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基于PSO算法的航空发动机起动燃油控制 被引量:5

A new method to control fuel flow of aircraft engine's starting process based on PSO Algorithm
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摘要 针对航空发动机起动过程燃油流量优化控制的实时性要求,提出一种新的航空发动机起动燃油控制方法——基于粒子群优化(PSO)算法的非线性预测控制.该方法在建立最小二乘支持向量机(LS-SVM)预测模型的基础上,运用PSO算法实现其滚动优化功能.经实例验证,燃油流量经过PSO算法优化控制后,高低压转子转速的超调量减小,并且其稳定的时间比没有经过优化控制的要快上5~6s.由仿真结果可知,该方法可以用于航空发动机起动过程燃油控制,当给定的约束条件足够精确时,能以较高的精度计算出最佳供油规律. In order to solve the real time application demand of the fuel flow control in aircraft engine's starting process,a new nonlinear predictive control method based on particle swarm optimization (PSO) algorithm is presented. The method established a predictive model with last suares support vector machines (LS-SVM) ,and then carried out the receding horizon optimization with PSO algorithm. The example validated that the new control method can reduce the rotors' rotating speed fluctuation and the steady state can be attained 5~6 seconds earlier than that without optimization. The result of the emulation shows that the method is suitable for the fuel flow control, and it can achieve the best control pattern of fuel flow when the restriction is provided accurately.
出处 《航空动力学报》 EI CAS CSCD 北大核心 2007年第6期923-927,共5页 Journal of Aerospace Power
关键词 航空 航天推进系统 航空发动机 起动 燃油流量 预测控制 滚动优化 粒子群优化算法 aerospace propulsion system aircraft engine starting fuel flow predictive control receding horizon optimization particle swarm optimization (PSO)
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  • 1Kennedy J, Eberhart R C. Particle swarm optimization[C]. Proc. IEEE Int. Conf. on Neural Networks, Perth, WA,Australia, 1995. 1 942-1 948
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  • 4Dautenhahn K. Book review: Swarm intelligence[J]. Genetic Programming and Evolvable Machines, 2002,3(1): 93-97

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