Learning-Based Adaptive Control for Stochastic Linear Systems with Input Constraints

Autor:	Siriya, Seth, Zhu, Jingge, Nešić, Dragan, Pu, Ye
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results. Comment: 16 pages, 2 figures, accepted at IEEE Control Systems Letters
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.07040 Zobrazit plný text záznamu View this record from Arxiv