Adaptive Control for Nonlinear Pure-Feedback Systems Subject to Prescribed Performance

This article researches the adaptive control tactic of nonlinear pure-feedback systems with prescribed performance. Firstly, using the mean value theorem, the non-affine form of the system is adjusted to a display structure that can be studied by backstepping method. To deal with plant dynamics that are not known, neural networks (NNs) are employed. Then, an adaptive design scheme based on NNs is created by fusing the backstepping technique with the Lyapunov stability theory. Based on the presented design tactic, the boundedness of system variables is guaranteed, and the output error is driven within the prescribed function all the time. Finally, the effectiveness of the designed strategy is substantiated via simulation images.