TY - JOUR
T1 - Identification of spatiotemporally invariant systems for control adaptation
AU - Sarwar, Azeem
AU - Voulgaris, Petros G.
AU - Salapaka, Srinivasa M.
N1 - Funding Information:
Petros G. Voulgaris received a Diploma in mechanical engineering from the National Technical University, Athens, Greece, in 1986, and the S.M. and Ph.D. degrees in aeronautics and astronautics from the Massachusetts Institute of Technology, Cambridge, in 1988 and 1991, respectively. Since August 1991, he has been with the Department of Aerospace Engineering, University of Illinois at Urbana Champaign, where he is currently a Professor. He also holds joint appointments with the Coordinated Science Laboratory, and the Department of Electrical and Computer Engineering at the same university. His research interests include robust and optimal control and estimation, communications and control, networks and control, and applications of advanced control methods to engineering practice including flight control, nanoscale control, robotics, and structural control systems. Dr. Voulgaris is a recipient of the National Science Foundation Research Initiation Award (1993), the Office of Naval Research Young Investigator Award (1995) and the UIUC Xerox Award for research. He has been an Associate Editor for the IEEE Transactions on Automatic Control and the ASME Journal of Dynamic Systems, Measurement and Control. He is also a Fellow of IEEE.
Funding Information:
This material is based upon work supported in part by the National Science Foundation under NSF Awards Nos CCR 03-25716 ITR , CNS 08-34409 , and by AFOSR grant FA9950-06-1-0252 . The material in this paper was partially presented at the American Control Conference (ACC 2010), June 30–July 2, 2010, Baltimore, Maryland, USA. This paper was recommended for publication in revised form by Associate Editor Xiaobo Tan under the direction of Editor Miroslav Krstic.
PY - 2012/9
Y1 - 2012/9
N2 - We present a distributed projection algorithm for system identification of spatiotemporally invariant systems with the ultimate purpose of utilizing it in an indirect adaptive control scheme. Each subsystem communicates only with its immediate neighbors to share its current estimate along with a cumulative improvement index. On the basis of the cumulative improvement index, the best estimate available is picked in order to carry out the next iteration. For small estimation error, the scheme switches over to a "smart" averaging routine. The proposed algorithm guarantees to bring the local estimates arbitrarily close to one another, developing a "local consensus", which makes it amenable to control by the application of indirect distributed adaptive control schemes. It is also shown through simulations that the proposed algorithm has a clear advantage over the standard projection algorithm. Our proposed algorithm is also suitable for addressing the estimation problem in distributed networks that arise in a variety of applications, such as environment monitoring, target localization and potential sensor network problems.
AB - We present a distributed projection algorithm for system identification of spatiotemporally invariant systems with the ultimate purpose of utilizing it in an indirect adaptive control scheme. Each subsystem communicates only with its immediate neighbors to share its current estimate along with a cumulative improvement index. On the basis of the cumulative improvement index, the best estimate available is picked in order to carry out the next iteration. For small estimation error, the scheme switches over to a "smart" averaging routine. The proposed algorithm guarantees to bring the local estimates arbitrarily close to one another, developing a "local consensus", which makes it amenable to control by the application of indirect distributed adaptive control schemes. It is also shown through simulations that the proposed algorithm has a clear advantage over the standard projection algorithm. Our proposed algorithm is also suitable for addressing the estimation problem in distributed networks that arise in a variety of applications, such as environment monitoring, target localization and potential sensor network problems.
KW - Complex systems
KW - Distributed parameter estimation
KW - Identification
KW - Interconnected systems
KW - Local consensus
KW - Spatially invariant
KW - Spatiotemporal systems
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U2 - 10.1016/j.automatica.2012.06.047
DO - 10.1016/j.automatica.2012.06.047
M3 - Article
AN - SCOPUS:84865041179
VL - 48
SP - 2079
EP - 2092
JO - Automatica
JF - Automatica
SN - 0005-1098
IS - 9
ER -