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Award ID contains: 1635014

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  1. ; ; ; (, IEEE transactions on control of network systems)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, 2021 60th IEEE Conference on Decision and Control (CDC))
    Accepted Manuscript Full Text Available
  3. ; ; (, SIAM Journal on Control and Optimization)
    Full Text Available 
  4. ; ; (, Proceedings of the 3rd Conference on Learning for Dynamics and Control)
    Accepted Manuscript Full Text Available
  5. ; (, 2020 59th IEEE Conference on Decision and Control (CDC))
    Full Text Available 
  6. ; ; (, IEEE Transactions on Control of Network Systems)
    Full Text Available 
  7. ; ; ; (, IEEE Transactions on Automatic Control)
    Full Text Available 
  8. ; (, IEEE Conference on Decision and Control)
    Full Text Available 
  9. ; ; (, 2018 IEEE Conference on Decision and Control (CDC))
    Given a linear dynamical system affected by noise, we consider the problem of optimally placing sensors (at design-time) subject to certain budget constraints to minimize the trace of the steady-state error covariance of the Kalman filter. Previous work has shown that this problem is NP-hard in general. In this paper, we impose additional structure by considering systems whose dynamics are given by a stochastic matrix corresponding to an underlying consensus network. In the case when there is a single input at one of the nodes in a tree network, we provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. However, we show that when the network has multiple inputs, the optimal sensor placement problem becomes NP-hard. 
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  10. ; (, 2018 IEEE Conference on Decision and Control (CDC))
    Full Text Available