Limbic System-Inspired Robust Event-Driven Control for High-Order Uncertain Nonlinear Systems

Abstract

Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing data processing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.