Learning self-triggered controllers with Gaussian processes

Autor:	Hashimoto, Kazumune, Yoshimura, Yuichi, Ushio, Toshimitsu
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	This paper investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant is \textit{unknown} apriori. To deal with the unknown transition dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. Moreover, we provide an overall implementation algorithm that jointly learns the dynamics of the plant and the self-triggered controller based on a reinforcement learning framework. Finally, a numerical simulation illustrates the effectiveness of the proposed approach. Comment: appear in IEEE Transactions on Cybernetics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.00178 Zobrazit plný text záznamu View this record from Arxiv