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Updated: Sep 9, 2025

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Event-triggered optimal control for modular reconfigurable manipulators with input constraints based on model

Fan Zhou1, Yifan Zhang1, Tianhao Ma2

  • 1School of Electrical and Electronic Engineering, Changchun University of Technology, 130012, Changchun, China.

ISA Transactions
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an event-triggered optimal control for modular reconfigurable manipulators (MRMs) using model predictive control (MPC). The method enhances performance and robustness while ensuring safety through torque constraints and adaptive dynamic programming.

Keywords:
Adaptive dynamic programmingEvent-triggered controlModel predictive controlModular reconfigurable manipulators

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence in Automation
  • Advanced Control Theory

Background:

  • Modular Reconfigurable Manipulators (MRMs) present complex control challenges due to their adaptable structures.
  • Existing control methods often struggle with decentralized coordination and robustness against model uncertainties.
  • Ensuring safety through input constraints is critical for practical MRM applications.

Purpose of the Study:

  • To develop an event-triggered optimal control strategy for MRMs.
  • To enhance system performance, robustness, and safety.
  • To address the challenges of decentralized control and model inaccuracies in MRMs.

Main Methods:

  • A decentralized Model Predictive Control (MPC) approach decomposes MRM control into module-specific tasks coordinated by a global framework.
  • Hyperbolic tangent functions are employed for input torque constraints to prevent safety hazards.
  • Adaptive Dynamic Programming (ADP) is integrated with MPC to improve robustness against modeling errors.
  • A critical neural network (NN) is utilized to solve the Hamilton-Jacobi-Bellman (HJB) equation for optimal control solutions.
  • Lyapunov stability theory is applied to guarantee uniform ultimate boundedness (UUB) of trajectory tracking errors.

Main Results:

  • The proposed event-triggered MPC method significantly reduces trajectory tracking errors in MRMs.
  • Resource consumption is minimized through the efficient, decentralized control strategy.
  • Constrained torque capabilities are enhanced, improving operational safety.
  • The integration of ADP and NN demonstrates improved system robustness.

Conclusions:

  • The developed event-triggered optimal control method offers a robust and efficient solution for MRMs.
  • The approach effectively balances performance, safety, and adaptability in complex robotic systems.
  • This work advances the state-of-the-art in control for modular and reconfigurable robotic platforms.