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This paper presents a Q-Iearning based dynamic intermittent mechanism to control linear systems evolving in continuous time. In contrast to existing event-triggered mechanisms, where complete knowledge of the system dynamics is required, the proposed dynamic intermittent control obviates this requirement while providing a quantified level of performance. An internal dynamical system will be introduced to generate the triggering condition. Then, a dynamic intermittent Q-Iearning is developed to learn the optimal value function and the hybrid controller. A qualitative performance analysis of the dynamic event-triggered control is given in comparison to the continuous-triggered control law to show the degree of suboptimality. The combined closed-loop system is written as an impulsive system, and it is proved to have an asymptotically stable equilibrium point without any Zeno behavior. A numerical simulation of an unknown unstable system is presented to show the efficacy of the proposed approach.
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Dynamic Intermittent Suboptimal Control: Performance Quantification and Comparisons
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.
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- Award ID(s):
- 1851588
- PAR ID:
- 10078410
- Date Published:
- Journal Name:
- 2018 37th Chinese Control Conference (CCC)
- Page Range / eLocation ID:
- 2017 to 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ChiCC.2018.8483352
