Neural-networks-based adaptive quantized feedback tracking of uncertain nonlinear strict-feedback systems with unknown time delays

초록

We develop a quantized-feedback-based adaptive delay-independent control design for systems with unknown strict-feedback nonlinearities and time-varying delays. It is assumed that full state variables quantized by uniform quantizers are only available for the feedback control design. Compared with the previous adaptive control designs of lower-triangular nonlinear time-delay systems, the major contribution of this paper is to develop quantized-states-basedmemoryless adaptive control and stability analysis strategies to deal with unmatched and unknown time-delay nonlinearities. An adaptive neural network controller and its adaptive laws are designed via quantized state variables where neural networks are employed to compensate for unknown time-delay nonlinear effects. By deriving theoretical lemmas on the boundedness of quantization errors of the closed-loop signals, the stability of the resulting closed-loop system and the convergence of the tracking error are analyzed. Finally, simulation results are provided to validate the effectiveness of the theoretical result. © 2020 The Franklin Institute

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