10-28(First online date of this platform, not the publication date of the document)
Pages：11( 1838-1848 )