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Decomposition and Structured Model Reduction for Large Scale Systems Analysis

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If you have a question about this talk, please contact Tim Hughes.

In order to analyse or design a controller for a dynamical system one often needs to first reduce the state dimension. In the realm of networked systems this is true even for Linear Time Invariant (LTI) systems. Traditional approaches to model reduction such as truncation and singular perturbation require one to balance the model first, thus introducing a coordinate transformation on the state variable. For networked systems, balancing annihilates the sparsity pattern in the system matrices, and therefore removes the inherent network structure. In this talk I will present a method for structure-preserving model reduction based on balance truncation. The method comes with error bounds and the reduction can be computed using semidefinite programming in the worst case and linear algebra in the best case. The algorithms developed are applied to mechanical and biological systems and shown to outperform existing approaches.

This talk is part of the CUED Control Group Seminars series.

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