Abstract
A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.
Highlights
Since the retirement of space shuttle, many countries start to develop the 2nd generation reusable launch vehicles (RLVs), which are more autonomous and reliable
The results show that Artificial-memory-principle optimization (AMPO) has the ability of high convergence speed, which is very critical for RLV for trajectory design
This paper proposed a AMPO algorithm for RLV to design the re-entry trajectory, and make a comparison with differential evolution (DE) and genetic algorithm (GA)
Summary
Since the retirement of space shuttle, many countries start to develop the 2nd generation reusable launch vehicles (RLVs), which are more autonomous and reliable. In order to achieve a much wider adaptability, these algorithms abandon mathematical or physical searching ways, and turn to imitate the behaviors of creatures or phenomenon in nature. Their searching process seems to be fuzzy or random, researches showed NIAs have a global convergence. These algorithms have a large family, including artificial neural network (ANN) [3,4], ant colony algorithm (ACA) [5], genetic algorithm (GA) [6], differential evolution (DE) [7] and particle swarm optimization (PSO) [8], and so on.
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