Optimal Engine Selection and Trajectory Optimization Using Genetic Algorithms for Conceptual Design Optimization of Reusable Space Launch Vehicles
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Simulation and Application of GPOPS for Trajectory Optimization and Mission Planning Tool Danielle E
Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-10-2010 Simulation and Application of GPOPS for Trajectory Optimization and Mission Planning Tool Danielle E. Yaple Follow this and additional works at: https://scholar.afit.edu/etd Part of the Systems Engineering and Multidisciplinary Design Optimization Commons Recommended Citation Yaple, Danielle E., "Simulation and Application of GPOPS for Trajectory Optimization and Mission Planning Tool" (2010). Theses and Dissertations. 2060. https://scholar.afit.edu/etd/2060 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. SIMULATION AND APPLICATION OF GPOPS FOR A TRAJECTORY OPTIMIZATION AND MISSION PLANNING TOOL THESIS Danielle E. Yaple, 2nd Lieutenant, USAF AFIT/GAE/ENY/10-M29 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT/GAE/ENY/10-M29 SIMULATION AND APPLICATION OF GPOPS FOR A TRAJECTORY OPTIMIZATION AND MISSION PLANNING TOOL THESIS Presented to the Faculty Department of Aeronautics and Astronautics Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Aeronautical Engineering Danielle E. -
Proquest Dissertations
ROBUST MODEL PREDICTIVE CONTROL FOR PROCESS CONTROL AND SUPPLY CHAIN OPTIMIZATION By XIANG LI, B.Eng., M.Eng. A Thesis Submitted to the School of Graduate Studies in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy McMaster University © Copyright by Xiang Li, September 2009 Library and Archives Bibliotheque et 1*1 Canada Archives Canada Published Heritage Direction du Branch Patrimoine de I'edition 395 Wellington Street 395, rue Wellington OttawaONK1A0N4 Ottawa ON K1A 0N4 Canada Canada Your file Votre reference ISBN: 978-0-494-64703-5 Our file Notre reference ISBN: 978-0-494-64703-5 NOTICE: AVIS: The author has granted a non L'auteur a accorde une licence non exclusive exclusive license allowing Library and permettant a la Bibliotheque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par telecommunication ou par Nnternet, preter, telecommunication or on the Internet, distribuer et vendre des theses partout dans le loan, distribute and sell theses monde, a des fins commerciales ou autres, sur worldwide, for commercial or non support microforme, papier, electronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette these. Ni thesis. Neither the thesis nor la these ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent etre imprimes ou autrement printed or otherwise reproduced reproduits sans son autorisation. -
Convex Optimization for Trajectory Generation Arxiv:2106.09125V1 [Math.OC] 16 Jun 2021
Preprint Convex Optimization for Trajectory Generation A Tutorial On Generating Dynamically Feasible Trajectories Reliably And Efficiently Danylo Malyuta a,? , Taylor P. Reynolds a, Michael Szmuk a, Thomas Lew b, Riccardo Bonalli b, Marco Pavone b, and Behc¸et Ac¸ıkmes¸e a a William E. Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, WA 98195, USA b Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA Reliable and efficient trajectory generation methods are a fundamental need for autonomous dynamical systems of tomorrow. The goal of this article is to provide a comprehensive tutorial of three major con- vex optimization-based trajectory generation methods: lossless convexification (LCvx), and two sequential convex programming algorithms known as SCvx and GuSTO. In this article, trajectory generation is the computation of a dynamically feasible state and control signal that satisfies a set of constraints while opti- mizing key mission objectives. The trajectory generation problem is almost always nonconvex, which typ- ically means that it is not readily amenable to efficient and reliable solution onboard an autonomous ve- hicle. The three algorithms that we discuss use problem reformulation and a systematic algorithmic strat- egy to nonetheless solve nonconvex trajectory generation tasks through the use of a convex optimizer. The theoretical guarantees and computational speed offered by convex optimization have made the algo- rithms popular in both research and industry circles. To date, the list of applications include rocket land- ing, spacecraft hypersonic reentry, spacecraft rendezvous and docking, aerial motion planning for fixed- wing and quadrotor vehicles, robot motion planning, and more. Among these applications are high-profile rocket flights conducted by organizations like NASA, Masten Space Systems, SpaceX, and Blue Origin. -
Opty: Software for Trajectory Optimization and Parameter Identification Using Direct Collocation
opty: Software for trajectory optimization and parameter identification using direct collocation Jason K. Moore1 and Antonie van den Bogert2 DOI: 10.21105/joss.00300 1 University of California, Davis 2 Cleveland State University Software • Review Summary • Repository • Archive opty is a tool for describing and solving trajectory optimization and parameter identi- Submitted: 04 June 2017 fication problems based on symbolic descriptions of ordinary differential equations and Published: 30 January 2018 differential algebriac equations that describe a dynamical system. The motivation for its development resides in the need to solve optimal control problems of biomechanical Licence Authors of JOSS papers retain systems. The target audience is engineers and scientists interested in solving nonlinear copyright and release the work un- optimal control and parameter identification problems with minimal computational over- der a Creative Commons Attri- head. bution 4.0 International License A user of opty is responsible for specifying the system’s dynamics (the ODE/DAEs), the (CC-BY). cost or fitness function, bounds on the solution, and the initial guess for the solution. opty uses this problem specification to derive the constraints needed to solve the optimization problem using the direct collocation method (Betts 2010). This method maps the problem to a non-linear programming problem and the result is then solved numerically with an interior point optimizer IPOPT (Wächter and Biegler 2006) which is wrapped by cyipopt (cyipopt Developers 2017) for use in Python. The software allows the user to describe the dynamical system of interest at a high level in symbolic form without needing to concern themselves with the numerical computation details. -
Advances in Trajectory Optimization for Space Vehicle Control
Advances in Trajectory Optimization for Space Vehicle Control Danylo Malyutaa,∗, Yue Yua, Purnanand Elangoa, Beh¸cetA¸cıkme¸sea aWilliam E. Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, WA 98195, USA Abstract Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent ad- vances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control -
Casadi – a Software Framework for Nonlinear Optimization and Optimal Control
Mathematical Programming Computation manuscript No. (will be inserted by the editor) CasADi – A software framework for nonlinear optimization and optimal control Joel A. E. Andersson Joris Gillis Greg Horn James B.· Rawlings Moritz· Diehl · · Received: date / Accepted: date Abstract We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in appli- cations from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years. Keywords Optimization Optimal control Open source optimization software · · Mathematics Subject Classification (2000) 90C99 93A30 97A01 · · J. A. E. Andersson Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA E-mail: [email protected] J. Gillis Production Engineering, Machine Design and Automation (PMA) Section, KU Leuven, Belgium E-mail: [email protected] G. Horn Kitty Hawk, Mountain View, USA E-mail: [email protected] J. B. Rawlings Department of Chemical and Biological Engineering, University of Wisconsin-Madison, USA E-mail: [email protected] M. -
Nonlinear Trajectory Optimization with Path Constraints Applied to Spacecraft Reconfiguration Maneuvers by Ian Miguel Garcia
Nonlinear Trajectory Optimization with Path Constraints Applied to Spacecraft Reconfiguration Maneuvers by Ian Miguel Garcia S.B. Aeronautics and Astronautics, Massachusetts Institute of Technology, 2003 S.B. Mathematics, Massachusetts Institute of Technology, 2003 Submitted to the Department of Aeronautics and Astronautics in partial fulfillment of the requirements for the degree oSCUSTSiS OF TECHNOLOGY Master of Science in Aeronautics and Astronautics JUN 2 3 2005 at the LIBRARI MASSACHUSETTS INSTITUTE OF TECHNOLOG June 2005 @ Massachusetts Institute of Technology 2005. All rights reserved. A uthor .......... ... .. ............... ............. Department of Aeronautics and Astronautics May 20, 2005 C ertified by ................... ......... Jonathan P. How 4ssociate Professor Jlhesis Supervisor Accepted by.............. Jaime Peraire Professor of Aeronautics and Astronautics Chair, Committee on Graduate Students AERO Nonlinear Trajectory Optimization with Path Constraints Applied to Spacecraft Reconfiguration Maneuvers by Ian Miguel Garcia Submitted to the Department of Aeronautics and Astronautics on May 20, 2005, in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics and Astronautics Abstract Future space assembly and science missions (e.g., Terrestrial Planet Finder) will typ- ically require complex reconfiguration maneuvers involving well coordinated 6 DOF motions of the entire fleet of spacecraft. The motions must also satisfy constraints such as collision avoidance and pointing restrictions on some of the instruments. This problem is particularly difficult due to the nonlinearity of the attitude dynamics and the non-convexity of some of the constraints. The coupling of the positions and atti- tudes of the N spacecraft by some of the constraints adds a significant complication because it requires that the trajectory optimization be solved as a single 6N DOF problem. -
An Introduction to Trajectory Optimization: How to Do Your Own Direct Collocation ∗
AN INTRODUCTION TO TRAJECTORY OPTIMIZATION: HOW TO DO YOUR OWN DIRECT COLLOCATION ∗ MATTHEW KELLY † Abstract. This paper is an introductory tutorial for numerical trajectory optimization with a focus on direct collocation methods. These methods are relatively simple to understand and effectively solve a wide variety of trajectory optimization problems. Throughout the paper we illustrate each new set of concepts by working through a sequence of four example problems. We start by using trapezoidal collocation to solve a simple one-dimensional toy-problem and work up to using Hermite–Simpson collocation to compute the optimal gait for a bipedal walking robot. Along the way, we cover basic debugging strategies and guidelines for posing well-behaved optimization problems. The paper concludes with a short overview of other methods for trajectory optimization. We also provide an electronic supplement that contains well-documented Matlab code for all examples and methods presented in this paper. Our primary goal is to provide the reader with the resources necessary to understand and successfully implement their own direct collocation methods. 1. Introduction. What is trajectory optimization? Let’s start with an example: imagine a satellite moving between two planets. We would use the term trajectory to describe the path the the satellite takes between the two planets. Usually, this path would include both state (e.g. position and velocity) and control (e.g. thrust) as functions of time. The term trajectory optimization refers to a set of methods that are used to find the best choice of trajectory, typically by selecting the inputs to the system, known as controls, as functions of time.