Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems - Brossura

Libro 23 di 63: Springer Aerospace Technology

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun

 
9789811398476: Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems

Sinossi

This book explores the design of optimal trajectories for space maneuver vehicles (SMVs) using optimal control-based techniques. It begins with a comprehensive introduction to and overview of three main approaches to trajectory optimization, and subsequently focuses on the design of a novel hybrid optimization strategy that combines an initial guess generator with an improved gradient-based inner optimizer. Further, it highlights the development of multi-objective spacecraft trajectory optimization problems, with a particular focus on multi-objective transcription methods and multi-objective evolutionary algorithms. In its final sections, the book studies spacecraft flight scenarios with noise-perturbed dynamics and probabilistic constraints, and designs and validates new chance-constrained optimal control frameworks. 

The comprehensive and systematic treatment of practical issues in spacecraft trajectory optimization is one of the book’s major features, making it particularly suited for readers who are seeking practical solutions in spacecraft trajectory optimization. It offers a valuable asset for researchers, engineers, and graduate students in GNC systems, engineering optimization, applied optimal control theory, etc.

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Informazioni sull?autore

Dr. Runqi Chai’s research interests include spacecraft trajectory optimization, multi-objective optimization, optimal control theory, model predictive control and chance-constrained optimization. Currently he serves as an active reviewer for several international publications including IEEE and AIAA.


The list of Runqi Chai’s publications is as follows:
R. Chai, A. Savvaris, A. Tsourdos, S. Chai, and Y. Xia, Trajectory Optimization of Space Maneuver Vehicle Using a Hybrid Optimal Control Solver. IEEE Transactions on Cybernetics, 2019, vol. 49, no. 2, pp. 467-480
R. Chai, A. Savvaris, and S. Chai*, Integrated missile guidance and control using optimization-based predictive control, Nonlinear Dynamics, 2019, 1-17, Available online.
R. Chai, A. Savvaris, A. Tsourdos, S. Chai*, and Y. Xia, Optimal fuel consumption finite-thrust orbital hopping of aeroassisted spacecraft, Aerospace Science and Technology, 2018, vol. 75, no. 4, pp. 172-182

Dalla quarta di copertina

This book explores the design of optimal trajectories for space maneuver vehicles (SMVs) using optimal control-based techniques. It begins with a comprehensive introduction to and overview of three main approaches to trajectory optimization, and subsequently focuses on the design of a novel hybrid optimization strategy that combines an initial guess generator with an improved gradient-based inner optimizer. Further, it highlights the development of multi-objective spacecraft trajectory optimization problems, with a particular focus on multi-objective transcription methods and multi-objective evolutionary algorithms. In its final sections, the book studies spacecraft flight scenarios with noise-perturbed dynamics and probabilistic constraints, and designs and validates new chance-constrained optimal control frameworks. 
The comprehensive and systematic treatment of practical issues in spacecraft trajectory optimization is one of the book’s major features, making it particularly suited for readers who are seeking practical solutions in spacecraft trajectory optimization. It offers a valuable asset for researchers, engineers, and graduate students in GNC systems, engineering optimization, applied optimal control theory, etc.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Altre edizioni note dello stesso titolo

9789811398445: Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems

Edizione in evidenza

ISBN 10:  9811398445 ISBN 13:  9789811398445
Casa editrice: Springer Nature, 2019
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