LEADER 05507nam 2200685 a 450 001 9910830464603321 005 20230721030236.0 010 $a1-281-31911-2 010 $a9786611319113 010 $a0-470-72420-X 010 $a0-470-72419-6 035 $a(CKB)1000000000376958 035 $a(EBL)351037 035 $a(OCoLC)476170259 035 $a(SSID)ssj0000130426 035 $a(PQKBManifestationID)11159706 035 $a(PQKBTitleCode)TC0000130426 035 $a(PQKBWorkID)10080655 035 $a(PQKB)11735367 035 $a(MiAaPQ)EBC351037 035 $a(EXLCZ)991000000000376958 100 $a20071026d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCooperative control of distributed multi-agent systems$b[electronic resource] /$fedited by Jeff S. Shamma 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cJohn Wiley & Sons$dc2007 215 $a1 online resource (453 p.) 300 $aDescription based upon print version of record. 311 $a0-470-06031-X 320 $aIncludes bibliographical references and index. 327 $aCooperative Control of Distributed Multi-Agent Systems; Contents; List of Contributors; Preface; Part I Introduction; 1 Dimensions of cooperative control; 1.1 Why cooperative control?; 1.1.1 Motivation; 1.1.2 Illustrative example: command and control of networked vehicles; 1.2 Dimensions of cooperative control; 1.2.1 Distributed control and computation; 1.2.2 Adversarial interactions; 1.2.3 Uncertain evolution; 1.2.4 Complexity management; 1.3 Future directions; Acknowledgements; References; Part II Distributed Control and Computation 327 $a2 Design of behavior of swarms: From flocking to data fusion using microfilter networks2.1 Introduction; 2.2 Consensus problems; 2.3 Flocking behavior for distributed coverage; 2.3.1 Collective potential of flocks; 2.3.2 Distributed flocking algorithms; 2.3.3 Stability analysis for flocking motion; 2.3.4 Simulations of flocking; 2.4 Microfilter networks for cooperative data fusion; Acknowledgements; References; 3 Connectivity and convergence of formations; 3.1 Introduction; 3.2 Problem formulation; 3.3 Algebraic graph theory 327 $a3.4 Stability of vehicle formations in the case of time-invariant communication3.4.1 Formation hierarchy; 3.5 Stability of vehicle formations in the case of time-variant communication; 3.6 Stabilizing feedback for the time-variant communication case; 3.7 Graph connectivity and stability of vehicle formations; 3.8 Conclusion; Acknowledgements; References; 4 Distributed receding horizon control: stability via move suppression; 4.1 Introduction; 4.2 System description and objective; 4.3 Distributed receding horizon control; 4.4 Feasibility and stability analysis; 4.5 Conclusion; Acknowledgement 327 $aReferences5 Distributed predictive control: synthesis, stability and feasibility; 5.1 Introduction; 5.2 Problem formulation; 5.3 Distributed MPC scheme; 5.4 DMPC stability analysis; 5.4.1 Individual value functions as Lyapunov functions; 5.4.2 Generalization to arbitrary number of nodes and graph; 5.4.3 Exchange of information; 5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems; 5.5 Distributed design for identical unconstrained LTI subsystems; 5.5.1 LQR properties for dynamically decoupled systems; 5.5.2 Distributed LQR design; 5.6 Ensuring feasibility 327 $a5.6.1 Robust constraint fulfillment5.6.2 Review of methodologies; 5.7 Conclusion; References; 6 Task assignment for mobile agents; 6.1 Introduction; 6.2 Background; 6.2.1 Primal and dual problems; 6.2.2 Auction algorithm; 6.3 Problem statement; 6.3.1 Feasible and optimal vehicle trajectories; 6.3.2 Benefit functions; 6.4 Assignment algorithm and results; 6.4.1 Assumptions; 6.4.2 Motion control for a distributed auction; 6.4.3 Assignment algorithm termination; 6.4.4 Optimality bounds; 6.4.5 Early task completion; 6.5 Simulations; 6.5.1 Effects of delays; 6.5.2 Effects of bidding increment 327 $a6.5.3 Early task completions 330 $aThe paradigm of 'multi-agent' cooperative control is the challenge frontier for new control system application domains, and as a research area it has experienced a considerable increase in activity in recent years. This volume, the result of a UCLA collaborative project with Caltech, Cornell and MIT, presents cutting edge results in terms of the "dimensions" of cooperative control from leading researchers worldwide. This dimensional decomposition allows the reader to assess the multi-faceted landscape of cooperative control. Cooperative Control of Distributed Multi-Agent Systems is organized 606 $aDistributed artificial intelligence 606 $aControl theory 606 $aCooperation$xMathematics 606 $aDistributed databases 606 $aElectronic data processing$xDistributed processing 615 0$aDistributed artificial intelligence. 615 0$aControl theory. 615 0$aCooperation$xMathematics. 615 0$aDistributed databases. 615 0$aElectronic data processing$xDistributed processing. 676 $a003.5 676 $a003/.5 701 $aShamma$b Jeff S$01663527 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830464603321 996 $aCooperative control of distributed multi-agent systems$94020888 997 $aUNINA