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Event-triggered adaptive tracking control for uncertain nonlinear systems based on a new funnel function.

Cungen Liu1, Xiaoping Liu2, Huanqing Wang3

  • 1School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, PR China.

ISA Transactions
|September 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive event-triggered funnel controller for nonlinear systems with unknown parameters. It ensures bounded signals and faster tracking error convergence while avoiding Zeno behavior.

Keywords:
Adaptive controlEvent-triggered controlFunnel controlNonlinear system

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Area of Science:

  • Control Theory
  • Nonlinear Systems
  • Adaptive Control

Background:

  • Event-triggered control reduces system resource usage.
  • Funnel control ensures bounded tracking errors.
  • Strict-feedback nonlinear systems present significant control challenges, especially with unknown parameters.

Purpose of the Study:

  • To develop an adaptive event-triggered funnel controller for strict-feedback nonlinear systems.
  • To address unknown system parameters and optimize tracking error convergence.
  • To guarantee signal boundedness and avoid Zeno behavior.

Main Methods:

  • Proposed a novel adjustable funnel function with online parameter adjustment based on tracking error.
  • Designed an adaptive event-triggered funnel controller.
  • Utilized Lyapunov stability analysis to prove system boundedness.

Main Results:

  • All signals in the closed-loop system are guaranteed to be bounded.
  • The output tracking error converges faster and remains within an adjustable funnel.
  • Zeno behavior (infinite control actions in finite time) is successfully avoided.
  • Simulation results validated the controller's effectiveness.

Conclusions:

  • The developed adaptive event-triggered funnel controller is effective for strict-feedback nonlinear systems with unknown parameters.
  • The adjustable funnel function allows for dynamic error management and faster convergence.
  • This approach offers a resource-efficient and robust control solution.