Adaptive Neural Tracking Control of Full-state Constrained Nonstrict-feedback Time-delay Systems with Input Saturation

In this study, an adaptive neural backstepping control scheme is proposed for a class of nonstrict-feedback time-delay systems with input saturation, full-state constraints and unknown disturbances. A structural property of radial basis function neural network is presented to deal with the design from the nonstrict-feedback formation. This method does not require the parameter separation technique and its assumption. With the help of the Lyapunov-Krasovskii functionals and Young’s inequalities, the effects of time delays are compensated, and the unknown disturbances are eliminated in the design process. The barrier Lyapunov function (BLF) is applied to arrest the violation of the full-state constraints. To overcome the problem of input saturation nonlinearity, the smooth nonaffme function of the control input signal is adopted to approach the input saturation function. Moreover, an adaptive backstepping neural control strategy is proposed. The proposed adaptive neural controller ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the tracking error can converge to a small neighborhood of the origin. The simulation result shows the effectiveness of this method.