Adaptive NNs asymptotic tracking control for high-order nonlinear systems under prescribed performance and asymmetric output constraints

This article studies the adaptive neural networks (NNs) asymptotic tracking control of high-order nonlinear systems subject to prescribed performance, non-strict-feedback structure, and output constraints. To address the output constraint issue while guaranteeing that the tracking error stays within the specified area, a variable fused with the time-varying constraint functions is introduced. Then, a pivotal form of coordinate transformation is developed, which plays a key role in achieving asymptotic tracking performance. Based on the backstepping and Lyapunov method, the designed control scheme assures that all system variables are semi-globally uniformly ultimately bounded, the output constraints are never broken, and the tracking error always stays within the predefined function and asymptotically converges to zero. Finally, the effectiveness of theoretical findings is verified via simulation studies.