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Event-triggered adaptive iterative learning prescribed performance control of high-order strict feedback nonlinear

Leyan Fang1, Mingzhe Hou1, Xindi Xu1

  • 1Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.

ISA Transactions
|November 27, 2025
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Summary

This study introduces an event-triggered adaptive iterative learning prescribed performance control (PPC) for nonlinear systems, improving tracking performance under communication constraints. The novel approach ensures bounded errors and enhanced parameter estimation for robust control.

Keywords:
Adaptive iterative learning controlEvent-triggered controlHigh-order strict feedback nonlinear systemsParameter estimationPrescribed performance control

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Area of Science:

  • Control Engineering
  • Nonlinear Systems Theory
  • Robotics

Background:

  • Existing adaptive iterative learning control methods often use first-order systems and ignore network communication limits.
  • High-order strict feedback nonlinear systems present challenges due to uncertainties and complex dynamics.

Purpose of the Study:

  • To develop an event-triggered adaptive iterative learning prescribed performance control (PPC) for uncertain high-order strict feedback nonlinear systems.
  • To address limitations of existing methods by incorporating network communication constraints and improving control performance.

Main Methods:

  • Designed an event-triggered adaptive iterative learning PPC algorithm using nonlinear mapping and dynamic surface control.
  • Employed an improved parameter estimation error reconstruction mechanism and a projection operator.
  • Constructed a preset error trajectory within a performance envelope to manage initial conditions and tracking errors.

Main Results:

  • Proved uniform ultimate boundedness of tracking error and parameter estimation errors.
  • Demonstrated that tracking error satisfies prescribed transient performance in each iteration.
  • Validated the approach via simulations on a single-link flexible-joint robot.

Conclusions:

  • The proposed event-triggered adaptive iterative learning PPC is effective for uncertain high-order nonlinear systems.
  • The method enhances parameter estimation and avoids control law singularity.
  • Successfully addresses network communication constraints while ensuring prescribed performance.