Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

IEEE Access

Juan Liu¹  Huachao Liu²  Zuojin Li²  Wei Zhou²  Jianyang Shi² 

[1] China Coal Technology and Engineering Group, Chongqing Research Institute, Chongqing, China;School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China;

关键词: Adaptive dynamic programming (ADP);    unscented Kalman filter (UKF);    data-based;    optimal control;    desulfurization;   

DOI  :  10.1109/ACCESS.2019.2949143

来源: DOAJ

【 摘 要 】

Desulfurization control of natural gas has long been a challenging industrial issue owing to its inherent difficulty in establishing accurate mathematical model for the nonlinear and strong coupling process. In this paper, a data-based adaptive dynamic programming (ADP) algorithm is presented to solve optimal control for natural gas desulfurization. First, neural network (NN) is used to reconstruct the dynamics of the desulfurization system via the input and output production data. Then, an improved unscented Kalman filter (IUKF) aided ADP method is presented to solve optimal control problem for desulfurization system, where IUKF algorithm is developed as a new weight-updating strategy for the action network and the critic network. The IUKF aided algorithm can improve the convergence speed as well as the anti-interference ability of the ADP controller. Furthermore, the proposed IUKF-ADP algorithm is implemented using the heuristic dynamic programming (HDP) structure. Finally, the effectiveness of the proposed IUKF-ADP algorithm is demonstrated through experiments of the natural gas desulfurization system.

【 授权许可】

Unknown