Data-Based Optimal Tracking Control for Natural Gas Desulfurization System

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.