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Data driven control for a class of nonlinear systems with output saturation.

Xuhui Bu1, Qingfeng Wang2, Zhongsheng Hou3

  • 1School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454003, China; Institute of Artificial Intelligence and Control, Qingdao University of Science and Technology, Qingdao 266061, China.

ISA Transactions
|August 1, 2018
PubMed
Summary

This study introduces a data-driven control (DDC) method for nonlinear systems with output saturation. The approach ensures system convergence despite saturation, with tracking error dependent on trajectory changes.

Keywords:
ConvergenceData driven designModel free adaptive controlSensor saturation

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Area of Science:

  • Control Engineering
  • Nonlinear System Analysis
  • Data-Driven Methods

Background:

  • Non-affine nonlinear systems present challenges for traditional control.
  • Output saturation significantly impacts system performance and stability.
  • Data-driven control (DDC) offers an alternative for complex systems.

Purpose of the Study:

  • To develop a DDC algorithm for non-affine nonlinear systems with output saturation.
  • To analyze the convergence properties and performance effects of output saturation.
  • To validate the proposed DDC approach through simulation examples.

Main Methods:

  • Dynamic linearization to create a time-varying linear data model.
  • Development of a DDC algorithm using only input and saturated output data.
  • Rigorous mathematical proof of algorithm convergence.
  • Analysis of output saturation's impact on convergence rate and tracking error.

Main Results:

  • A novel DDC algorithm is proposed for nonlinear systems with output saturation.
  • The algorithm's convergence is mathematically proven.
  • Output saturation slows the convergence rate but maintains stability.
  • Tracking error is bounded and converges to zero for constant desired trajectories.

Conclusions:

  • The proposed DDC method effectively handles output saturation in nonlinear systems.
  • System stability is preserved despite saturation effects.
  • Performance, particularly convergence speed, is influenced by saturation and trajectory dynamics.