Paper Info
Title
An online actor/critic algorithm for event-triggered optimal control of continuous-time nonlinear systems
Abstract
This paper proposes a novel optimal adaptive event-triggered control algorithm for nonlinear continuous-time systems. The goal is to reduce the controller updates, by sampling the state only when an event is triggered to maintain stability and optimality. The online algorithm is implemented based on an actor/critic neural network structure. The algorithm proposed exhibits dynamics with continuous evolutions described by ordinary differential equations and instantaneous jumps. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable.
Year
DOI
Venue
2014
10.1109/ACC.2014.6859198
American Control Conference
Keywords
Field
DocType
Lyapunov methods,adaptive control,asymptotic stability,closed loop systems,continuous time systems,differential equations,discrete event systems,evolutionary computation,neurocontrollers,nonlinear control systems,optimal control,Lyapunov stability proof,actor-critic neural network structure,asymptotic stability,closed-loop system,continuous evolution,continuous-time nonlinear systems,controller update reduction,event-triggered optimal control,instantaneous jumps,online actor-critic algorithm,optimal adaptive event-triggered control algorithm,optimality,ordinary differential equations,state sampling,Even-triggered optimal adaptive control,actor/critic framework
Online algorithm,Nonlinear system,Ordinary differential equation,Computer science,Control theory,Lyapunov stability,Control engineering,Artificial neural network,Stability theory,Control theory,Mathematical optimization,Optimal control,Algorithm
Conference
ISSN
Citations 
PageRank 
0743-1619
2
0.37
References 
Authors
2
1
Name
Order
Citations
PageRank
Kyriakos G. Vamvoudakis1274.74