Neural-network-based optimal tracking control scheme for a class of unknown discrete-time nonlinear systems using iterative ADP algorithm

In this paper, an optimal tracking control scheme is proposed for a class of unknown discrete-time nonlinear systems using iterative adaptive dynamic programming (ADP) algorithm. First, in order to obtain the dynamics of the system, an identifier is constructed by a three-layer feedforward neural network (NN). Second, a feedforward neuro-controller is designed to get the desired control input of the system. Third, via system transformation, the original tracking problem is transformed into a regulation problem with respect to the state tracking error. Then, the iterative ADP algorithm based on heuristic dynamic programming is introduced to deal with the regulation problem with convergence analysis. In this scheme, feedforward NNs are used as parametric structures for facilitating the implementation of the iterative algorithm. Finally, simulation results are also presented to demonstrate the effectiveness of the proposed scheme.