PID控制器
控制理论(社会学)
强化学习
计算机科学
控制器(灌溉)
人工神经网络
控制工程
自适应控制
人工智能
作者
Xuesong Wang,Yuhu Cheng,Wei Sun
出处
期刊:Journal of China University of Mining and Technology
[Elsevier]
日期:2007-03-01
卷期号:17 (1): 40-44
被引量:40
标识
DOI:10.1016/s1006-1266(07)60009-1
摘要
Abstract Aimed at the lack of self-tuning PID parameters in conventional PID controllers, the structure and learning algorithm of an adaptive PID controller based on reinforcement learning were proposed. Actor-Critic learning was used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of re-inforcement learning effectively. In order to reduce the demand of storage space and to improve the learning efficiency, a single RBF neural network was used to approximate the policy function of Actor and the value function of Critic si-multaneously. The inputs of RBF network are the system error, as well as the first and the second-order differences of error. The Actor can realize the mapping from the system state to PID parameters, while the Critic evaluates the outputs of the Actor and produces TD error. Based on TD error performance index and gradient descent method, the updating rules of RBF kernel function and network weights were given. Simulation results show that the proposed controller is efficient for complex nonlinear systems and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.
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