This book focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.

Introduces different optimization and guidance and control approaches to aerospace vehicle trajectory-related problems Presents new optimization-based approaches for constrained aerospace vehicle guidance and control problems Provides in-depth design and analysis of AI-oriented guidance and control algorithms for aerospace vehicles

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Runqi Chai received the Ph.D. degree in aerospace engineering from Cranfield University, Cranfield, UK, in August 2018. He was Research Fellow at Cranfield University from 2018 to 2021. He was also Visiting Researcher at The University of Manchester, Manchester, UK, in 2021. He is currently Professor at the Beijing Institute of Technology, Beijing, China. His research interests include trajectory optimization, networked control systems, multiagent control systems, and autonomous vehicle motion planning and control. Kaiyuan Chen received the Ph.D. degree from Swansea University in 2022. She serves as Research Fellow at Vanke School of Public Health, Tsinghua University. Currently, she is Member of Institute of Electrical and Electronics Engineers (IEEE) and Chinese Association of Automation (CAA). Her research interests include optimal control, smart health care, global health intelligent governance, the application of unmanned system in public health, andother relevant fields. Lingguo Cui received the Ph.D. degree in computer science and engineering from Beijing Institute of Technology, Beijing, China, in March 2007. She was Visiting Researcher at Cranfield University, Cranfield, UK, in 2016 and at University of Florida, USA, from 2011 to 2012. She is currently Associate Professor at the Beijing Institute of Technology, Beijing, China. Her research interests include information security, wireless sensor networks, optimal control, and optimization theory. Senchun Chai received the B.S. and master's degrees in control science and engineering from the Beijing Institute of Technology, Beijing, China, in 2001 and 2004, respectively, and the Ph.D. degree in networked control system from the University of South Wales, Pontypridd, UK, in 2007. He was Research Fellow at Cranfield University, Bedford, UK, from 2009 to 2010 and was Visiting Scholar at the University of Illinois at Urbana-ChampaignUrbana, Champaign, IL, USA, from January 2010 to May 2010. He is currently Professor at the School of Automation, Beijing Institute of Technology. He has authored and coauthored more than 100 journal and conference papers. His current research interests include flight control system, networked control systems, embedded systems, and multiagent control systems. Gokhan Inalhan received the B.Sc. degree in aeronautical engineering from Istanbul Technical University, in 1997 and the M.Sc. and Ph.D. degrees in aeronautics and astronautics from Stanford University in 1998 and 2004, respectively. He is BAE Systems Chair, Professor of Autonomous Systems and Artificial Intelligence, and Deputy Head of Autonomous and Cyber-physical Systems Centre at Cranfield University, Bedford, UK. He has previously served as Director General with ITU Aerospace Research Centre, Istanbul, Turkey. He has authored or coauthored over 200 papers, book chapters, proceedings, and technical reports on his areas of interest. His research interests include advanced controls, optimization, and modeling aspects associated with autonomy and artificial intelligence for air, space, defense, and transportation systems (urban air mobility, air traffic management/unmanned aerial systems (UAS) traffic management) with seminal works on multiple aircraft coordination and decentralized control of UASs. Antonios Tsourdos received the M.E. degree in electronic, control, and systems engineering from the University of Sheffield, Sheffield, UK, in 1995, the M.Sc. degree in systems engineering from Cardiff University, Cardiff, UK, in 1996, and the Ph.D. degree in nonlinear robust autopilot design and analysis from Cranfield University, Bedford, UK, in 1999. In 1999, he joined the Cranfield University as Lecturer, was appointed Head of the Centre of Autonomous and Cyber-Physical Systems in 2007, Professor of Autonomous Systems and Control in 2009, and Director of Research-Aerospace, Tra

Inhalt
Part I Advanced trajectory optimization methods.- Chapter 1 Review of advanced trajectory optimization methods.- Chapter 2 Heurestic optimization-based trajectory optimization.- Chapter 3 Highly fidelity trajectory optimization.- Chapter 4 Fast trajectory optimization with chance constraints.- Chapter 5 Fast generation of chance-constrained flight trajectory for unmanned vehicles.- Part II Advanced guidance and control methods for aerospace vehicles.- Chapter 6 Review of advanced guidance and control methods.- Chapter 7 Optimization-based predictive G&C method.- Chapter 8 Robust model predictive control for attitude control tracking.