基于SVR-PSO改进算法的航空发动机稳定性控制

王磊; 谢寿生; 蒋爱武; 苗卓广; 翟旭升

基于SVR-PSO改进算法的航空发动机稳定性控制

1.
空军工程大学工程学院, 西安 710038
2.
中国人民解放军95095部队, 南宁 530048

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出版历程
- 收稿日期: 2011-03-12
- 修回日期: 2011-09-01
- 刊出日期: 2012-02-28

Aero-engine stability seeking control based on improved SVR-PSO

1.
The Engineering Institute, Air Force Engineering University, Xi’an 710038, China
2.
Unit 95095 of Chinese People’s Liberation Army, Nanning 530048, China

摘要

摘要: 在多变量发动机寻优控制中,用支持向量回归算法(SVR)对粒子群优化算法(PSO)进行改进可以有效避免局部最优解的出现.将改进算法应用于航空发动机实时稳定性控制,根据发动机仿真计算程序计算出发动机在各工作点处的稳定裕度,根据控制参数的变化域进行全局寻优,寻找满足压缩系统稳定裕度最小的工作点.仿真和分析表明:该算法实时性高,收敛速度快,具有较强的全局寻优能力,能在保证发动机稳定裕度最小的同时有效降低涡轮前温度和耗油率.
- 粒子群算法 /
- 支持向量回归算法 /
- 航空发动机 /
- 稳定性寻优控制 /
- 稳定裕度
Abstract: In the seeking control of multivariable aero-engine,general optimization algorithm may find the locally optimal solution.An improved algorithm based on particle swarm optimization (PSO) and support vector regression (SVR) was applied to aero-engine stability seeking control,setting the lower limits for surge margin to keep aero-engine working stably.The working point with minimum surge margin was searched globally.The simulation results show that the designed stability control algorithm could reduce the surge margin while keeping the thrust constant and reduce the turbine inlet temperature and specific fuel consumption effectively.
- particle swarm optimization(PSO) /
- support vector regression(SVR) /
- aero-engine /
- stability seeking control /
- surge margin

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参考文献(14)

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