Adaptive neural control of uncertain nonholonomic systems with unknown virtual control coefficients
虚拟控制系数未知的非完整系统的自适应神经网络控制

An adaptive neural network control is applied to nonholonomic systems in chain form, with unknown virtual control coefficients and strong drift nonlinearities. The Backstepping technique and state-scaling are employed in designing the adaptive neural network control laws. Nussbaum-type functions are used to solve the problem of the completely unknown control direction. The uniform ultimate boundedness of all signals in the closed-loop is guaranteed; and the systems states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is achieved by appropriately choosing the design parameters. The proposed adaptive neural network control is free from the control singularity problem. An adaptive control-based switching strategy is used to overcome the uncontrollability problem associated with x0(t0) = 0. Simulation results are provided to show the effectiveness of the proposed approach.