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Fusion event-triggered model predictive control based on shrinking prediction horizon

Qun Cao (Department of Control Science and Engineering, Beijing Institute of Technology, Beijing, China)
Yuanqing Xia (Department of Control Science and Engineering, Beijing Institute of Technology, Beijing, China)
Zhongqi Sun (Department of Control Science and Engineering, Beijing Institute of Technology, Beijing, China)
Li Dai (Department of Control Science and Engineering, Beijing Institute of Technology, Beijing, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 13 October 2022

Issue publication date: 6 December 2022

65

Abstract

Purpose

This paper aims to design an algorithm which is used to deal with non-linear discrete systems with constraints under the lower computation burden. As a result, we solve the non-holonomic vehicle tracking problem with the lower computational load and the convergence performance.

Design/methodology/approach

A fusion event-triggered model predictive control version is developed in this paper. The authors designed a shrinking prediction strategy.

Findings

The fusion event-triggered model predictive control scheme combines the strong points of event triggered and self-triggered methods. As the practical state approaches the terminal set, the computational complexity of optimal control problem (OCP) decreases.

Originality/value

The proposed strategy has proven to stabilize the system and also guarantee a reproducible solution for the OCP. Also, it is proved to be effected by the performance of the simulation results.

Keywords

Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 61720106010, Grant 61836001.

Citation

Cao, Q., Xia, Y., Sun, Z. and Dai, L. (2022), "Fusion event-triggered model predictive control based on shrinking prediction horizon", Assembly Automation, Vol. 42 No. 6, pp. 721-729. https://doi.org/10.1108/AA-02-2022-0022

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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