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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Related Experiment Video

Updated: Sep 12, 2025

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High-gain fuzzy observer based quantized input control for uncertain nonlinear systems with sensor and actuator

Yue Sun1, Chuang Gao1, Yonghui Yang1

  • 1University of Science and Technology Liaoning, School of Electronic and Information Engineering, Anshan Liaoning, 114051, China.

ISA Transactions
|August 9, 2025
PubMed
Summary

This study introduces a novel control scheme to maintain nonlinear system performance despite sensor and actuator failures. The adaptive compensation method ensures reliable system output tracking, enhancing control robustness.

Keywords:
Actuator failureAdaptive backstepping controlCommand filtered techniqueHigh-gain fuzzy observerQuantitative input controlSensor failure

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Area of Science:

  • Control Systems Engineering
  • Nonlinear System Analysis
  • Fault-Tolerant Control

Background:

  • Nonlinear systems are susceptible to performance degradation due to sensor and actuator failures.
  • Existing control schemes often struggle to maintain stability and performance under such fault conditions.
  • Robust control strategies are crucial for reliable operation in safety-critical applications.

Purpose of the Study:

  • To propose a novel control scheme for nonlinear systems addressing simultaneous sensor and actuator failures.
  • To develop an adaptive compensation mechanism for high-gain fuzzy observers to mitigate sensor failure impacts.
  • To integrate command filtering and quantitative input control for simplified design and efficient resource utilization.

Main Methods:

  • A novel compensation failure gain mechanism with an adaptive compensation term for high-gain fuzzy observers.
  • Application of the command filtered technique to manage system dynamics.
  • Utilization of quantitative input control for improved communication resource efficiency.
  • Ensuring system output follows a reference signal under failure conditions.

Main Results:

  • The proposed control scheme effectively compensates for sensor and actuator failures in nonlinear systems.
  • The adaptive compensation term in the high-gain fuzzy observer successfully mitigates sensor failure effects.
  • The integration of command filtering and quantitative input control simplified the controller design.
  • Simulation examples demonstrated the practicality and advantages of the proposed fault-tolerant control scheme.

Conclusions:

  • The developed control scheme provides robust performance for nonlinear systems facing sensor and actuator failures.
  • The adaptive compensation strategy enhances the resilience of fuzzy observers to sensor faults.
  • The controller ensures reliable system output tracking, proving its effectiveness and practicality.