A unified framework for iterative learning formation control of multi-agent systems Iterative Learning Formation Control of Nonlinear Multi-agent Systems applies iterative learning control to distributed formation problems. Written by Yang Liu, a researcher with more than 90 publications, this book integrates distributed cooperative, intelligent control, and engineering applications. The book develops ILC-based formation protocol designs for agents governed by complex nonlinear dynamics operating under significant uncertainties, with coverage on adaptive ILC protocols for randomly varying trial lengths, rigorous convergence analysis establishing conditions under which formation errors converge to zero or remain bounded, and detailed simulations demonstrating performance across application domains including spacecraft formation flying, unmanned aerial vehicle swarms, and cooperative mobile robot tasks. Readers will discover: Methods for comprehending the formation control for agents with complex nonlinear dynamicsAdaptive iterative learning control protocols designed to cope effectively with randomly varying trial lengths across repeated operationsConvergence analysis establishing precise conditions under which formation tracking errors converge to zero or remain within bounded rangesDetailed simulation studies illustrating protocol performance across spacecraft formation flying, UAV swarms, and cooperative mobile roboticsDesigned for researchers, graduate students, and practitioners in control theory and aerospace engineering, robotics, and intelligent transportation systems, this book provides the theoretical foundations and analytical tools needed to design and verify iterative learning formation control protocols for autonomous multi-agent systems executing repetitive tasks.

A unified framework for iterative learning formation control of multi-agent systems Iterative Learning Formation Control of Nonlinear Multi-agent Systems applies iterative learning control to distributed formation problems. Written by Yang Liu, a researcher with more than 90 publications, this book integrates distributed cooperative, intelligent control, and engineering applications. The book develops ILC-based formation protocol designs for agents governed by complex nonlinear dynamics operating under significant uncertainties, with coverage on adaptive ILC protocols for randomly varying trial lengths, rigorous convergence analysis establishing conditions under which formation errors converge to zero or remain bounded, and detailed simulations demonstrating performance across application domains including spacecraft formation flying, unmanned aerial vehicle swarms, and cooperative mobile robot tasks. Readers will discover: Methods for comprehending the formation control for agents with complex nonlinear dynamicsAdaptive iterative learning control protocols designed to cope effectively with randomly varying trial lengths across repeated operationsConvergence analysis establishing precise conditions under which formation tracking errors converge to zero or remain within bounded rangesDetailed simulation studies illustrating protocol performance across spacecraft formation flying, UAV swarms, and cooperative mobile roboticsDesigned for researchers, graduate students, and practitioners in control theory and aerospace engineering, robotics, and intelligent transportation systems, this book provides the theoretical foundations and analytical tools needed to design and verify iterative learning formation control protocols for autonomous multi-agent systems executing repetitive tasks.

A unified framework for iterative learning formation control of multi-agent systems Iterative Learning Formation Control of Nonlinear Multi-agent Systems applies iterative learning control to distributed formation problems. Written by Yang Liu, a researcher with more than 90 publications, this book integrates distributed cooperative, intelligent control, and engineering applications. The book develops ILC-based formation protocol designs for agents governed by complex nonlinear dynamics operating under significant uncertainties, with coverage on adaptive ILC protocols for randomly varying trial lengths, rigorous convergence analysis establishing conditions under which formation errors converge to zero or remain bounded, and detailed simulations demonstrating performance across application domains including spacecraft formation flying, unmanned aerial vehicle swarms, and cooperative mobile robot tasks. Readers will discover: Methods for comprehending the formation control for agents with complex nonlinear dynamicsAdaptive iterative learning control protocols designed to cope effectively with randomly varying trial lengths across repeated operationsConvergence analysis establishing precise conditions under which formation tracking errors converge to zero or remain within bounded rangesDetailed simulation studies illustrating protocol performance across spacecraft formation flying, UAV swarms, and cooperative mobile roboticsDesigned for researchers, graduate students, and practitioners in control theory and aerospace engineering, robotics, and intelligent transportation systems, this book provides the theoretical foundations and analytical tools needed to design and verify iterative learning formation control protocols for autonomous multi-agent systems executing repetitive tasks.