AI-Guided Adaptive Control Framework for Nonlinear Systems Under Uncertainty

Abstract

This paper proposes an AI-guided adaptive control framework for nonlinear systems operating under external disturbances, measurement noise, and parametric uncertainties. Unlike conventional adaptive controllers that rely solely on instantaneous tracking error or predefined rule-based mechanisms, the proposed approach integrates artificial intelligence–based awareness to evaluate the dynamic condition of the system in real time. The AI module analyzes the tracking error behavior and system response patterns to generate adaptive signals that regulate the controller parameters in a smooth and bounded manner.

The adaptive control law is designed to preserve the simplicity and reliability of classical control structures while enhancing robustness and transient performance. A Lyapunov-based stability analysis is developed to guarantee boundedness of all closed-loop signals and asymptotic convergence of the tracking error. The effectiveness of the proposed AI-guided controller is validated through numerical simulations on representative nonlinear benchmark systems. Simulation results demonstrate improved settling time, reduced overshoot, enhanced disturbance rejection, and smoother control effort compared to conventional PID and rule-based adaptive controllers.

A novel entropy-based awareness metric is introduced to quantify system uncertainty and modulate adaptation intensity accordingly.