Public defence in Automation, Systems and Control Engineering, M.Sc. Tahmoores Farjam
M.Sc. Tahmoores Farjam will defend the thesis "Control-Aware Distributed Channel Access for Networked Control Systems" on 19 December 2022 at 14 (EET) in Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation.
Opponent Dr. Konstantinos Gatsis, University of Oxford, Yhdistynyt kuningaskunta
Custos: Prof. Themistoklis Charalambous, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation
Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/
Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53
Public defence announcement:
Modern industrial control environments consist of a multitude of spatially distributed sensors, controllers, and actuators that form various complex control loops. Networked control systems (NCSs) refer to such systems wherein the communication and information exchange between these distributed components happen over a shared communication network. NCSs offer several advantages over their traditional counterparts, such as higher ﬂexibility and scalability with lower deployment and maintenance costs. However, they also introduce novel challenges that need to be addressed before their full potential can be realized.
A major challenge arises from the limited capacity of the network which necessitates efﬁcient sharing of the available communication resources among control loops based on their real-time needs. This thesis contributes to this endeavor by proposing control-aware channel access methods for various channel models. This method is shown to be capable of enabling near-optimal performance in terms of a control-oriented objective, and the conditions that guarantee stability of the system operating with this method are established. A shortcoming of this approach is that knowledge of certain channel parameters is essential for its implementation. To overcome this in practical settings, various machine learning algorithms are utilized in this framework such that the unknown parameters are learned with respect to the control-oriented objective in a distributed manner.
Contact information of doctoral candidate