Event-Triggered Optimal Control With Performance Guarantees Using Adaptive Dynamic Programming

This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performan...

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Published in:	IEEE transaction on neural networks and learning systems Vol. 31; no. 1; pp. 76 - 88
Main Authors:	Luo, Biao, Yang, Yin, Liu, Derong, Wu, Huai-Ning
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive control Adaptive dynamic programming (ADP) Artificial neural networks Computer simulation Dynamic programming event-triggered Indexes Learning systems Lower bounds Mathematical model neural network (NN) Neural networks Nonlinear control Nonlinear systems Optimal control Performance analysis performance guarantee Upper bounds
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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Abstract	This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method.
AbstractList	This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method. This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method.This paper studies the problem of event-triggered optimal control (ETOC) for continuous-time nonlinear systems and proposes a novel event-triggering condition that enables designing ETOC methods directly based on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We provide formal performance guarantees by proving a predetermined upper bound. Moreover, we also prove the existence of a lower bound for interexecution time. For implementation purposes, an adaptive dynamic programming (ADP) method is developed to realize the ETOC using a critic neural network (NN) to approximate the value function of the HJB equation. Subsequently, we prove that semiglobal uniform ultimate boundedness can be guaranteed for states and NN weight errors with the ADP-based ETOC. Simulation results demonstrate the effectiveness of the developed ADP-based ETOC method.
Author	Yang, Yin Luo, Biao Liu, Derong Wu, Huai-Ning
Author_xml	– sequence: 1 givenname: Biao orcidid: 0000-0002-3353-2586 surname: Luo fullname: Luo, Biao email: biao.luo@hotmail.com organization: School of Automation, Central South University, Changsha, China – sequence: 2 givenname: Yin surname: Yang fullname: Yang, Yin email: yyang@qf.org.qa organization: College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar – sequence: 3 givenname: Derong orcidid: 0000-0003-3715-4778 surname: Liu fullname: Liu, Derong email: derongliu@foxmail.com organization: School of Automation, Guangdong University of Technology, Guangzhou, China – sequence: 4 givenname: Huai-Ning orcidid: 0000-0002-4366-5147 surname: Wu fullname: Wu, Huai-Ning email: whn@buaa.edu.cn organization: Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/30892242$$D View this record in MEDLINE/PubMed
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SubjectTerms	Adaptive control Adaptive dynamic programming (ADP) Artificial neural networks Computer simulation Dynamic programming event-triggered Indexes Learning systems Lower bounds Mathematical model neural network (NN) Neural networks Nonlinear control Nonlinear systems Optimal control Performance analysis performance guarantee Upper bounds
Title	Event-Triggered Optimal Control With Performance Guarantees Using Adaptive Dynamic Programming
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