Convex Trajectory Optimization for Aerospace Vehicles: Methods and Applications is a comprehensive guide to leveraging convex optimization for solving complex trajectory design problems in aerospace engineering. This book provides a unified treatment of theory, algorithms, and practical applications, showing how convexification and sequential convex programming (SCP) can transform traditionally challenging, nonconvex aerospace trajectory optimization problems into tractable formulations that deliver strong performance. Readers will discover how these methods enable efficient, reliable, and scalable solutions for missions ranging from lunar landings and orbit transfers to atmospheric entry and advanced air mobility operations. Unlike conventional approaches that struggle with computational complexity and sensitivity to initial guesses, convex optimization offers robustness and real-time implementability, which are critical for modern autonomous aerospace systems.

Packed with detailed case studies and implementation strategies, this book bridges cutting-edge research and practical engineering needs. It is an indispensable resource for aerospace engineers, researchers, and graduate students seeking to design next-generation aerospace vehicles that achieve optimal performance under stringent operational constraints.

Zhenbo Wang, Ph.D., is an Associate Professor of Mechanical and Aerospace Engineering at the University of Tennessee, Knoxville. His research focuses on guidance and control (G&C) systems, with particular expertise in trajectory optimization, mission planning, convex optimization methods, and autonomous aerospace systems. Prof. Wang has published extensively in leading journals and conferences on optimal control, sequential convex programming, machine learning, and real-time trajectory generation and mission coordination for space, air, ground, and maritime applications. He earned his Ph.D. in Aerospace Engineering from Purdue University, where his work on advanced trajectory optimization laid the foundation for several innovative algorithms now used in aerospace G&C. His research has been supported by major agencies and industry partners, contributing to projects ranging from planetary landing to next-generation air and ground mobility systems. Prof. Wang is passionate about bridging theory and practice and developing computationally efficient algorithms that meet the stringent demands of modern aerospace missions. He actively collaborates with government agencies, industry partners, and academic institutions to advance autonomy and resilience in aerospace systems. In addition to his research, he is dedicated to mentoring graduate students and educating the next generation of engineers in aerospace and transportation systems.