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A data-driven fault-tolerant control design of linear multivariable systems with performance optimization.

Zhe Li1, Guang-Hong Yang2

  • 1College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

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|August 3, 2017
PubMed
Summary

This study introduces a data-driven fault-tolerant control (FTC) scheme using Youla parameterization for MIMO systems. It effectively mitigates fault impacts and optimizes performance even with unknown system parameters.

Keywords:
Adaptive scheme.Data-drivenFault-tolerant control (FTC)Residual generator

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Area of Science:

  • Control Systems Engineering
  • Data-Driven Control
  • System Identification

Background:

  • Fault-tolerant control (FTC) is crucial for maintaining system stability and performance during component failures.
  • Multiple-input multiple-output (MIMO) systems present complex challenges for control design due to interdependencies.
  • Existing FTC methods often require accurate system models, which are not always available.

Purpose of the Study:

  • To propose an integrated data-driven fault-tolerant control (FTC) design scheme for MIMO systems.
  • To address challenges posed by unknown system model parameters in FTC design.
  • To enhance system robustness and performance optimization during fault conditions.

Main Methods:

  • Utilizing Youla parameterization for control design.
  • Applying canonical form identification for residual observer design in fault-free scenarios.
  • Employing a gradient-based algorithm for online tuning of Youla parameters in faulty cases.
  • Introducing a novel adaptive scheme for residual generator robustness.

Main Results:

  • Successful attenuation of fault influence in MIMO systems.
  • Optimization of system performance through online parameter tuning.
  • Improved robustness of the residual generator against system deviations.
  • Demonstrated effectiveness via simulation on a two-tank flow system.

Conclusions:

  • The proposed integrated data-driven FTC scheme effectively handles unknown system parameters.
  • The Youla parameterization combined with adaptive techniques offers robust fault tolerance.
  • The method optimizes system performance and prevents over-activation of the residual generator.