Quantized Input Robust Adaptive Neural Network Control for Nonlinear Systems With Full State Constraints

In this article, a novel robust adaptive neural network tracking control scheme is presented for a class of uncertain nonlinear systems with quantized inputs and full state constraints. A tan-type barrier Lyaponov function is proposed to constrain all states, and the unknown nonlinear function term in virtual control is approximated by radial basis function neural network(RBFNN). The uncertainty term and disturbance term in the system are dealt with by robust scheme. Under the proposed quantized tracking control scheme, the communication load of the system is reduced, the boundedness of all signals in the closed-loop system is verified, the full state constraints are satisfied. Simulation results are presented to illustrate the effectiveness of the proposed adaptive control scheme.