{"product_id":"cooperative-control-of-distributed-multi-agent-systems-hardback-9780470060315","title":"Cooperative Control of Distributed Multi-Agent Systems (Hardback) 9780470060315","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCooperative Control of Distributed Multi-Agent Systems\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJeff Shamma (Edited by), J Shamma (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470060315, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 7 December 2007\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e464 pages\u003cbr\u003e25 x 17.4 x 3 cm, 0.948 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe 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.  \u003cp\u003eCooperative Control of Distributed Multi-Agent Systems is organized into four main themes, or dimensions, of cooperative control: distributed control and computation, adversarial interactions, uncertain evolution and complexity management. The military application of autonomous vehicles systems or multiple unmanned vehicles is primarily targeted; however much of the material is relevant to a broader range of multi-agent systems including cooperative robotics, distributed computing, sensor networks and data network congestion control.\u003c\/p\u003e \u003cp\u003eCooperative Control of Distributed Multi-Agent Systems offers the reader an organized presentation of a variety of recent research advances, supporting software and experimental data on the resolution of the cooperative control problem. It will appeal to senior academics, researchers and graduate students as well as engineers working in the areas of cooperative systems, control and optimization.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eList of Contributors xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Dimensions of cooperative control 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJeff S. Shamma and Gurdal Arslan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Why cooperative control? 3\u003c\/p\u003e \u003cp\u003e1.1.1 Motivation 3\u003c\/p\u003e \u003cp\u003e1.1.2 Illustrative example: command and control of networked vehicles 4\u003c\/p\u003e \u003cp\u003e1.2 Dimensions of cooperative control 5\u003c\/p\u003e \u003cp\u003e1.2.1 Distributed control and computation 5\u003c\/p\u003e \u003cp\u003e1.2.2 Adversarial interactions 11\u003c\/p\u003e \u003cp\u003e1.2.3 Uncertain evolution 14\u003c\/p\u003e \u003cp\u003e1.2.4 Complexity management 15\u003c\/p\u003e \u003cp\u003e1.3 Future directions 16\u003c\/p\u003e \u003cp\u003eAcknowledgements 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Distributed Control and Computation 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Design of behavior of swarms: From flocking to data fusion using microfilter networks 21\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eReza Olfati-Saber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Consensus problems 22\u003c\/p\u003e \u003cp\u003e2.3 Flocking behavior for distributed coverage 25\u003c\/p\u003e \u003cp\u003e2.3.1 Collective potential of flocks 27\u003c\/p\u003e \u003cp\u003e2.3.2 Distributed flocking algorithms 29\u003c\/p\u003e \u003cp\u003e2.3.3 Stability analysis for flocking motion 30\u003c\/p\u003e \u003cp\u003e2.3.4 Simulations of flocking 32\u003c\/p\u003e \u003cp\u003e2.4 Microfilter networks for cooperative data fusion 32\u003c\/p\u003e \u003cp\u003eAcknowledgements 39\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Connectivity and convergence of formations 43\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSonja Glavaˇski, Anca Williams and Tariq Samad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 43\u003c\/p\u003e \u003cp\u003e3.2 Problem formulation 44\u003c\/p\u003e \u003cp\u003e3.3 Algebraic graph theory 46\u003c\/p\u003e \u003cp\u003e3.4 Stability of vehicle formations in the case of time-invariant communication 48\u003c\/p\u003e \u003cp\u003e3.4.1 Formation hierarchy 48\u003c\/p\u003e \u003cp\u003e3.5 Stability of vehicle formations in the case of time-variant communication 54\u003c\/p\u003e \u003cp\u003e3.6 Stabilizing feedback for the time-variant communication case 57\u003c\/p\u003e \u003cp\u003e3.7 Graph connectivity and stability of vehicle formations 58\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 60\u003c\/p\u003e \u003cp\u003eAcknowledgements 60\u003c\/p\u003e \u003cp\u003eReferences 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Distributed receding horizon control: stability via move suppression 63\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWilliam B. Dunbar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 63\u003c\/p\u003e \u003cp\u003e4.2 System description and objective 64\u003c\/p\u003e \u003cp\u003e4.3 Distributed receding horizon control 68\u003c\/p\u003e \u003cp\u003e4.4 Feasibility and stability analysis 72\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 76\u003c\/p\u003e \u003cp\u003eAcknowledgement 76\u003c\/p\u003e \u003cp\u003eReferences 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Distributed predictive control: synthesis, stability and feasibility 79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTam´as Keviczky, Francesco Borrelli and Gary J. Balas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 79\u003c\/p\u003e \u003cp\u003e5.2 Problem formulation 81\u003c\/p\u003e \u003cp\u003e5.3 Distributed MPC scheme 83\u003c\/p\u003e \u003cp\u003e5.4 DMPC stability analysis 85\u003c\/p\u003e \u003cp\u003e5.4.1 Individual value functions as Lyapunov functions 87\u003c\/p\u003e \u003cp\u003e5.4.2 Generalization to arbitrary number of nodes and graph 89\u003c\/p\u003e \u003cp\u003e5.4.3 Exchange of information 90\u003c\/p\u003e \u003cp\u003e5.4.4 Stability analysis for heterogeneous unconstrained LTI subsystems 91\u003c\/p\u003e \u003cp\u003e5.5 Distributed design for identical unconstrained LTI subsystems 93\u003c\/p\u003e \u003cp\u003e5.5.1 LQR properties for dynamically decoupled systems 95\u003c\/p\u003e \u003cp\u003e5.5.2 Distributed LQR design 98\u003c\/p\u003e \u003cp\u003e5.6 Ensuring feasibility 102\u003c\/p\u003e \u003cp\u003e5.6.1 Robust constraint fulfillment 102\u003c\/p\u003e \u003cp\u003e5.6.2 Review of methodologies 103\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 106\u003c\/p\u003e \u003cp\u003eReferences 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Task assignment for mobile agents 109\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrandon J. Moore and Kevin M. Passino\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 109\u003c\/p\u003e \u003cp\u003e6.2 Background 111\u003c\/p\u003e \u003cp\u003e6.2.1 Primal and dual problems 111\u003c\/p\u003e \u003cp\u003e6.2.2 Auction algorithm 113\u003c\/p\u003e \u003cp\u003e6.3 Problem statement 115\u003c\/p\u003e \u003cp\u003e6.3.1 Feasible and optimal vehicle trajectories 115\u003c\/p\u003e \u003cp\u003e6.3.2 Benefit functions 117\u003c\/p\u003e \u003cp\u003e6.4 Assignment algorithm and results 118\u003c\/p\u003e \u003cp\u003e6.4.1 Assumptions 118\u003c\/p\u003e \u003cp\u003e6.4.2 Motion control for a distributed auction 119\u003c\/p\u003e \u003cp\u003e6.4.3 Assignment algorithm termination 120\u003c\/p\u003e \u003cp\u003e6.4.4 Optimality bounds 124\u003c\/p\u003e \u003cp\u003e6.4.5 Early task completion 128\u003c\/p\u003e \u003cp\u003e6.5 Simulations 130\u003c\/p\u003e \u003cp\u003e6.5.1 Effects of delays 130\u003c\/p\u003e \u003cp\u003e6.5.2 Effects of bidding increment 132\u003c\/p\u003e \u003cp\u003e6.5.3 Early task completions 133\u003c\/p\u003e \u003cp\u003e6.5.4 Distributed vs. centralized computation 134\u003c\/p\u003e \u003cp\u003e6.6 Conclusions 136\u003c\/p\u003e \u003cp\u003eAcknowledgements 137\u003c\/p\u003e \u003cp\u003eReferences 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 On the value of information in dynamic multiple-vehicle routing problems 139\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlessandro Arsie, John J. Enright and Emilio Frazzoli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 139\u003c\/p\u003e \u003cp\u003e7.2 Problem formulation 141\u003c\/p\u003e \u003cp\u003e7.3 Control policy description 144\u003c\/p\u003e \u003cp\u003e7.3.1 A control policy requiring no explicit communication: the unlimited sensing capabilities case 144\u003c\/p\u003e \u003cp\u003e7.3.2 A control policy requiring communication among closest neighbors: the limited sensing capabilities case 145\u003c\/p\u003e \u003cp\u003e7.3.3 A sensor-based control policy 148\u003c\/p\u003e \u003cp\u003e7.4 Performance analysis in light load 150\u003c\/p\u003e \u003cp\u003e7.4.1 Overview of the system behavior in the light load regime 150\u003c\/p\u003e \u003cp\u003e7.4.2 Convergence of reference points 152\u003c\/p\u003e \u003cp\u003e7.4.3 Convergence to the generalized median 156\u003c\/p\u003e \u003cp\u003e7.4.4 Fairness and efficiency 157\u003c\/p\u003e \u003cp\u003e7.4.5 A comparison with algorithms for vector quantization and centroidal Voronoi tessellations 160\u003c\/p\u003e \u003cp\u003e7.5 A performance analysis for sTP, mTP\/FG and mTP policies 161\u003c\/p\u003e \u003cp\u003e7.5.1 The case of sTP policy 161\u003c\/p\u003e \u003cp\u003e7.5.2 The case of mTP\/FG and mTP policies 167\u003c\/p\u003e \u003cp\u003e7.6 Some numerical results 169\u003c\/p\u003e \u003cp\u003e7.6.1 Uniform distribution, light load 169\u003c\/p\u003e \u003cp\u003e7.6.2 Non-uniform distribution, light load 169\u003c\/p\u003e \u003cp\u003e7.6.3 Uniform distribution, dependency on the target generation rate 170\u003c\/p\u003e \u003cp\u003e7.6.4 The sTP policy 171\u003c\/p\u003e \u003cp\u003e7.7 Conclusions 172\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimal agent cooperation with local information 177\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEric Feron and Jan DeMot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 177\u003c\/p\u003e \u003cp\u003e8.2 Notation and problem formulation 179\u003c\/p\u003e \u003cp\u003e8.3 Mathematical problem formulation 181\u003c\/p\u003e \u003cp\u003e8.3.1 DP formulation 181\u003c\/p\u003e \u003cp\u003e8.3.2 LP formulation 182\u003c\/p\u003e \u003cp\u003e8.4 Algorithm overview and LP decomposition 184\u003c\/p\u003e \u003cp\u003e8.4.1 Intuition and algorithm overview 184\u003c\/p\u003e \u003cp\u003e8.4.2 LP decomposition 185\u003c\/p\u003e \u003cp\u003e8.5 Fixed point computation 193\u003c\/p\u003e \u003cp\u003e8.5.1 Single agent problem 193\u003c\/p\u003e \u003cp\u003e8.5.2 Mixed forward-backward recursion 194\u003c\/p\u003e \u003cp\u003e8.5.3 Forward recursion 198\u003c\/p\u003e \u003cp\u003e8.5.4 LTI system 199\u003c\/p\u003e \u003cp\u003e8.5.5 Computation of the optimal value function at small separations 202\u003c\/p\u003e \u003cp\u003e8.6 Discussion and examples 205\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 209\u003c\/p\u003e \u003cp\u003eAcknowledgements 209\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multiagent cooperation through egocentric modeling 213\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVincent Pei-wen Seah and Jeff S. Shamma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 213\u003c\/p\u003e \u003cp\u003e9.2 Centralized and decentralized optimization 215\u003c\/p\u003e \u003cp\u003e9.2.1 Markov model 215\u003c\/p\u003e \u003cp\u003e9.2.2 Fully centralized optimization 218\u003c\/p\u003e \u003cp\u003e9.2.3 Fully decentralized optimization 219\u003c\/p\u003e \u003cp\u003e9.3 Evolutionary cooperation 220\u003c\/p\u003e \u003cp\u003e9.4 Analysis of convergence 222\u003c\/p\u003e \u003cp\u003e9.4.1 Idealized iterations and main result 222\u003c\/p\u003e \u003cp\u003e9.4.2 Proof of Theorem 9.4.2 224\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 227\u003c\/p\u003e \u003cp\u003eAcknowledgements 228\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Adversarial Interactions 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multi-vehicle cooperative control using mixed integer linear programming 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMatthew G. Earl and Raffaello D’Andrea\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 233\u003c\/p\u003e \u003cp\u003e10.2 Vehicle dynamics 235\u003c\/p\u003e \u003cp\u003e10.3 Obstacle avoidance 238\u003c\/p\u003e \u003cp\u003e10.4 RoboFlag problems 241\u003c\/p\u003e \u003cp\u003e10.4.1 Defensive Drill 1: one-on-one case 242\u003c\/p\u003e \u003cp\u003e10.4.2 Defensive Drill 2: one-on-one case 247\u003c\/p\u003e \u003cp\u003e10.4.3 \u003ci\u003eND\u003c\/i\u003e-on-\u003ci\u003eNA \u003c\/i\u003ecase 250\u003c\/p\u003e \u003cp\u003e10.5 Average case complexity 251\u003c\/p\u003e \u003cp\u003e10.6 Discussion 254\u003c\/p\u003e \u003cp\u003e10.7 Appendix: Converting logic into inequalities 255\u003c\/p\u003e \u003cp\u003e10.7.1 Equation (10.24) 256\u003c\/p\u003e \u003cp\u003e10.7.2 Equation (10.33) 257\u003c\/p\u003e \u003cp\u003eAcknowledgements 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 LP-based multi-vehicle path planning with adversaries 261\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeorgios C. Chasparis and Jeff S. Shamma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 261\u003c\/p\u003e \u003cp\u003e11.2 Problem formulation 263\u003c\/p\u003e \u003cp\u003e11.2.1 State-space model 263\u003c\/p\u003e \u003cp\u003e11.2.2 Single resource models 264\u003c\/p\u003e \u003cp\u003e11.2.3 Adversarial environment 265\u003c\/p\u003e \u003cp\u003e11.2.4 Model simplifications 265\u003c\/p\u003e \u003cp\u003e11.2.5 Enemy modeling 266\u003c\/p\u003e \u003cp\u003e11.3 Optimization set-up 267\u003c\/p\u003e \u003cp\u003e11.3.1 Objective function 267\u003c\/p\u003e \u003cp\u003e11.3.2 Constraints 268\u003c\/p\u003e \u003cp\u003e11.3.3 Mixed-integer linear optimization 268\u003c\/p\u003e \u003cp\u003e11.4 LP-based path planning 269\u003c\/p\u003e \u003cp\u003e11.4.1 Linear programming relaxation 269\u003c\/p\u003e \u003cp\u003e11.4.2 Suboptimal solution 269\u003c\/p\u003e \u003cp\u003e11.4.3 Receding horizon implementation 270\u003c\/p\u003e \u003cp\u003e11.5 Implementation 271\u003c\/p\u003e \u003cp\u003e11.5.1 Defense path planning 271\u003c\/p\u003e \u003cp\u003e11.5.2 Attack path planning 274\u003c\/p\u003e \u003cp\u003e11.5.3 Simulations and discussion 276\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 278\u003c\/p\u003e \u003cp\u003eAcknowledgements 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Characterization of LQG differential games with different information patterns 281\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAshitosh Swarup and Jason L. Speyer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 281\u003c\/p\u003e \u003cp\u003e12.2 Formulation of the discrete-time LQG game 282\u003c\/p\u003e \u003cp\u003e12.3 Solution of the LQG game as the limit to the LEG Game 283\u003c\/p\u003e \u003cp\u003e12.3.1 Problem formulation of the LEG Game 284\u003c\/p\u003e \u003cp\u003e12.3.2 Solution to the LEG Game problem 285\u003c\/p\u003e \u003cp\u003e12.3.3 Filter properties for small values of \u003ci\u003eθ \u003c\/i\u003e288\u003c\/p\u003e \u003cp\u003e12.3.4 Construction of the LEG equilibrium cost function 290\u003c\/p\u003e \u003cp\u003e12.4 LQG game as the limit of the LEG Game 291\u003c\/p\u003e \u003cp\u003e12.4.1 Behavior of filter in the limit 291\u003c\/p\u003e \u003cp\u003e12.4.2 Limiting value of the cost 291\u003c\/p\u003e \u003cp\u003e12.4.3 Convexity conditions 293\u003c\/p\u003e \u003cp\u003e12.4.4 Results 293\u003c\/p\u003e \u003cp\u003e12.5 Correlation properties of the LQG game filter in the limit 294\u003c\/p\u003e \u003cp\u003e12.5.1 Characteristics of the matrix \u003ci\u003eP\u003c\/i\u003e−1 \u003ci\u003ei Pi \u003c\/i\u003e295\u003c\/p\u003e \u003cp\u003e12.5.2 Transformed filter equations 295\u003c\/p\u003e \u003cp\u003e12.5.3 Correlation properties of \u003ci\u003eε\u003c\/i\u003e2 \u003ci\u003ei \u003c\/i\u003e296\u003c\/p\u003e \u003cp\u003e12.5.4 Correlation properties of \u003ci\u003eε\u003c\/i\u003e1 \u003ci\u003ei \u003c\/i\u003e297\u003c\/p\u003e \u003cp\u003e12.6 Cost function properties—effect of a perturbation in \u003ci\u003eup \u003c\/i\u003e297\u003c\/p\u003e \u003cp\u003e12.7 Performance of the Kalman filtering algorithm 298\u003c\/p\u003e \u003cp\u003e12.8 Comparison with the Willman algorithm 299\u003c\/p\u003e \u003cp\u003e12.9 Equilibrium properties of the cost function: the saddle interval 299\u003c\/p\u003e \u003cp\u003e12.10 Conclusion 300\u003c\/p\u003e \u003cp\u003eAcknowledgements 300\u003c\/p\u003e \u003cp\u003eReferences 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Uncertain Evolution 303\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Modal estimation of jump linear systems: an information theoretic viewpoint 305\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNuno C. Martins and Munther A. Dahleh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Estimation of a class of hidden markov models 305\u003c\/p\u003e \u003cp\u003e13.1.1 Notation 307\u003c\/p\u003e \u003cp\u003e13.2 Problem statement 308\u003c\/p\u003e \u003cp\u003e13.2.1 Main results 308\u003c\/p\u003e \u003cp\u003e13.2.2 Posing the problem statement as a coding paradigm 309\u003c\/p\u003e \u003cp\u003e13.2.3 Comparative analysis with previous work 309\u003c\/p\u003e \u003cp\u003e13.3 Encoding and decoding 310\u003c\/p\u003e \u003cp\u003e13.3.1 Description of the estimator (decoder) 311\u003c\/p\u003e \u003cp\u003e13.4 Performance analysis 312\u003c\/p\u003e \u003cp\u003e13.4.1 An efficient decoding algorithm 312\u003c\/p\u003e \u003cp\u003e13.4.2 Numerical results 314\u003c\/p\u003e \u003cp\u003e13.5 Auxiliary results leading to the proof of theorem 13.4.3 316\u003c\/p\u003e \u003cp\u003eAcknowledgements 319\u003c\/p\u003e \u003cp\u003eReferences 320\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Conditionally-linear filtering for mode estimation in jump-linear systems 323\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDaniel Choukroun and Jason L. Speyer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 323\u003c\/p\u003e \u003cp\u003e14.2 Conditionally-Linear Filtering 324\u003c\/p\u003e \u003cp\u003e14.2.1 Short review of the standard linear filtering problem 324\u003c\/p\u003e \u003cp\u003e14.2.2 The conditionally-linear filtering problem 326\u003c\/p\u003e \u003cp\u003e14.2.3 Discussion 330\u003c\/p\u003e \u003cp\u003e14.3 Mode-estimation for jump-linear systems 333\u003c\/p\u003e \u003cp\u003e14.3.1 Statement of the problem 333\u003c\/p\u003e \u003cp\u003e14.3.2 State-space model for y \u003ci\u003ek \u003c\/i\u003e335\u003c\/p\u003e \u003cp\u003e14.3.3 Development of the conditionally-linear filter 337\u003c\/p\u003e \u003cp\u003e14.3.4 Discussion 340\u003c\/p\u003e \u003cp\u003e14.3.5 Reduced order filter 341\u003c\/p\u003e \u003cp\u003e14.3.6 Comparison with Wonham filter 343\u003c\/p\u003e \u003cp\u003e14.3.7 Case of noisy observations of x\u003ci\u003ek \u003c\/i\u003e345\u003c\/p\u003e \u003cp\u003e14.4 Numerical Example 350\u003c\/p\u003e \u003cp\u003e14.4.1 Gyro failure detection from accurate spacecraft attitude measurements Description 350\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 354\u003c\/p\u003e \u003cp\u003e14.6 Appendix A: Inner product of equation (14.14) 355\u003c\/p\u003e \u003cp\u003e14.7 Appendix B: Development of the filter equations (14.36) to (14.37) 356\u003c\/p\u003e \u003cp\u003eAcknowledgements 358\u003c\/p\u003e \u003cp\u003eReferences 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Cohesion of languages in grammar networks 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eY. Lee, T.C. Collier, C.E. Taylor and E.P. Stabler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 359\u003c\/p\u003e \u003cp\u003e15.2 Evolutionary dynamics of languages 360\u003c\/p\u003e \u003cp\u003e15.3 Topologies of language populations 361\u003c\/p\u003e \u003cp\u003e15.4 Language structure 363\u003c\/p\u003e \u003cp\u003e15.5 Networks induced by structural similarity 365\u003c\/p\u003e \u003cp\u003e15.5.1 Three equilibrium states 366\u003c\/p\u003e \u003cp\u003e15.5.2 Density of grammar networks and language convergence 368\u003c\/p\u003e \u003cp\u003e15.5.3 Rate of language convergence in grammar networks 370\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 372\u003c\/p\u003e \u003cp\u003eAcknowledgements 374\u003c\/p\u003e \u003cp\u003eReferences 374\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Complexity Management 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Complexity management in the state estimation of multi-agent systems 379\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDomitilla Del Vecchio and Richard M. Murray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 379\u003c\/p\u003e \u003cp\u003e16.2 Motivating example 381\u003c\/p\u003e \u003cp\u003e16.3 Basic concepts 384\u003c\/p\u003e \u003cp\u003e16.3.1 Partial order theory 384\u003c\/p\u003e \u003cp\u003e16.3.2 Deterministic transition systems 386\u003c\/p\u003e \u003cp\u003e16.4 Problem formulation 387\u003c\/p\u003e \u003cp\u003e16.5 Problem solution 388\u003c\/p\u003e \u003cp\u003e16.6 Example: the RoboFlag Drill 391\u003c\/p\u003e \u003cp\u003e16.6.1 RoboFlag Drill estimator 392\u003c\/p\u003e \u003cp\u003e16.6.2 Complexity of the RoboFlag Drill estimator 394\u003c\/p\u003e \u003cp\u003e16.6.3 Simulation results 395\u003c\/p\u003e \u003cp\u003e16.7 Existence of discrete state estimators on a lattice 395\u003c\/p\u003e \u003cp\u003e16.8 Extensions to the estimation of discrete and continuous variables 399\u003c\/p\u003e \u003cp\u003e16.8.1 RoboFlag Drill with continuous dynamics 404\u003c\/p\u003e \u003cp\u003e16.9 Conclusion 405\u003c\/p\u003e \u003cp\u003eAcknowledgement 406\u003c\/p\u003e \u003cp\u003eReferences 406\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Abstraction-based command and control with patch models 409\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eV. G. Rao, S. Goldfarb and R. D’Andrea\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 409\u003c\/p\u003e \u003cp\u003e17.2 Overview of patch models 411\u003c\/p\u003e \u003cp\u003e17.3 Realization and verification 415\u003c\/p\u003e \u003cp\u003e17.4 Human and artificial decision-making 419\u003c\/p\u003e \u003cp\u003e17.4.1 Example: the surround behavior 421\u003c\/p\u003e \u003cp\u003e17.5 Hierarchical control 423\u003c\/p\u003e \u003cp\u003e17.5.1 Information content and situation awareness 426\u003c\/p\u003e \u003cp\u003e17.6 Conclusion 429\u003c\/p\u003e \u003cp\u003eReferences 431\u003c\/p\u003e \u003cp\u003eIndex 433\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52256847659288,"sku":"9780470060315","price":116.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470060315.jpg?v=1781275217","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/cooperative-control-of-distributed-multi-agent-systems-hardback-9780470060315","provider":"Freshly Printed Books","version":"1.0","type":"link"}