An online actor/critic algorithm for event-triggered optimal control of continuous-time nonlinear systems

Author

Vamvoudakis, Kyriakos G.

Author_Institution

Center for Control, Dynamical-Syst. & Comput. (CCDC), Univ. of California, Santa Barbara, Santa Barbara, CA, USA

fYear

2014

fDate

4-6 June 2014

Firstpage

1

Lastpage

6

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.

Keywords

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; Asymptotic stability; Closed loop systems; Equations; Heuristic algorithms; Lyapunov methods; Neural networks; Stability analysis; Even-triggered optimal adaptive control; actor/critic framework;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2014

Conference_Location

Portland, OR

ISSN

0743-1619

Print_ISBN

978-1-4799-3272-6

Type

conf

DOI

10.1109/ACC.2014.6859198

Filename

6859198