Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance. (January 2018)

• Record Type: Journal Article
• Authors: Vamvoudakis, Kyriakos G.; Ferraz, Henrique
• Is Part Of: Automatica. Volume 87(2018)
• Page Start: 412
• Page End: 420
• Publication Date: 2018-01
• Subjects: Networked control -- Optimal control -- Q-learning; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2017.03.013
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper proposes a new model-free event-triggered optimal control algorithm for continuous-time linear systems. The problem is formulated as an infinite-horizon optimal adaptive learning problem, and we are able to simultaneously address the issue of designing a control and a triggering mechanism with guaranteed optimal performance by design. In order to provide a model-free solution, we adopt a Q-learning framework with a critic network to approximate the optimal cost and a zero-order hold actor network to approximate the optimal control. Since we have dynamics that evolve in continuous and discrete-time, we write the closed-loop system as an impulsive model and prove asymptotic stability of its equilibrium. Numerical simulation of an unknown unstable system is presented to show the efficacy of the proposed approach.