Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme
Yuan‐Xin Li, Ming Wei, Shaocheng Tong
Abstract
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteria are introduced with the aim to ensure that the tracking error enters into a small region around the origin in finite time. Finally, the stability of the closed-loop system is ensured via a fractional Lyapunov function theory and two simulation examples were used to prove the validity of the designed control scheme.
Topics & Concepts
BacksteppingControl theory (sociology)Nonlinear systemScheme (mathematics)Stability (learning theory)Adaptive controlLyapunov functionArtificial neural networkComputer scienceLyapunov stabilityEvent (particle physics)MathematicsControl (management)Artificial intelligencePhysicsMachine learningMathematical analysisQuantum mechanicsAdaptive Control of Nonlinear SystemsAdaptive Dynamic Programming ControlAdvanced Control Systems Design