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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler-Lagrange

He Li1, Cheng-Lin Liu1, Ya Zhang2

  • 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, PR China.

ISA Transactions
|January 7, 2022
PubMed
Summary

This study presents a fixed-time fault-tolerant consensus tracking method for uncertain multiple Euler-Lagrange systems. The approach ensures system stability and accurate tracking despite actuator faults and uncertainties.

Keywords:
Adaptive controlDistributed observerFixed-time consensus trackingMultiple Euler–Lagrange systemsRadical basic function neural networks

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Area of Science:

  • Robotics and Control Systems
  • Nonlinear Control Theory
  • Fault-Tolerant Control

Background:

  • Multiple Euler-Lagrange Systems (MELS) are complex nonlinear systems requiring robust control strategies.
  • Achieving consensus tracking in MELS is challenging due to system uncertainties and potential actuator faults.
  • Existing methods may not guarantee convergence within a fixed-time or effectively handle fault tolerance.

Purpose of the Study:

  • To address the fixed-time fault-tolerant consensus tracking (FTCT) problem for uncertain MELS.
  • To develop a control strategy that ensures fast and reliable state estimation and tracking.
  • To handle actuator faults and system uncertainties within a fixed-time framework.

Main Methods:

  • Design of a fixed-time distributed observer (DO) for leader state estimation.
  • Utilization of radical basis function neural networks (RBFNN) for approximating system uncertainties.
  • Application of backstepping technique to design a fault-tolerant local control protocol (FTLCP) and update laws.

Main Results:

  • The designed observer and control protocol ensure fixed-time convergence of error variables to a small neighborhood of zero.
  • The RBFNN effectively compensates for system uncertainties.
  • The fault-tolerant strategy successfully mitigates the impact of actuator faults.

Conclusions:

  • The proposed FTCT method provides a robust and efficient solution for uncertain MELS.
  • The approach guarantees fixed-time convergence, enhancing system performance and reliability.
  • Simulation results validate the effectiveness and practicality of the developed fault-tolerant control strategy.