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Research Papers

Design of Repetitive-Control System With Input Dead Zone Based on Generalized Extended-State Observer

[+] Author and Article Information
Min Wu

Professor
School of Automation,
China University of Geosciences;
Hubei Key Laboratory of Advanced Control and
Intelligent Automation for Complex Systems,
Wuhan 430074, Hubei, China
e-mail: wumin@cug.edu.cn

Pan Yu

School of Information Science and Engineering,
Central South University,
Changsha 410083, Hunan, China;
Hubei Key Laboratory of Advanced Control and
Intelligent Automation for Complex Systems,
Wuhan 430074, Hubei, China

Xin Chen

Professor
School of Automation,
China University of Geosciences;
Hubei Key Laboratory of Advanced Control and
Intelligent Automation for Complex Systems,
Wuhan 430074, Hubei, China

Jinhua She

Professor
School of Automation,
China University of Geosciences;
Hubei Key Laboratory of Advanced Control and
Intelligent Automation for Complex Systems,
Wuhan 430074, Hubei, China;
School of Engineering,
Tokyo University of Technology,
Hachioji 192-0982, Tokyo, Japan

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received April 27, 2016; final manuscript received December 22, 2016; published online May 10, 2017. Assoc. Editor: Douglas Bristow.

J. Dyn. Sys., Meas., Control 139(7), 071008 (May 10, 2017) (9 pages) Paper No: DS-16-1212; doi: 10.1115/1.4035615 History: Received April 27, 2016; Revised December 22, 2016

This paper concerns a repetitive-control system with an input-dead-zone (IDZ) nonlinearity. First, the expression for the IDZ is decomposed into a linear term and a disturbance-like one that depends on the parameters of the dead zone. A function of the system-state error is used to approximate the combination of the disturbancelike term and an exogenous disturbance. The estimate is used to compensate for the overall effect of the IDZ and the exogenous disturbance. Next, the state-feedback gains are obtained from a linear matrix inequality that contains two tuning parameters for adjusting control performance; and the pole assignment method is employed to design the gain of a state observer. Then, two stability criteria are used to test the stability of the closed-loop system. The method is simple, employing neither an inverse model of the plant nor an adaptive control technique. It is also robust with regard to the different parameters of the IDZ, uncertainties in the plant, and the exogenous disturbance. Finally, two numerical examples demonstrate the effectiveness of this method and its advantages over others.

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References

Figures

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Fig. 1

GESO-based MRCS with an IDZ

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Fig. 3

Reference input and exogenous disturbance

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Fig. 4

Simulation results for GESO-based MRCS of Plant (35) with IDZ (37)

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Fig. 5

Simulation results for method in Ref. [8] of Plant (35) with ΔA=0, ΔB=0, d0(t)=0, and IDZ (37)

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Fig. 6

Simulation results for GESO-based MRCS of Plant (35) with ΔA=0, ΔB=0, d0(t)=0, and IDZ (37)

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Fig. 7

Simulation results for GESO-based MRCS of Plant (35) with IDZ (43)

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Fig. 8

Simulation results for MRCS with and without GESO of Plant (44) with IDZ (45)

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