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A novel linear tracking integrator with integral compensation and its application.

Xingling Shao1, Jun Liu2, Honglun Wang3

  • 1Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China; School of Automation Science and Electrical Engineering, Beihang University, 100191 Beijing, China.

ISA Transactions
|October 2, 2017
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Summary

A new linear tracking integrator (LTI) offers drift-free integral estimation from noisy data, even with bias. This method accurately estimates integrals and tracks signals without needing observability, outperforming traditional filters.

Keywords:
Describing functionFinite-time convergent integral observerFrequency-domainIntegral actionLinear tracking integratorNoisy measurement

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Area of Science:

  • Control Systems Engineering
  • Signal Processing

Background:

  • Integral estimation is crucial but challenged by measurement bias and noise.
  • Existing methods like finite-time convergent integral observers (FTCIO) can suffer from integral drift.

Purpose of the Study:

  • To propose a novel linear tracking integrator (LTI) for efficient and drift-free integral estimation.
  • To address limitations of FTCIO in handling integral drift.
  • To provide accurate integral and tracking estimates from noisy measurements without observability constraints.

Main Methods:

  • Development of a linear tracking integrator (LTI) incorporating integral compensation in the feedback path.
  • Utilizing the describing function method to analyze FTCIO limitations.
  • Frequency-domain analysis for LTI parameter tuning guidelines.
  • Comparative simulations against the Kalman filter.

Main Results:

  • The proposed LTI achieves drift-free integral estimation.
  • LTI provides accurate integral and tracking estimates simultaneously from noisy measurements.
  • LTI does not require the condition of observability.
  • Simulations demonstrate LTI's superiority over the Kalman filter in handling bias.
  • LTI's effectiveness is validated in an autopilot design application.

Conclusions:

  • The novel LTI offers a robust and practical solution for integral estimation in the presence of bias.
  • LTI presents a significant advancement for applications requiring precise integral tracking, such as aircraft autopilot systems.