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Fixed-time learning-based optimal tracking control for robotic systems with prescribed performance constraints.

Zhinan Peng1, Xingyu Zhang1, Zhuo Xia1

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel control framework for robotic systems, ensuring optimal tracking and performance constraints. It utilizes fixed-time learning and dynamic event-triggered control to enhance efficiency and stability.

Keywords:
Adaptive dynamic programmingDynamic event-triggeredFixed-time convergenceOptimal tracking controlPrescribed performance constraints

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Area of Science:

  • Robotics
  • Control Theory
  • Machine Learning

Background:

  • Robotic systems often face performance constraints due to structural and task requirements.
  • Ensuring optimal tracking control under these constraints is a significant challenge.

Purpose of the Study:

  • To develop a fixed-time learning-based dynamic event-triggered control framework for optimal tracking in robotic systems.
  • To address prescribed performance constraints in robotic system states.

Main Methods:

  • Prescribed Performance Control (PPC) theory to manage state constraints.
  • Critic-only Adaptive Dynamic Programming (ADP) for optimal control law approximation.
  • Fixed-Time Convergence (FTC) weight update law with Concurrent Learning (CL) for neural network (NN) weight estimation.
  • Dynamic event-triggered mechanism to minimize sampling and computation.

Main Results:

  • Guaranteed fixed-time convergence of weight estimation error under relaxed persistent excitation.
  • Demonstrated stability of the closed-loop system.
  • Reduced sampling instances and computational load through the event-triggered mechanism.

Conclusions:

  • The proposed framework effectively achieves optimal tracking control for robotic systems with prescribed performance constraints.
  • The method enhances control system efficiency and stability.
  • Simulation results validate the effectiveness and superiority of the proposed approach.