Adaptive Dynamic Programming for Optimal Control of Coal Gasification Process

Abstract

In this chapter, a coal gasification optimal tracking control problem is solved through a data-based optimal learning control scheme using iterative adaptive dynamic programming (ADP) approach. According to the system data, neural networks (NNs) are used to construct the dynamics of coal gasification process , the coal quality function, and the reference control, respectively, where the mathematical model of the system is unnecessary. The approximation errors from NN construction of the disturbance and the controls are both considered. Via system transformation, the optimal tracking control problem with approximation errors and disturbances is effectively transformed into a two-person zero-sum game. An iterative ADP algorithm is then developed to obtain the optimal control laws for the transformed system. Convergence property is developed to guarantee that the cost function converges to a finite neighborhood of the optimal cost function, and the convergence criterion is also obtained. Finally, numerical results are given to illustrate the performance of the present method.