Adaptive neural network control of nonholonomic systems with unknown virtual control coefficients

Abstract

In this paper, adaptive neural network control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The proposed adaptive neural network control proves that all the signals in the closed-loop system are uniformly ultimately bounded, and the systems states converge to a small neighborhood of zero. The adaptive neural network control laws are developed using state scaling and backstepping without a prior knowledge of the signs of the unknown virtual control coefficients. Nussbaum-type functions are used to solve the problem of the unknown control direction. The proposed adaptive neural network control is free of control singularity problem. Simulation results are provided to show the effectiveness of the proposed approach.