Department: Mechanical & Aerospace Engineering
Research Institute Affiliation: Center for Control Systems and Dynamics (CCSD)
Faculty Advisor(s): Raymond de Callafon

Primary Student
Name: Huazhen Fang
Email: hufang@ucsd.edu
Phone: 858-361-0523
Grad Year: 2013

Student Collaborators
Jia Wang, jiawang@mail.dlut.edu.cn

A networked control system (NCS), which has the control loops constructed over a real-time network, has attracted much attention from both academia and the industry. NCSs not only have wide applications in various fields, but also pose many theoretical challenges on control-related topics. Although various control algorithms have been proposed for NCSs, most of them assume that system model is known ?a priori?. Thus it is of paramount importance to identify the model of a NCS before control design. Because there exist complicated uncertainty issues in the real-world networked control systems, including network uncertainties, data uncertainties, and time variations, traditional identification schemes can hardly be applied to address this problem. This study presents a deterministic approach to the robust design of networked control model in the presence of unknown but finite uncertainties in the network identification data. The aim is to solve the difficulties associated with the robust identification method due to lack of a priori knowledge on the uncertainties of the networked identification system. The authors explore the use of H_infinity estimation theory and least squares estimation for online learning of networked control model without making any assumption and requiring a priori knowledge of upper bounds, statistics and distribution.

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