Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective (Emerging Methodologies and Applications in Modelling, Identification and Control)

Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective reports and encourages technology transfer in the field of cooperative control of multi-agent systems. The book deals with UGVs, UAVs, UUVs and spacecraft, and more. It presents an extended exposition of the authors' recent work on all aspects of multi-agent technology. Modelling and cooperative control of multi-agent systems are topics of great interest, across both academia (research and education) and industry (for real applications and end-users). Graduate students and researchers from a wide spectrum of specialties in electrical, mechanical or aerospace engineering fields will use this book as a key resource. Helps shape the reader's understanding of optimal and robust cooperative control design techniques for multi-agent systems Presents new theoretical control challenges and investigates unresolved/open problems Explores future research trends in multi-agent systems Offers a certain amount of analytical mathematics, practical numerical procedures, and actual implementations of some proposed approaches Contents About the authors Preface Acknowledgments 1 Introduction 1.1 Background 1.1.1 Motivations 1.1.2 Control architectures and strategies 1.1.3 Related applications 1.2 Overview of related works 1.2.1 Consensus control 1.2.1.1 Basic concept 1.2.1.2 Optimal cooperative control 1.2.1.3 Robust cooperative control 1.2.2 Formation control 1.2.3 Other related research 1.2.4 Future research topics 1.3 Objectives of this book 1.4 Book outline 2 Preliminaries 2.1 Matrix theory 2.2 Stability theory 2.3 Basic algebraic graph theory 2.3.1 Basic definitions 2.3.2 Graph matrices 2.3.3 Properties 2.4 Useful lemmas on inequalities 3 Optimal consensus control of multiple integrator systems 3.1 Problem formulation 3.2 Optimal consensus control with obstacle avoidance for single-integrator case 3.2.1 Optimal consensus algorithm: single-integrator case 3.2.2 Numerical examples 3.2.2.1 Consensus without obstacles on the trajectories of agents 3.2.2.2 Consensus with multiple obstacles on the trajectories of agents 3.3 Optimal consensus control with obstacle avoidance for double-integrator case 3.3.1 Optimal consensus algorithm: double-integrator case 3.3.2 Numerical examples 3.3.2.1 Consensus without obstacles on the trajectories of the agents 3.3.2.2 Consensus with multiple obstacles on the trajectories of the agents 3.4 Conclusion remarks 4 Optimal cooperative tracking and flocking of multi-agent systems 4.1 Optimal rendezvous and cooperative tracking control with obstacle avoidance 4.1.1 Problem formulation 4.1.2 Optimal rendezvous algorithm with obstacle avoidance 4.1.3 Numerical examples A. Rendezvous without obstacles on the trajectories of agents B. Rendezvous with two obstacles on the trajectory of agents 4.1.4 Extension to cooperative tracking problem with obstacle avoidance 4.1.4.1 Cooperative tracking algorithm with obstacle avoidance 4.1.4.2 Numerical examples A. Cooperative tracking of a reference with constant velocity B. Cooperative tracking of a dynamic reference trajectory 4.2 Optimal flocking control design with obstacle avoidance 4.2.1 Problem formulation 4.2.2 Optimal flocking control algorithm design 4.2.3 Numerical examples A. Flocking with velocity alignment and navigation B. Flocking with velocity alignment, navigation, and cohesion C. Flocking with velocity alignment, navigation, cohesion, and obstacle/ collision avoidance 4.3 Conclusion remarks 5 Optimal formation control of multiple UAVs 5.1 Problem formulation 5.2 Integrated optimal control approach to formation control problem 5.3 Numerical examples 5.3.1 Formation control without obstacles on the trajectories of UAVs 5.3.2 Formation control with two obstacles on the trajectories of UAVs 5.4 Conclusion remarks 6 Optimal coverage control of multi-robot systems 6.1 Problem formulation 6.1.1 Voronoi tessellation and locational optimization 6.1.2 Potential field method for collision avoidance 6.2 Coverage controller design with known density function 6.2.1 Coverage controller design 6.2.2 Numerical examples 6.3 Coverage controller design with density function estimation 6.3.1 Estimation based coverage controller design 6.3.2 Numerical examples 6.4 Conclusion remarks 7 Robust consensus control of multi-agent systems with input delay 7.1 Problem formulation 7.2 Consensus of Lipschitz nonlinear systems with input delay: model reduction method 7.2.1 Stability analysis for single nonlinear system 7.2.2 Consensus analysis 7.2.3 Controller design 7.3 Consensus of Lipschitz nonlinear systems with input delay: truncated predictor feedback method 7.3.1 Finite-dimensional consensus controller design 7.3.2 Overview of truncated predictor feedback approach 7.3.3 Consensus analysis 7.4 Numerical examples 7.4.1 A circuit example 7.4.2 Numerical examples 7.5 Conclusion remarks 8 Robust consensus control of multi-agent systems with disturbance rejection 8.1 Problem formulation 8.2 Disturbance rejection for a directed graph 8.2.1 Consensus controller design with disturbance rejection 8.2.2 Consensus analysis 8.3 Fully distributed consensus disturbance rejection 8.3.1 Local information based disturbance rejection controller 8.3.2 Consensus analysis 8.4 Disturbance rejection in leader-follower format 8.4.1 Leader-follower disturbance rejection controller 8.4.2 Consensus analysis 8.5 Numerical examples 8.6 Conclusion remarks 9 Robust consensus control of nonlinear odd power integrator systems 9.1 Problem formulation 9.2 Distributed controller for nonlinear odd power integrator systems 9.3 Numerical examples 9.3.1 Perturbed case 9.3.2 Perturbation-free case 9.4 Conclusion remarks 10 Robust cooperative control of networked negative-imaginary systems 10.1 Problem formulation 10.1.1 NI system definition 10.1.2 Typical results for NI systems 10.2 Robust consensus control for multi-NI systems 10.2.1 Homogeneous NI systems 10.2.2 Robust output feedback consensus algorithm for homogeneous multi-agent systems 10.2.3 Convergence set study 10.2.4 Numerical examples 10.2.4.1 Multiple single-integrator systems 10.2.4.2 Multiple double-integrator systems 10.2.4.3 Multiple flexible structures systems 10.3 Robust consensus control of heterogeneous multi-NI systems 10.3.1 Heterogeneous NI systems 10.3.2 Robust output feedback consensus algorithm for heterogeneous multi-agent systems 10.3.2.1 NI plants without free body dynamics 10.3.2.2 NI plants with free body dynamics 10.3.3 Numerical examples 10.3.3.1 Two lightly damped and one undamped flexible structures 10.3.3.2 One single integrator, one double integrator, one undamped and one lightly damped flexible structure 10.4 Extension to robust cooperative control 10.5 Conclusion remarks Bibliography Index __Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective__ reports and encourages technology transfer in the field of cooperative control of multi-agent systems. The book deals with UGVs, UAVs, UUVs and spacecraft, and more. It presents an extended exposition of the authors' recent work on all aspects of multi-agent technology. Modelling and cooperative control of multi-agent systems are topics of great interest, across both academia (research and education) and industry (for real applications and end-users). Graduate students and researchers from a wide spectrum of specialties in electrical, mechanical or aerospace engineering fields will use this book as a key resource.