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Optimal LQG Control Under Delay-Dependent Costly Information
In the design of closed-loop networked control systems (NCSs), induced transmission delay between sensors and the control station is an often present issue which compromises control performance and may even cause instability. A very relevant scenario in which network-induced delay needs to be invest...
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Published in: | IEEE control systems letters 2019-01, Vol.3 (1), p.102-107 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | In the design of closed-loop networked control systems (NCSs), induced transmission delay between sensors and the control station is an often present issue which compromises control performance and may even cause instability. A very relevant scenario in which network-induced delay needs to be investigated is costly usage of communication resources. More precisely, advanced communication technologies, e.g., 5G, are capable of offering latency-varying information exchange for different prices. Therefore, induced delay becomes a decision variable. It is then the matter of decision maker's willingness to either pay the required cost to have lowlatency access to the communication resource, or delay the access at a reduced price. In this letter, we consider optimal price-based bi-variable decision making problem for single-loop NCS with a stochastic linear time-invariant system. Assuming that communication incurs cost such that transmission with shorter delay is more costly, a decision maker determines the switching strategy between communication links of different delays such that an optimal balance between the control performance and the communication cost is maintained. In this letter, we show that, under mild assumptions on the available information for decision makers, the separation property holds between the optimal link selecting and control policies. As the cost function is decomposable, the optimal policies are efficiently computed. |
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ISSN: | 2475-1456 2475-1456 |
DOI: | 10.1109/LCSYS.2018.2853648 |