Robust adaptive neural control for strict-feedback nonlinear systems

Abstract

A novel robust adaptive neural control approach is presented for a general class of strict-feedback nonlinear systems with both nonlinear uncertainties and virtual control gain nonlinearities that may not be linearly parameterized. A unified and systematic procedure is employed to derive a robust adaptive tracking controller by using of the backstepping technique and radial basis function neural networks (RBFNN). The outstanding feature of the algorithm is that it has much fewer learning parameters than that of existing backstepping method combing with adaptive neural networks, such that computation load can be reduced obviously. Semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop nonlinear system is achieved. The output of the system is proven to converge to a small neighborhood of the desired trajectories by Lyapunov approach. Two examples are used to demonstrate the effectiveness and performance of the proposed approaches.