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A deterministic robust control with parameter optimization for uncertain two-wheel driven mobile robot.

Qilin Wu1, Fei Lin2, Han Zhao2

  • 1School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China.

ISA Transactions
|December 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces fuzzy set theory to enhance mobile robot control accuracy by addressing uncertainty. The proposed fuzzy robust control method improves system performance and optimizes parameters for high-precision engineering applications.

Keywords:
Fuzzy set theoryOptimal designRobust controlUncertain two-wheel driven mobile robot

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Fuzzy Logic

Background:

  • Mobile robot control accuracy is limited by uncertainty, hindering high-precision applications.
  • Fuzzy set theory offers a framework to model and manage system uncertainties.
  • Existing control methods may not adequately address both robustness and performance optimization.

Purpose of the Study:

  • To develop a fuzzy mobile robot system to improve control accuracy.
  • To design a robust control method ensuring system boundedness.
  • To optimize control parameters balancing performance and cost using fuzzy numbers.

Main Methods:

  • Establishing a fuzzy mobile robot system model.
  • Designing a virtual speed controller using the backstepping method.
  • Proposing a robust control strategy for uniform boundedness.
  • Implementing a fuzzy optimization approach to minimize a performance index.

Main Results:

  • The fuzzy robust control method guarantees uniform boundedness and uniform ultimate boundedness.
  • The fuzzy optimization strategy successfully obtains optimal control parameters.
  • The proposed method demonstrates improved control accuracy compared to the linear quadratic regulator (LQR).

Conclusions:

  • Fuzzy set theory effectively models mobile robot uncertainty, enhancing control accuracy.
  • The proposed fuzzy robust control and optimization strategy is effective for high-precision applications.
  • This approach offers a viable solution for balancing performance and cost in mobile robot control.