TY - GEN
T1 - Sparsity based feedback design
T2 - A new paradigm in opportunistic sensing
AU - Bhattacharya, Sourabh
AU - Başar, Tamer
PY - 2011
Y1 - 2011
N2 - We introduce the concept of using compressive sensing techniques to provide feedback in order to control dynamical systems. Compressive sensing algorithms use l1-regularization for reconstructing data from a few measurement samples. These algorithms provide highly efficient reconstruction for sparse data. For data that is not sparse enough, the reconstruction technique produces a bounded error in the estimate. In a dynamical system, such erroneous state-estimation can lead to undesirable effects in the output of the plant. In this work, we present some techniques to overcome the aforementioned restriction. Our efforts fall into two main categories. First, we present some techniques to design feedback systems that sparsify the state in order to perfectly reconstruct it using compressive sensing algorithms. We study the effect of such sparsification schemes on the stability and regulation of the plant. Second, we study the characteristics of dynamical systems that produce sparse states so that compressive sensing techniques can be used for feedback in such scenarios without any additional modification in the feedback loop.
AB - We introduce the concept of using compressive sensing techniques to provide feedback in order to control dynamical systems. Compressive sensing algorithms use l1-regularization for reconstructing data from a few measurement samples. These algorithms provide highly efficient reconstruction for sparse data. For data that is not sparse enough, the reconstruction technique produces a bounded error in the estimate. In a dynamical system, such erroneous state-estimation can lead to undesirable effects in the output of the plant. In this work, we present some techniques to overcome the aforementioned restriction. Our efforts fall into two main categories. First, we present some techniques to design feedback systems that sparsify the state in order to perfectly reconstruct it using compressive sensing algorithms. We study the effect of such sparsification schemes on the stability and regulation of the plant. Second, we study the characteristics of dynamical systems that produce sparse states so that compressive sensing techniques can be used for feedback in such scenarios without any additional modification in the feedback loop.
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U2 - 10.1109/acc.2011.5991014
DO - 10.1109/acc.2011.5991014
M3 - Conference contribution
AN - SCOPUS:80053163843
SN - 9781457700804
T3 - Proceedings of the American Control Conference
SP - 3704
EP - 3709
BT - Proceedings of the 2011 American Control Conference, ACC 2011
PB - Institute of Electrical and Electronics Engineers Inc.
ER -