Dynamic Intermittent Suboptimal Control: Performance Quantification and Comparisons
Abstract
This paper presents a novel intermittent suboptimal event-triggered controller design for continuous-time nonlinear systems. The stability of the equilibrium point of the closed-loop system, and the performances are analyzed and quantified theoretically. It is proven that the static and the dynamic event-triggered suboptimal controllers have a known degree of suboptimality compared to the conventional optimal control policy. In order to generate dynamic event-triggering framework, we introduce an internal dynamical system. Moreover, the Zeno behavior is excluded. Finally, a simulation example is conducted to show the effectiveness of the proposed intermittent mechanisms.
Recommended Citation
Y. Yang et al., "Dynamic Intermittent Suboptimal Control: Performance Quantification and Comparisons," Proceedings of the 37th Chinese Control Conference (2018, Wuhan, China), pp. 2017 - 2022, IEEE Computer Society, Jul 2018.
The definitive version is available at https://doi.org/10.23919/ChiCC.2018.8483352
Meeting Name
37th Chinese Control Conference, CCC (2018: Jul. 25-27, Wuhan, China)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
Closed loop systems; Continuous time systems; Dynamical systems; Continuous time nonlinear systems; Dynamic triggering; Event-triggered controls; Intermittent mechanism; Optimal control policy; Optimal controls; Performance analysis; Suboptimal controllers; Controllers; Dynamic triggering condition
International Standard Book Number (ISBN)
978-988-15639-5-8
International Standard Serial Number (ISSN)
1934-1768
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Technical Committee on Control Theory, Chinese Association of Automation, All rights reserved.
Publication Date
01 Jul 2018
Comments
This work was supported in part by the National Natural Science Foundation of China (NSFC Grant No. 61333002 and No. 61473032), Fundamental Research Funds for the China Central Universities of USTB (FRF-GF-17-B48), the Mary K. Finley Endowment, the Missouri S&T Intelligent Systems Center and the National Science Foundation.