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

摘要

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-based memoryless 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. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.