Meta-Self-Referential Adaptive Control (Meta-SRAC): Resolving the Robustness-Precision Dilemma via State-Dependent Meta-Regularization

Abstract

This work introduces the Meta-Self-Referential Adaptive Control (Meta-SRAC) framework, addressing the long-standing robustness–precision trade-off in adaptive control systems. Unlike classical approaches relying on fixed leakage parameters, the proposed method introduces a state-dependent meta-regulation mechanism that dynamically adjusts adaptation damping based on the internal energy of the adaptive parameters. A composite Lyapunov framework is developed to rigorously establish uniform ultimate boundedness (UUB) of all closed-loop signals. Extensive simulations demonstrate that Meta-SRAC preserves high steady-state precision while ensuring parameter boundedness, reducing actuator stress by 22–48%, and improving transient safety margins. This work provides a principled pathway toward self-regulated adaptive systems capable of simultaneously achieving robustness, precision, and efficiency.