TY - GEN
T1 - On Separation of Distributed Estimation and Control for LTI Systems
AU - Savas, Anthony J.
AU - Park, Shinkyu
AU - Poor, H. Vincent
AU - Leonard, Naomi E.
N1 - Funding Information:
Supported by the DoD through the NDSEG program, ONR grant N00014-19-1-2556, funding from KAUST, and Princeton School of Engineering and Applied Science through the generosity of Lydia and William Addy ’82. * indicates equal contributions.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The separation principle in a centralized estimation and control problem gives us the flexibility to design a feedback controller independent of the state estimator. However, the same principle does not hold when the estimation and control are distributed over a network of agents. In this case, the estimator may need to be redesigned when the controller is revised, which can be computationally expensive. We investigate a weaker notion of the separation principle in the distributed estimation and control of linear time-invariant (LTI) systems. As a main contribution, applying the small-gain theorem, we characterize the notion using matrix inequalities and compute a set of feedback controllers that agents in the network can adopt without redesigning the estimator. We also analyze how the frequency of information exchange between neighboring agents affects the characterization. We illustrate our analytical results through simulations of a multi-vehicle system problem.
AB - The separation principle in a centralized estimation and control problem gives us the flexibility to design a feedback controller independent of the state estimator. However, the same principle does not hold when the estimation and control are distributed over a network of agents. In this case, the estimator may need to be redesigned when the controller is revised, which can be computationally expensive. We investigate a weaker notion of the separation principle in the distributed estimation and control of linear time-invariant (LTI) systems. As a main contribution, applying the small-gain theorem, we characterize the notion using matrix inequalities and compute a set of feedback controllers that agents in the network can adopt without redesigning the estimator. We also analyze how the frequency of information exchange between neighboring agents affects the characterization. We illustrate our analytical results through simulations of a multi-vehicle system problem.
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U2 - 10.1109/CDC51059.2022.9992724
DO - 10.1109/CDC51059.2022.9992724
M3 - Conference contribution
AN - SCOPUS:85147027230
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 963
EP - 968
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -