Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

In this paper, we present an adaptive neuro-fuzzy controller design for a class of uncertain nonholonomicsystems in the perturbed chained form with unknown virtual control coefficients and strong drift nonlinearities. The robustadaptive neuro-fuzzy control laws are developed using state scaling and backstepping. Semiglobal uniform ultimate boundednessof all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhoodof zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the designparameters. By using fuzzy logic approximation, the proposed control is free of control singularity problem. An adaptivecontrol-based switching strategy is proposed to overcome the uncontrollability problem associated with x0(t0) = 0.