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100   $a20200204d2020      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDistributed Average Tracking in Multi-agent Systems /$fby Fei Chen, Wei Ren
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (240 pages)
311   $a3-030-39535-9 
327   $aChapter 1. Overview of Distributed Average Tracking -- Chapter 2. Preliminaries -- Chapter 3. Distributed Average Tracking via Nonsmooth Feedback -- Chapter 4. Distributed Average Tracking via an Extended PI Scheme -- Chapter 5. Distributed Average Tracking for Double-Integrator Dynamics -- Chapter 6. Distributed Average Tracking for General Linear Dynamics -- Chapter 7. Distributed Average Tracking for Euler-Lagrange Dynamics -- Chapter 8. Distributed Average Tracking with Input Saturation.
330   $aThis book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track ? using local communication only ? the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler?Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization. The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry.
606   $aAutomatic control
606   $aSystem theory
606   $aArtificial intelligence
606   $aRobotics
606   $aAutomation
606   $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010
606   $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020
615  0$aAutomatic control.
615  0$aSystem theory.
615  0$aArtificial intelligence.
615  0$aRobotics.
615  0$aAutomation.
615 14$aControl and Systems Theory.
615 24$aSystems Theory, Control.
615 24$aArtificial Intelligence.
615 24$aRobotics and Automation.
676   $a006.30285436
700   $aChen$b Fei$4aut$4http://id.loc.gov/vocabulary/relators/aut$01227128
702   $aRen$b Wei$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910483579803321
996   $aDistributed Average Tracking in Multi-agent Systems$92849398
997   $aUNINA