Composite adaptive NN learning and control for discrete-time nonlinear uncertain systems in normal form

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This paper addresses the problem of composite adaptive learning and tracking control for discrete-time nonlinear uncertain systems in general normal form. To deal with the system's unstructured nonlinear uncertain dynamics, novel adaptive neural network (NN) learning control strategies are proposed by extending methodologies from the continuous-time deterministic learning theory. Both state-feedback and output-feedback cases are considered. The proposed control schemes are compelling in the sense that (i) they are capable of rendering not only stable tracking control, but also locally-accurate learning/identification of unknown system dynamics during stable closed-loop control; (ii) the learned knowledge can be effectively represented and stored as constant NN models, whose weights are guaranteed to partially converge to ideal/optimal values. Based on this, experience-based controllers are also developed to pursue improved tracking control performance without online adaptation. In particular for the output-feedback control case, a new observer-less adaptive NN learning control scheme is proposed without resorting to high-gain observers, followed by an observer-less output-feedback experience-based controller. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed approaches.

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