Bassam Bamieh
University of California, Santa Barbara
217 Papers
1.4K Citations
Bassam Bamieh is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Optimal control & Distributed parameter system. The author has an hindex of 37, co-authored 206 publications. Previous affiliations of Bassam Bamieh include Cornell University & Rice University.
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Papers
Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback
TL;DR: In this article, the authors consider distributed consensus and vehicular formation control problems and show that in low spatial dimensions, local feedback is not sufficient to regulate large-scale disturbances, but it can in higher spatial dimensions.
393
A convex characterization of distributed control problems in spatially invariant systems with communication constraints
TL;DR: It is shown that the problem of optimal design can be cast as a convexproblem provided that the plant has a similar funnel-causality structure, and the propagation speeds in the controller are at least as fast as those in the plant.
268
The H 2 problem for sampled-data systems m for sampled-data systems
Bassam Bamieh,J. Boyd Pearson +1 more
TL;DR: In this paper, a generalization of the H2 cost to periodic systems is presented, which is then applied to the continuous-time closed-loop mapping in a sampled-data control system.
171
Identification of linear parameter varying models
Bassam Bamieh,Laura Giarre +1 more
- 07 Dec 1999
TL;DR: In this article, the problem of identifying discrete-time linear parameter varying models of nonlinear or time-varying systems is considered, and the identification problem can be reduced to a linear regression, and conditions on persistency of excitation in terms of the inputs and parameter trajectories.
151
Consensus and Coherence in Fractal Networks
Stacy Patterson,Bassam Bamieh +1 more
TL;DR: In this paper, first-and second-order consensus algorithms in networks with stochastic disturbances are studied and the deviation from consensus is quantified using the notion of network coherence, which can be expressed as an $H 2 -norm.
107