Distributed Adaptive Consensus Control Of Uncertain Multi Agent Systems
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Distributed Adaptive Consensus Control of Uncertain Multi-Agent Systems
Multi-agent systems are special networked systems full of research interest and practical sense, which are abundant in real life, ranging from mobile robot networks, intelligent transportation management, to multiple spacecraft, surveillance and monitoring. Consensus control is one of the most typical and hot research issues for multi-agent systems. Distributed Adaptive Consensus Control of Uncertain Multi-agent Systems provides innovative technologies to design and analyze distributed adaptive consensus for multi-agent systems with model uncertainties. Based on the basic graph theory and adaptive backstepping control, this monograph: · Describes the state of the art on distributed adaptive control, finite-time consensus control and event-triggered consensus control · Studies distributed adaptive consensus under directed communication graph condition: the methods with linearly parametric reference, hierarchical decomposition, and design of auxiliary filers · Explores adaptive finite-time consensus for uncertain nonlinear systems · Considers distributed adaptive consensus with event-triggered communication via state feedback and output feedback · Investigates distributed adaptive formation control of nonholonomic mobile robots with experimental verification · Provides distributed adaptive attitude synchronization control schemes for multiple spacecraft with event-triggered communication Distributed Adaptive Consensus Control of Uncertain Multi-agent Systems can help engineering students and professionals to efficiently learn distributed adaptive control design tool for handling uncertain multi-agent systems with directed communication graph, guaranteeing finite-time convergence and saving communication resources.
Distributed Adaptive Consensus Control of Uncertain Multi-Agent Systems
Multi-agent systems are special networked systems full of research interest and practical sense, which are abundant in real life, ranging from mobile robot networks, intelligent transportation management, to multiple spacecraft, surveillance and monitoring. Consensus control is one of the most typical and hot research issues for multi-agent systems. Distributed Adaptive Consensus Control of Uncertain Multi-agent Systems provides innovative technologies to design and analyze distributed adaptive consensus for multi-agent systems with model uncertainties. Based on the basic graph theory and adaptive backstepping control, this monograph: · Describes the state of the art on distributed adaptive control, finite-time consensus control and event-triggered consensus control · Studies distributed adaptive consensus under directed communication graph condition: the methods with linearly parametric reference, hierarchical decomposition, and design of auxiliary filers · Explores adaptive finite-time consensus for uncertain nonlinear systems · Considers distributed adaptive consensus with event-triggered communication via state feedback and output feedback · Investigates distributed adaptive formation control of nonholonomic mobile robots with experimental verification · Provides distributed adaptive attitude synchronization control schemes for multiple spacecraft with event-triggered communication Distributed Adaptive Consensus Control of Uncertain Multi-agent Systems can help engineering students and professionals to efficiently learn distributed adaptive control design tool for handling uncertain multi-agent systems with directed communication graph, guaranteeing finite-time convergence and saving communication resources.
Robot Manipulator Control
Robot Manipulator Control offers a complete survey of control systems for serial-link robot arms and acknowledges how robotic device performance hinges upon a well-developed control system. Containing over 750 essential equations, this thoroughly up-to-date Second Edition, the book explicates theoretical and mathematical requisites for controls design and summarizes current techniques in computer simulation and implementation of controllers. It also addresses procedures and issues in computed-torque, robust, adaptive, neural network, and force control. New chapters relay practical information on commercial robot manipulators and devices and cutting-edge methods in neural network control.