Adaptive Iterative Learning Fault-Tolerant Control for High-Order Non-Strict Feedback Nonlinear Systems with Prescribed Performance

This study explores the issue of devising an adaptive iterative learning fault-tolerance control approach for high-order nonlinear systems with prescribed performance in non-strict feedback form is investigated. By establishing an iterative update law under the same initial conditions, the actuator fault is compensated. To address the challenges posed by unknown nonlinear functions, the use of fuzzy logic systems for approximation. Additionally, the error transformation technique is designed on the adaptive robust fault-tolerant controller, which aims to secure the convergence of the output tracking error towards a prescribed performance metric. Ultimately, by employing Lyapunov stability analysis, the tracking error converges towards a small range near zero as the iteration progresses.