| 摘要(英) |
In the planning stage of space missions, trajectory optimization plays an important role in planning the flight path that maximum or minimum some quanitities before the mission begins. This type of mathematically problem can be expressed as a continuous time optimal control problem. In recent years, more space missions choose to use electric propulsion for orbital operations. However, this propulsion system is classified into the low thrust propulsion system. The problem of low thrust orbit transition is a difficult problem. Compared with the high thrust spacecraft, the low thrust engine takes more time to complete the mission, and this is the reason that it is difficult to accurately calculate the state of the spacecraft at each time point in mathematical. This thesis is to study how to parallelize the full-space Lagrange-Newton method, to make this algorithm can compute faster for solving the problem of low thrust trajectory problem. When computing KKT systems, different from most methods that dividing the dense matrix evenly, we store the data in a sparse matrix and reduce it by respectively dividing the state variables and the Lagrange multipliers into each computational cores. Reducing the number of data transfers for this algorithm allows the numerical solver to significantly reduce computational time. We take a few two-dimensional low-thrust orbit transfer trajectory optimization problems like the examples to test the feasibility of this algorithm in the application of low thrust transfer. |
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