Xue Shi, Nan Chen, Ting-Yu Wei, Jiayu Wu, Peilei Xiao
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引用次数: 2
Abstract
A controller for DC-DC switching converters based on solely AI algorithm is proposed with a simpler structure than the traditional neural network-PID controllers. Reinforcement learning is used to train the AI controller online using deep deterministic policy gradient (DDPG) algorithm. The AI controller with an actor-critical architecture realizes model-free control with strong self-adaptive ability for different control objects, which can be used for different types of DC-DC switching converters. The performance of a buck DC-DC switching converter with the AI controller is compared with a neural network-PID controller through simulation. The simulation results show that the settling time is improved by at least 65% and overshoot/undershoot is decreased by at least 43%.
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