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Event-triggered adaptive control for nonlinear systems using time-receding horizons without initial dependence.

Zhiwei Hao1, Xiaokui Yue2, Zheng Wang3

  • 1National Key Laboratory of Aerospace Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China; Department of Electrical and Computer Engineering, National University of Singapore, 117576, Singapore.

ISA Transactions
|January 20, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces event-triggered adaptive control for uncertain systems, overcoming complexity and reducing data transmission. The new method ensures stability within performance constraints, even with unknown initial conditions.

Keywords:
Adaptive controlEvent-triggered mechanismNonlinear systemPractically finite-time stabilityTime-receding horizons

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Systems Theory
  • Adaptive Control

Background:

  • Backstepping design in uncertain strict-feedback systems is complicated by unknown coupling dynamics of virtual variables.
  • Existing methods often struggle with performance constraints, initial condition dependence, and high data transfer costs.

Purpose of the Study:

  • To develop a full-state constraint event-triggered adaptive control strategy for uncertain strict-feedback systems.
  • To address challenges in shaping performance constraints, eliminating initial dependence, and reducing data transmission.

Main Methods:

  • Constructed a time-receding horizon (TRH) integrated with a quadratic Lyapunov function (QLF) for constrained tracking.
  • Designed a tuning function to manage arbitrary system initial conditions and performance constraints.
  • Developed a dual-channel event-triggered mechanism (ETM) for efficient controller and estimator updates.
  • Utilized a specific inequality with backstepping to manage system uncertainties without recursive complexity explosion.

Main Results:

  • Achieved practical finite-time stability (PFS) by ensuring system stabilization within constrained regions before a prescribed time.
  • Successfully relaxed initial condition dependence through the designed tuning function.
  • Demonstrated reduced data transmission burden via the event-triggered mechanism.
  • Validated the method's effectiveness in overcoming uncertainties without complexity explosion.

Conclusions:

  • The proposed event-triggered adaptive control method effectively handles full-state constraints in uncertain strict-feedback systems.
  • The approach successfully mitigates initial condition dependence and reduces data transmission requirements.
  • Simulation results confirm the practical finite-time stability and overall effectiveness of the developed control strategy.