AbstractQ-learning algorithm is used to solve the optimal stabilization control problem while only the data, rather than the model of the plant, is available. Due to the continuity of state space and control space, Q-learning can only be implemented in an approximate manner. Therefore, the proposed approximate Q-learning algorithm can obtain only one suboptimal controller. Although the obtained controller is suboptimal, the simulation shows that the closed-loop domain of attraction of the proposed algorithm is broader and the cost function is also smaller than the linear quadratic regulator and deep deterministic policy gradient method for the strongly nonlinear plant.
Fund:Supported by National Natural Science Foundation of China(No.61703369), Zhejiang Key Research and Development Program(No.2017C03039), Major Science and Technology Project of Wenzhou(No.ZS2017007)
About author:: LU Chaolun, Ph.D. candidate. His research interests include data-driven control and reinforcement learning.LI Yongqiang, Ph.D., lecturer. His research interests include data-driven control and optimal control.FENG Yuanjing(Corresponding author), Ph.D., professor. His research interests include image processing and intelligent optimization.
LU Chaolun,LI Yongqiang,FENG Yuanjing. Data Driven Optimal Stabilization Control and Simulation Based on Reinforcement Learning[J]. , 2019, 32(4): 345-352.
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