Low-Thrust Spacecraft Transfer Orbit Program

Low-Thrust Spacecraft Transfer Orbit Program Overview

Low-thrust spacecraft typically employ electric propulsion systems and other low-thrust engines. Compared to traditional chemical propulsion, they generate smaller thrust but offer higher specific impulse, making them suitable for long-duration, high-efficiency orbital transfer missions. The core challenge in calculating low-thrust transfer orbits lies in solving optimal control problems to achieve objectives such as minimum fuel consumption or shortest transfer time.

Program Implementation Methodology

Dynamics Modeling: Establish spacecraft motion equations based on two-body problems or high-precision dynamic models, incorporating continuous low-thrust effects using differential equation formulations like ode45 solvers.

Optimal Control Problem: Typically employs indirect methods (e.g., Pontryagin's maximum principle) or direct methods (e.g., pseudospectral methods) to transform the problem into parameter optimization, implemented through MATLAB's symbolic math toolbox for Hamiltonian derivation.

Numerical Solution: Utilizes MATLAB boundary value problem solvers (bvp4c/bvp5c) or optimization tools (fmincon) for trajectory computation, often requiring iterative calculations of co-state variables with convergence tolerance settings.

Visualization Output: Generates plots for transfer trajectory curves, thrust direction angle variations, and state variable (position/velocity) evolution over time using MATLAB's plotting functions with customized axis labels and animation capabilities.

Extended Applications

Multi-objective Optimization: Balancing fuel consumption versus transfer time using Pareto front algorithms Constraint Handling: Obstacle avoidance maneuvers or specific illumination condition satisfaction through nonlinear constraint programming Deep Space Mission Extension: Incorporating gravity assists or hybrid low-thrust/impulse control schemes with event detection functions

Note: Practical implementation requires attention to initial value sensitivity and computational convergence issues, addressable through phased optimization or genetic algorithm pre-optimization techniques like gamultiobj for robust solutions.