Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes

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• 作者：Biao Luo ; Huai-Ning Wu ; Han-Xiong Li
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2014
• 出版时间：May 14, 2014
• 年：2014
• 卷：53
• 期：19
• 页码：8106-8119
• 全文大小：576K
• 年卷期：v.53,no.19(May 14, 2014)
• ISSN：1520-5045

文摘

For many real complicated industrial processes, the accurate system model is often unavailable. In this paper, we consider the partially unknown spatially distributed processes (SDPs) which are described by general highly dissipative nonlinear partial differential equations (PDEs) and develop a data-based adaptive suboptimal neuro-control method by introducing the thought of reinforcement learning (RL). First, based on the empirical eigenfunctions computed with Karhunen鈥揕o猫ve decomposition, singular perturbation theory is used to derive a reduced-order model of an ordinary differential equation that represents the dominant dynamics of the SDP. Second, the Hamilton鈥揓acobi鈥揃ellman (HJB) approach is used for the suboptimal control design, and the thought of policy iteration (PI) is introduced for online learning of the solution of the HJB equation, and its convergence is established. Third, a neural network (NN) is employed to approximate the cost function in the PI procedure, and a NN weight tuning algorithm based on the gradient descent method is proposed. We prove that the developed online adaptive suboptimal neuro-controller can ensure that the original closed-loop PDE system is semiglobally uniformly ultimately bounded. Finally, the developed data-based control method is applied to a nonlinear diffusion-reaction process, and the achieved results demonstrate its effectiveness.