Robust stability of reduced-order linear ADRC for first-order plants with large time-delay

WANG Yongshuai; CHEN Zengqiang; SUN Mingwei; SUN Qinglin

doi:10.3969/j.issn.0253-2778.2019.01.008

JUSTC > 2019 > 49(1): 55-62. > DOI: 10.3969/j.issn.0253-2778.2019.01.008

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Open Access JUSTC

Robust stability of reduced-order linear ADRC for first-order plants with large time-delay

1.
College of Artificial Intelligence,Nankai University,Tianjin 300350,China
2.
Key Laboratory of Intelligent Robotics of Tianjin,Tianjin 300350,China

Cite this: JUSTC, 2019, 49(1): 55-62

https://doi.org/10.3969/j.issn.0253-2778.2019.01.008

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Received Date: September 20, 2017
Revised Date: September 26, 2018
Published Date: January 30, 2019

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Abstract

Abstract

Aiming at first-order inertial plants with large time-delay, the stability and robustness of RLADRC are studied by combining it with the Smith predictor. The stable feasible region of parameters is obtained according to routh criterion together with the verification of the numerical analysis. Besides, the phase margin range in the feasible region is analyzed according to the frequency response. In the end, the predictive RLADRC is compared with single RLADRC on robustness when parameters of control plants have some perturbation, and the results prove that predictive RLADRC has better dynamic performance and stronger robustness based on Monte Carlo experiments. These conclusions can be used to design parameters of the Smith predictor and RLADRC controllers.

Abstract

Aiming at first-order inertial plants with large time-delay, the stability and robustness of RLADRC are studied by combining it with the Smith predictor. The stable feasible region of parameters is obtained according to routh criterion together with the verification of the numerical analysis. Besides, the phase margin range in the feasible region is analyzed according to the frequency response. In the end, the predictive RLADRC is compared with single RLADRC on robustness when parameters of control plants have some perturbation, and the results prove that predictive RLADRC has better dynamic performance and stronger robustness based on Monte Carlo experiments. These conclusions can be used to design parameters of the Smith predictor and RLADRC controllers.