Trajectory Design Employing Convex Optimization for Landing on Irregularly Shaped Asteroids
| Autor: | Robin M. Pinson, Ping Lu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Engineering Optimization problem Soft landing Computer science Aerospace Engineering Thrust 02 engineering and technology 01 natural sciences law.invention Nonlinear programming 020901 industrial engineering & automation 0203 mechanical engineering Control theory law 0103 physical sciences Initial value problem Cartesian coordinate system Quadratic programming Electrical and Electronic Engineering Aerospace engineering 010303 astronomy & astrophysics 020301 aerospace & aeronautics Spacecraft business.industry Applied Mathematics Trajectory optimization Optimal control Space and Planetary Science Control and Systems Engineering Asteroid Physics::Space Physics Convex optimization Trajectory Astrophysics::Earth and Planetary Astrophysics business |
| Zdroj: | Journal of Guidance, Control, and Dynamics. 41:1243-1256 |
| ISSN: | 1533-3884 0731-5090 |
| DOI: | 10.2514/1.g003045 |
| Popis: | Mission proposals that land on asteroids are becoming popular. However, in order to have a successful mission the spacecraft must reliably and softly land at the intended landing site. The problem under investigation is how to design a fuel-optimal powered descent trajectory that can be quickly computed on- board the spacecraft, without interaction from ground control. An optimal trajectory designed immediately prior to the descent burn has many advantages. These advantages include the ability to use the actual vehicle starting state as the initial condition in the trajectory design and the ease of updating the landing target site if the original landing site is no longer viable. For long trajectories, the trajectory can be updated periodically by a redesign of the optimal trajectory based on current vehicle conditions to improve the guidance performance. One of the key drivers for being completely autonomous is the infrequent and delayed communication between ground control and the vehicle. Challenges that arise from designing an asteroid powered descent trajectory include complicated nonlinear gravity fields, small rotating bodies and low thrust vehicles. There are two previous studies that form the background to the current investigation. The first set looked in-depth at applying convex optimization to a powered descent trajectory on Mars with promising results.1, 2 This showed that the powered descent equations of motion can be relaxed and formed into a convex optimization problem and that the optimal solution of the relaxed problem is indeed a feasible solution to the original problem. This analysis used a constant gravity field. The second area applied a successive solution process to formulate a second order cone program that designs rendezvous and proximity operations trajectories.3, 4 These trajectories included a Newtonian gravity model. The equivalence of the solutions between the relaxed and the original problem is theoretically established. The proposed solution for designing the asteroid powered descent trajectory is to use convex optimization, a gravity model with higher fidelity than Newtonian, and an iterative solution process to design the fuel optimal trajectory. The solution to the convex optimization problem is the thrust profile, magnitude and direction, that will yield the minimum fuel trajectory for a soft landing at the target site, subject to various mission and operational constraints. The equations of motion are formulated in a rotating coordinate system and includes a high fidelity gravity model. The vehicle's thrust magnitude can vary between maximum and minimum bounds during the burn. Also, constraints are included to ensure that the vehicle does not run out of propellant, or go below the asteroid's surface, and any vehicle pointing requirements. The equations of motion are discretized and propagated with the trapezoidal rule in order to produce equality constraints for the optimization problem. These equality constraints allow the optimization algorithm to solve the entire problem, without including a propagator inside the optimization algorithm. |
| Databáze: | OpenAIRE |
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