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THRUST CONTROL OF AN ELECTRIC PROPULSION SPACE VEHICLE WITH MINIMAL FUEL CONSUMPTION

机译:耗油量最小的电动推进空间车辆的推力控制

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The objective of this research is to develop a heuristic process to obtain a near optimal control strategy for an electric-propulsion spacecraft. The work reported here in particular uses a genetic algorithm (GA) to develop a control profile for an ionic thruster for a spacecraft such as the Jupiter icy Moons Orbiter (JIMO), although the methodology could be used for any deep-space mission using ionic thrusters. The spacecraft's mission is to travel between two points in space with high fuel efficiency, to intercept the target at the end of the predefined time frame with a predefined velocity, and to satisfy certain constraints on thrusting or coasting duration. Control strategics are represented as chromosomes, and a GA is used to find the best chromosome among the candidate solutions. By using intermittent low thrusts, simulations have JIMO satisfying mission objectives. The fitness function used to obtain the control strategies minimizes the final-state error and the fuel consumption; this function is simple and effective. The final, comprehensive control strategy, however, is sensitive to the under-performance of JIMO's propulsion system, indicating how critical the performance of JIMO's engine and actuator is for the outcome of the mission.
机译:这项研究的目的是开发一种启发式方法,以获得电动推进器的接近最佳控制策略。此处报道的工作特别使用遗传算法(GA)来为航天飞机(如木星冰月卫星(JIMO))的离子推进器开发控制配置文件,尽管该方法可以用于使用离子的任何深空任务推进器。航天器的任务是在高燃油效率的空间中在两个点之间旅行,以预定的速度在预定的时间范围结束时拦截目标,并满足对推力或滑行持续时间的某些限制。控制策略以染色体表示,遗传算法用于在候选解决方案中找到最佳染色体。通过使用间歇性低推力,JIMO仿真可以满足任务目标。用于获得控制策略的适应度函数可将最终状态误差和燃油消耗降至最低。此功能简单有效。但是,最终的综合控制策略对JIMO推进系统的性能欠佳很敏感,这表明JIMO的发动机和执行器的性能对于任务的完成至关重要。

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