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<p>Achieving unbiased control is a challenging problem when there is an unknown measurement bias in the sensor. For a class of nonholonomic mobile robots with positioning deviation, this article addresses the problem of unbiased control by using composite learning to estimate the bias. To our best knowledge, it is the first case of using composite learning methods to achieve unbiased control in the presence of unknown sensor bias. Furthermore, since the excitation matrix is reversible when the interval excitation condition is met, the sensor bias can be accurately estimated, and a practical prescribed time unbiased controller is designed to achieve faster convergence and ensure steady-state performance. The simulation demonstrates the unbiasedness of the control method.</p>

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Composite learning-based unbiased control for nonholonomic mobile robots with sensor uncertainties

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Dianrui Mu,
  • Changchun Hua,
  • Yu Zhang,
  • Jiannan Chen,
  • Keli Pang

摘要

Achieving unbiased control is a challenging problem when there is an unknown measurement bias in the sensor. For a class of nonholonomic mobile robots with positioning deviation, this article addresses the problem of unbiased control by using composite learning to estimate the bias. To our best knowledge, it is the first case of using composite learning methods to achieve unbiased control in the presence of unknown sensor bias. Furthermore, since the excitation matrix is reversible when the interval excitation condition is met, the sensor bias can be accurately estimated, and a practical prescribed time unbiased controller is designed to achieve faster convergence and ensure steady-state performance. The simulation demonstrates the unbiasedness of the control method.