|Time:||October 24, 2023|
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Prof. Robert Bitmead
University of California, San Diego
San Diego, CA, USA
Tuesday 2023-10-24 4 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
Power grid, communications, computer and product reticulation networks are frequently layered or subdivided by design. The OSI seven-layer computer network model and the electrical grid division into generation, transmission, distribution and associated markets are cases in point. The layering divides responsibilities and can be driven by operational, commercial, regulatory and privacy concerns. From a control context, a layer, or part of a layer, in a network isolates the authority to manage, i.e. control, a dynamic system with connections into unknown and unmodeled parts of the network. The topology of these connections is fully prescribed but the interconnecting signals, currents in the case of power grids and bandwidths in communications, are largely unavailable, through lack of sensing and even prohibition. Accordingly, one is driven to simultaneous input and state estimation methods. This is the province of this presentation, guided by the structure of these network problems. We study a class of algorithms for this joint task, which has the unfortunate issue of inverting a subsystem, which if it has unstable transmission zeros leads to an unstable and unimplementable estimator. Two modifications to the algorithm to ameliorate this problem were recently proposed involving replacing the troublesome subsystem with its outer factor from its inner-outer factorization or using the Kalman filter for a high-variance white signal model for the unknown inputs. The outer factor has only stable transmission zeros and so is stably invertible. The Kalman filter is stable by design. Here, we establish the connections between the original estimation problem for state and input signal and the outputs/estimates from the algorithm applied solely to the outer factor. It is also shown that the outer factor algorithm is the limit of the high-variance strategy, which yields an even simpler approach and implementation. That is, both approaches are the same. Power systems provide the background examples.
Bob Bitmead is Distinguished Professor in Mechanical & Aerospace Engineering at the University of California, San Diego. He is currently SimTech Visiting Professor at the University of Stuttgart until December. He holds degrees in Applied Mathematics and Electrical Engineering from Sydney University and Newcastle University, both in Australia. He has held faculty positions at the Australian National University and James Cook University of North Queensland. He is a control theorist with a long experience in control applications in many industrial sectors. His theoretical work is strongly informed and guided by these applications. He received the 2014 ASME Rufus Oldenburger Medal and the 2015 IEEE Controls Systems Transition to Practice Award. Bob was President of the IEEE Control Systems Society in 2019. He is Fellow of IEEE, IFAC and the Australian Academy of Technological Sciences and Engineering. Bob brews his own beer and was from 2005-2022 an accredited and active Australian Rules Football umpire training with the San Diego Lions Australian Football Club.