June 5, 2017: Thomas Parisini, Imperial College, London
Monday, June 5, 2017
11am -12pm
15 St. Mary’s St. Rm 105
Refreshments at 10:45
Plug and Play Monitoring and Fault-Tolerant Control of Large-scale Systems: A Scalable Distributed Approach
This lecture deals with a class of systems that are becoming ubiquitous in the current and future “distributed world” made by countless “nodes”, which can be cities, computers, people, etc., and interconnected by a dense web of transportation, communication, or social ties. The term “network”, describing such a collection of nodes and links, nowadays has become commonplace thanks to our extensive reliance on “connections of interdependent systems” in our everyday life, for building complex technical systems, infrastructures and so on. In an increasingly “smarter” planet, it is expected that such interconnected systems will be safe, reliable, available 24/7, and of low-cost maintenance. Therefore, health monitoring, fault diagnosis and fault-tolerant control are of customary importance to ensure high levels of safety, performance, reliability, dependability, and availability. For example, in the case of industrial plants, faults and malfunctions can result in off-specification production, increased operating costs, production line shutdown, danger conditions for humans, detrimental environmental impact, and so on. Faults and malfunctions need to be detected promptly and their source and severity should be diagnosed so that corrective actions can be taken as soon as possible. Once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected large-scale system. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in.
In the talk, an adaptive approximation-based distributed fault diagnosis approach for large-scale nonlinear systems will be dealt with, by exploiting a “divide et impera” approach in which the overall diagnosis problem is decomposed into smaller sub-problems, which can be solved within “local” computation architectures. The distributed detection, isolation and identification task is broken down and assigned to a network of “Local Diagnostic Units”, each having a “local view” of the system.
Moreover, the lecture will address the integration of a distributed model predictive control scheme and a distributed fault diagnosis architecture. Specifically, in the off-line control design phase we adopt a decentralized algorithm and we assume that the design of a local controller can use information at most from parents of the corresponding subsystem, i.e., subsystems that influence its dynamics. This implies that the whole model of the large-scale system is never used in any step of the design process. This approach has several advantages in terms of scalability: i) the communication flow at the design phase has the same topology of the coupling graph – usually sparse – ii) the local design of controllers and fault detectors can be conducted independently; iii) local design complexity scales with the number of parent subsystems only; iv) if a subsystem joins/leaves an existing network (plug-in/unplugging operation) at most children/parents subsystems have to retune their controllers and fault detectors. We refer to this kind of decentralized synthesis as plug & play design, if – in addition – the plug-in and unplugging operations can be performed through a procedure for automatically assessing whether the operation does not spoil stability and constraint satisfaction for the overall large-scale system.
Thomas Parisini received the “Laurea” degree (Cum Laude and printing honours) in Electronic Engineering from the University of Genoa in 1988 and the Ph.D. degree in Electronic Engineering and Computer Science in 1993. He was with Politecnico di Milano, as Associate Professor and since November 2010. He holds the Chair of Industrial Control at Imperial College London. During 2001-2012, he was Professor and Danieli Endowed Chair of Automation Engineering with University of Trieste. In 2009-2012, he was appointed Deputy Rector of University of Trieste. He authored or co-authored over 250 research papers in archival journals, book chapters, and international conference proceedings. His research interests include neural-network approximations for optimal control and filtering problems, fault diagnosis for nonlinear and distributed systems, estimation and suppression of periodical disturbances in control systems, and nonlinear model predictive control systems. Among several awards, he was a co-recipient of the 2004 Outstanding Paper Award of the IEEE Trans. on Neural Networks and a recipient of the 2007 IEEE Distinguished Member Award. He was involved as Project Leader in several projects funded by the European Union, by the Italian Ministry for Research, and he is currently leading major consultancy projects with the Danieli Group (world-leader in steel-making plants). In 2013 he was awarded a prestigious ABB Research Grant.
Faculty Host: Yannis Paschalidis