Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance

Qun Cao; Yuanqing Xia; Zhongqi Sun; Hongru Jiang; Xiaopeng Liu

doi:10.1109/DDCLS.2018.8515961

Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance

Qun Cao, Yuanqing Xia, Zhongqi Sun, Hongru Jiang, Xiaopeng Liu

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we present a self-triggered model predictive control (MPC) scheme for unicycle robots with coupled input constraint and bounded external disturbances. Firstly, based on Lyapunov theory, a self-triggered mechanism is developed to the aim of reducing the computation load of MPC. Secondly, by designing a robust terminal region and proper parameters, stability of the closed-loop system as well as a sub-optimal performance are guaranteed. In addition, we compare the given self-triggered MPC scheme with the traditional one. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed strategy.

Original language	English
Title of host publication	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	860-865
Number of pages	6
ISBN (Electronic)	9781538626184
DOIs	https://doi.org/10.1109/DDCLS.2018.8515961
Publication status	Published - 30 Oct 2018
Event	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, China Duration: 25 May 2018 → 27 May 2018

Publication series

Name	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

Conference

Conference	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Country/Territory	China
City	Enshi, Hubei Province
Period	25/05/18 → 27/05/18

Keywords

Bounded external disturbances
Input constraint
Model predictive control
Self-triggered
Unicycle robots

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10.1109/DDCLS.2018.8515961

Cite this

Cao, Q., Xia, Y., Sun, Z., Jiang, H., & Liu, X. (2018). Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (pp. 860-865). Article 8515961 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS.2018.8515961

Cao, Qun ; Xia, Yuanqing ; Sun, Zhongqi et al. / Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance. Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 860-865 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

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title = "Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance",

abstract = "In this paper, we present a self-triggered model predictive control (MPC) scheme for unicycle robots with coupled input constraint and bounded external disturbances. Firstly, based on Lyapunov theory, a self-triggered mechanism is developed to the aim of reducing the computation load of MPC. Secondly, by designing a robust terminal region and proper parameters, stability of the closed-loop system as well as a sub-optimal performance are guaranteed. In addition, we compare the given self-triggered MPC scheme with the traditional one. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed strategy.",

keywords = "Bounded external disturbances, Input constraint, Model predictive control, Self-triggered, Unicycle robots",

author = "Qun Cao and Yuanqing Xia and Zhongqi Sun and Hongru Jiang and Xiaopeng Liu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 ; Conference date: 25-05-2018 Through 27-05-2018",

year = "2018",

month = oct,

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doi = "10.1109/DDCLS.2018.8515961",

language = "English",

series = "Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018",

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Cao, Q, Xia, Y, Sun, Z, Jiang, H & Liu, X 2018, Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance. in Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018., 8515961, Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 860-865, 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei Province, China, 25/05/18. https://doi.org/10.1109/DDCLS.2018.8515961

Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance. / Cao, Qun; Xia, Yuanqing; Sun, Zhongqi et al.
Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 860-865 8515961 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance

AU - Cao, Qun

AU - Xia, Yuanqing

AU - Sun, Zhongqi

AU - Jiang, Hongru

AU - Liu, Xiaopeng

PY - 2018/10/30

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N2 - In this paper, we present a self-triggered model predictive control (MPC) scheme for unicycle robots with coupled input constraint and bounded external disturbances. Firstly, based on Lyapunov theory, a self-triggered mechanism is developed to the aim of reducing the computation load of MPC. Secondly, by designing a robust terminal region and proper parameters, stability of the closed-loop system as well as a sub-optimal performance are guaranteed. In addition, we compare the given self-triggered MPC scheme with the traditional one. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed strategy.

AB - In this paper, we present a self-triggered model predictive control (MPC) scheme for unicycle robots with coupled input constraint and bounded external disturbances. Firstly, based on Lyapunov theory, a self-triggered mechanism is developed to the aim of reducing the computation load of MPC. Secondly, by designing a robust terminal region and proper parameters, stability of the closed-loop system as well as a sub-optimal performance are guaranteed. In addition, we compare the given self-triggered MPC scheme with the traditional one. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed strategy.

KW - Bounded external disturbances

KW - Input constraint

KW - Model predictive control

KW - Self-triggered

KW - Unicycle robots

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BT - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

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Cao Q, Xia Y, Sun Z, Jiang H, Liu X. Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 860-865. 8515961. (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). doi: 10.1109/DDCLS.2018.8515961

Self-triggered MPC for tracking of constrained wheeled robots with additive disturbance

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