Adaptive neural network control for a class of non-strict-feedback nonlinear systems

This paper focuses on the problem of adaptive neural network control for a class of nonlinear systems in non-strict-feedback form in the presence of bounded disturbances. Based on the monotonically increasing property of the system bounding functions, a variable separation approach is proposed. By this approach, a state observer-based adaptive neural networks outputfeedback control scheme is presented for a class of nonlinear non-strict-feedback systems. It is shown that the proposed controller can guarantee semi-global boundedness of all the signals in the closed-loop systems and the output of the system converges to a small neighborhood of zero with appropriate design parameters. Simulation result is used to illustrate the effectiveness of the proposed method.