Neuro-optimal learning control scheme for gasification process with unknown system model

In this paper, a new iterative optimal learning control scheme for discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) approach is developed to obtain the optimal control law for coal gasification control systems. For the unknown coal gasification process, neural networks (NNs) are introduced to reconstruct the dynamics of the gasification process, where the approximation errors of the reconstruction dynamics are considered. Via system transformation, the optimal tracking control problem with approximation errors is transformed into a two-person zero-sum optimal control problem. A new iterative ADP algorithm is then developed to obtain the optimal control law for the transformed system with convergence analysis. Finally, numerical results are given to illustrate the performance of the present method.