Adaptive neural network finite-time tracking quantized control for uncertain nonlinear systems with full-state constraints and applications to QUAVs

摘要

A novel adaptive neural network finite-time tracking control strategy is developed for a class of uncertain nonlinear systems with asymmetric time-varying full-state constraints and input quantization. With the help of an introduced TVABLF, all state variables are confined to predefined regions. Based on the back stepping method and NNs approximation technique, a smooth tracking controller and its adaptive laws are co-designed for the uncertain systems. The system effects caused by the input quantization is compensated by the proposed algorithm with nonlinear decomposition. By adopting the SGPFS theory, the tracking performances are ensured to be achieved in finite time. It is rigorously proved that the output of the system follows the specified trajectory in finite time, whilst the system state variables are constrained within asymmetric boundaries. QUAVs are typical nonlinear systems of 6-Dof, and the control input signal requires quantization. We apply the developed method to the controller design of uncertain QUAVs, and the simulation results verify the effectiveness of the main results.(c) 2020 Published by Elsevier B.V.