Symplectic Pseudospectral Methods for Optimal Control

This work explores symplectic pseudospectral methods for nonlinear optimal control, addressing complex factors and offering practical applications, notably in autonomous vehicle path planning.

This book delves into the realm of symplectic pseudospectral methods specifically tailored for nonlinear optimal control problems. It addresses the challenges posed by complicated factors such as inequality constraints, state-delay, and unspecific terminal time, all within the framework of indirect methods. By exploring these advanced techniques, the text aims to provide a comprehensive understanding of how these methods can be effectively applied in various scenarios.

In Symplectic Pseudospectral Methods for Optimal Control, both theoretical foundations and practical applications are thoroughly examined. The author emphasizes the significance of computational efficiency and accuracy, highlighting how these symplectic methods outperform traditional direct pseudospectral approaches. This dual focus ensures that readers not only grasp the underlying principles but also see their relevance in real-world engineering problems.

The book is particularly valuable for those involved in path planning for autonomous vehicles, showcasing how these methods can be instrumental in addressing optimal control problems in this emerging field. Researchers, engineers, and graduate students in automatic control, path planning, and ordinary differential equations will find this work beneficial, as it bridges the gap between theory and practice in a highly relevant area of study.