Abstract:

This paper investigates the problem concerning tracking control for a class of single input and single output uncertain strict-feedback nonlinear systems, under the condition of the input dead-zone and output constraint with unknown time delays. Firstly, a state observer is established to estimate the unmeasured states. Secondly, radial basis function neural networks are used to approximate the unknown nonlinearity; a barrier Lyapunov function is designed to ensure that the output parameters are restricted and the effects of unknown time-delays are eliminated by choosing appropriate Lyapunov-Krasovskii functions in the design procedure. Finally, based on Lyapunov stability theory, a robust adaptive neural network output feedback controller is constructed. The designed controller overcomes the problem of over parameterization. It is shown that the designed control scheme can ensure that all the closed-loop signals are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin. Two examples are provided to further show the effectiveness of the proposed approach.