Bassam Bamieh

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Bassam Bamieh is Professor of Mechanical Engineering at  at the University of California at Santa Barbara. His research interests are in the fundamentals of Control and Dynamical Systems such as Robust, Optimal and Distributed Control, as well as working at the interface with other scientific ares such as shear flow transition and turbulence, thermoacoustics and quantum control. He is a past recipient of the  AACC Hugo Schuck Best Paper Award,  and the IEEE Control Systems Society G. S. Axelby Outstanding Paper Award (twice). He is a Fellow of the International Federation of Automatic Control (IFAC) , and a Fellow of the IEEE.

University of California-Santa Barbara
Distinguished Lecturer


Department of Mechanical Engineering, ENG II Bldg., University of California at Santa Barbara
Santa Barbara, California 93106
United States

Distinguished Lecture Program

Talk Title: Multiplicative Noise as a Structured Stochastic Uncertainty Problem

Linear systems with multiplicative, time-varying noise exhibit varied and rich phenomenology such as heavy tails and dramatic differences between different notions of convergence. We study such systems in a framework similar to that used in robust control where the stochastic parameters are viewed as a "structured uncertainty". In particular, a purely input-output approach is developed to characterize mean-square stability. This approach clarifies earlier results in this area and also easily produces new ones in the case of correlated uncertainties. Applications of this framework to networked dynamical systems with link failures and stochastic topologies will be illustrated. In addition, an application to a model of the Cochlea will be described which potentially explains otoacoustic emissions as an instability mechanism. Finally, we illustrate some interesting connections of this work with the phenomenon of Anderson Localization which is a canonical problem in the statistical physics of disordered media. 

Talk Title: Scaling Limits in Networked Control Systems

The question of how difficult or easy it is to control a certain network of interconnected dynamical agents is fundamental to understanding engineered or naturally occurring networks, such as vehicular formations or power grids amongst many others. I will argue that standard notions of stability and controllability as binary properties (e.g. a system is either stable or not), convergence rates, or even reachability analysis may fail to predict the behavior of large networks. These apparent difficulties motivate a notion of network controllability based on hard limits on performance in optimal control problems with structural constraints. While such problems are known to be generally intractable, I will show certain examples from vehicular platoons and power grids where informative and simple answers are possible in the asymptotic limit of large system size. This analysis gives asymptotic bounds on network performance and shows its dependence on both the complexity of individual node dynamics,  as well as network connectivity. Some interesting connections between these results and the statistical mechanics of disordered media will be highlighted.