Flight Test Techniques Used To Evaluate Performance Benefits During Formation Flight

This book reports on advances in sensing, modeling and control methods for different robotic platforms such as multi-degree of freedom robotic arms, unmanned aerial vehicles and autonomous mobile platforms. Based on 2018 Symposium on Mechatronics, Robotics, and Control (SMTRC’18), held as part of the 2018 CSME International Congress, in York University, Toronto, Canada, the book covers a variety of topics, from filtering and state estimation to adaptive control of reconfigurable robots and more. Next-generation systems with advanced control, planning, perception and interaction capabilities will achieve functionalities far beyond today’s technology. Two key challenges remaining for advanced robot technologies are related to sensing and control in robotic systems. Advanced perception is needed to navigate changing environments. Adaptive and intelligent control systems must be developed to enable operation in unstructured and dynamic environments. The selected chapters in this book focus on both of the aforementioned areas and highlight the main trends and challenges in robot sensing and control. The first part of the book introduces chapters which focus on advanced perception and sensing for robotics applications. They include sensor filtering and state estimation for bipedal robots and motion capture systems analysis. The second part focuses on different modeling and control methods for robotic systems including flight control for UAVs, multi-variable robust control for modular and reconfigurable robotics and control for precision micromanipulation.

This text explores formation control of vehicle systems and introduces three representative systems: space systems, aerial systems and robotic systems Formation Control of Multiple Autonomous Vehicle Systems offers a review of the core concepts of dynamics and control and examines the dynamics and control aspects of formation control in order to study a wide spectrum of dynamic vehicle systems such as spacecraft, unmanned aerial vehicles and robots. The text puts the focus on formation control that enables and stabilizes formation configuration, as well as formation reconfiguration of these vehicle systems. The authors develop a uniform paradigm of describing vehicle systems’ dynamic behaviour that addresses both individual vehicle’s motion and overall group’s movement, as well as interactions between vehicles. The authors explain how the design of proper control techniques regulate the formation motion of these vehicles and the development of a system level decision-making strategy that increases the level of autonomy for the entire group of vehicles to carry out their missions. The text is filled with illustrative case studies in the domains of space, aerial and robotics. • Contains uniform coverage of "formation" dynamic systems development • Presents representative case studies in selected applications in the space, aerial and robotic systems domains • Introduces an experimental platform of using laboratory three-degree-of-freedom helicopters with step-by-step instructions as an example • Provides open source example models and simulation codes • Includes notes and further readings that offer details on relevant research topics, recent progress and further developments in the field Written for researchers and academics in robotics and unmanned systems looking at motion synchronization and formation problems, Formation Control of Multiple Autonomous Vehicle Systems is a vital resource that explores the motion synchronization and formation control of vehicle systems as represented by three representative systems: space systems, aerial systems and robotic systems.

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

This book focuses on analysis and design problems for high-order linear time-invariant (LTI) swarm systems (multi-agent systems) to achieve consensus, formation, containment and formation-containment. As a first step, the concepts of practical consensus and formation-containment are introduced. Unlike previous research, the formation in this book can be time-varying. A general framework for consensus, consensus tracking, formation, containment and state formation-containment is presented for the first time. Sufficient/necessary and sufficient conditions, and approaches to designing the protocols for swarm systems to achieve these control objectives, are respectively proposed. Autonomous time-varying formation experiments using five quadrotor unmanned aerial vehicles (UAVs) are conducted in an outdoor setting to demonstrate the theoretical results.