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Observer-based adaptive backstepping control for fractional order systems with input saturation.

Dian Sheng1, Yiheng Wei1, Songsong Cheng1

  • 1Department of Automation, University of Science and Technology of China, Hefei 230026, China.

ISA Transactions
|July 8, 2017
PubMed
Summary

This study introduces an observer-based fractional order anti-saturation adaptive backstepping control for systems with input saturation. The novel approach ensures stability and compensates for saturation using virtual signals and an auxiliary system.

Keywords:
Adaptive backstepping controlIncommensurate fractional order systemsIndirect Lyapunov methodObserverSaturation

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Control Theory
  • Fractional Order Systems

Background:

  • Fractional order systems present unique control challenges.
  • Input saturation and partial state measurability complicate controller design.
  • Existing methods may not adequately address these combined complexities.

Purpose of the Study:

  • To develop an observer-based fractional order anti-saturation adaptive backstepping control scheme.
  • To address incommensurate fractional order systems with input saturation and partial state information.
  • To ensure system stability and performance under saturation.

Main Methods:

  • A novel fractional order state observer is designed for unmeasured states.
  • A fractional order auxiliary system generates virtual signals to compensate for input saturation.
  • Adaptive backstepping control with indirect Lyapunov method is employed for controller design.

Main Results:

  • The proposed observer successfully estimates system states from partial measurements.
  • The control scheme effectively compensates for input saturation.
  • Simulation results demonstrate the effectiveness and robustness of the proposed control strategy.

Conclusions:

  • The observer-based fractional order anti-saturation adaptive backstepping control is effective for incommensurate fractional order systems.
  • The method provides a viable solution for systems with input saturation and limited state information.
  • The proposed approach enhances system stability and control performance.