基于改进果蝇算法的涡轴发动机状态变量模型建立方法
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摘要
针对拟合法在航空发动机小偏差状态变量模型建立中受系统模态及模型阶次的限制,提出一种基于改进果蝇优化算法(MICFOA)建立小偏差状态变量模型的方法。首先,将该方法分为2个子过程:先优化系统矩阵和输入矩阵并找到最优结果,再对输出矩阵和传输矩阵优化;同时根据状态变量模型与非线性模型动态响应一致构造了不受变量值域影响的适应度函数。其次,在果蝇优化算法(FOA)中引入协同子种群策略和混沌映射策略来增强迭代寻优中种群多样性,引入自适应调整策略来平衡全局搜索与局部搜索的关系,避免算法早熟收敛。最后应用上述方法建立了涡轴发动机小偏差状态变量模型,并设计了LQ/H_∞抗扰控制器。仿真结果表明:MICFOA相比FOA能提高5～10个数量级的精度,且所建模型与非线性模型吻合一致,具有良好的动静态性能。
Aimed at the problems that the fitting method is limited by the system model and the model order in establishing a small deviation state variable model of aircraft engine, a method based on multiple-improved-chaotic fruit fly optimization algorithm(MICFOA) is proposed. Firstly, the method is divided into two sub-processes: first thing is to optimize the system matrix, input matrix, and to find the optimal results, and then is to optimize the output matrix and the transfer matrix. Simultaneously, work is done according to the principle that the SVM's dynamic response is consistent with the nonlinear model's, fitness functions are constructed that are unaffected by the variable value domain. Secondly, synergisitic sub-population strategy and chaos mapping strategy are introduced into FOA to improve the diversity of fruit fly populations by using the adaptive adjustment strategy introduced to balance the relationship between global search and local search to avoid premature convergence. Finally this method is used to establish a turbo-shaft engine's SVM, and to design LQ/H_∞ disturbance-rejection controller. The simulation results show that MICFOA can improve the accuracy of 5-10 orders of magnitude compared with FOA, the SVM has good dynamic and static performance, and the newly built model is consistent with the nonlinear model.
Aimed at the problems that the fitting method is limited by the system model and the model order in establishing a small deviation state variable model of aircraft engine, a method based on multiple-improved-chaotic fruit fly optimization algorithm(MICFOA) is proposed. Firstly, the method is divided into two sub-processes: first thing is to optimize the system matrix, input matrix, and to find the optimal results, and then is to optimize the output matrix and the transfer matrix. Simultaneously, work is done according to the principle that the SVM's dynamic response is consistent with the nonlinear model's, fitness functions are constructed that are unaffected by the variable value domain. Secondly, synergisitic sub-population strategy and chaos mapping strategy are introduced into FOA to improve the diversity of fruit fly populations by using the adaptive adjustment strategy introduced to balance the relationship between global search and local search to avoid premature convergence. Finally this method is used to establish a turbo-shaft engine's SVM, and to design LQ/H_∞ disturbance-rejection controller. The simulation results show that MICFOA can improve the accuracy of 5-10 orders of magnitude compared with FOA, the SVM has good dynamic and static performance, and the newly built model is consistent with the nonlinear model.
引文
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[17] 秦艳平,李斌,梁俊龙,等.面向控制的燃油调节器动态特性研究[J].火箭推进,2012,38(5):7-12.QIN Y P,LI B,LIANG J L,et al.Control-Oriented Research on Dynamic Characteristics of Fuel Regulator[J].Journal of Rocket Propulsion,2012,38(5):7-12.(in Chinese)
[2] 樊思齐,李华聪,樊丁.航空发动机控制[M].西安:西北工业大学出版社,2008.FAN S Q,LI H C,FAN D.Aero-Engine Control[M].Xi’an:Northwestern Polytechnical University Press,2008.(in Chinese)
[3] SUGIYAMA N.Derivation of ABCD System Matrices from Nonlinear Dynamic Simulation of Jet Engines[Z].AIAA-923319,1992.
[4] 姚华.航空发动机全权限数字电子控制系统[M].北京:航空工业出版社,2014.YAO H.Full Authority Digital Electronic Control System for Aero-Engine[M].Beijing:Aviation Industry Press,2014.(in Chinese)
[5] 冯正平,孙健国.航空发动机小偏差状态变量模型的建立方法[J].推进技术,2001,22(1):54-57.FENG Z P,SUN J G.Modeling of Small Perturbation State Variable Model for Aeroengines[J].Journal of Propulsion Technology,2001,22(1):54-57.(in Chinese)
[6] 胡宇,杨月诚,张世英,等.基于改进拟合法的涡扇发动机状态变量模型建立方法[J].推进技术,2013,34(3):405-410.HU Y,YANG Y C,ZHANG S Y,et al.Establishment of Turbofan Engine State Variable Model Based on Improved Fitting Method[J].Journal of Propulsion Technology,2013,34(3):405-410.(in Chinese)
[7] 周文祥,单晓明,耿志东,等.自寻优求解法建立涡轴发动机状态变量模型[J].航空动力学报,2008,23(12):2314-2320.ZHOU W X,SHAN X M,GENG Z D,et al.Establishment of State Space Model of Turboshaft Engine with Self-Optimized Method[J].Journal of Aerospace Power,2008,23(12):2314-2320.(in Chinese)
[8] 张海波,杨小龙,林一晖.一种求取发动机状态变量模型的改进拟合法[J].航空动力学报,2011,26(8):1907-1913.ZHANG H B,YANG X L,LIN Y H.An Improved Method of Identification for Aero-Engine′s State Variable Model[J].Journal of Aerospace Power,2011,26(8):1907-1913.(in Chinese)
[9] 李秋红,孙健国.基于遗传算法的航空发动机状态变量模型建立方法[J].航空动力学报,2006,21(2):427-431.LI Q H,SUN J G.Aero-Engine State Variable Modeling Based on the Genetic Algorithm[J].Journal of Aerospace Power,2006,21(2):427-431.(in Chinese)
[10] PAN W.A New Fruit Fly Optimization Algorithm:Taking the Financial Distress Model as an Example[J].Knowledge-Based Systems,2012,26(1):69-74.
[11] 吴小文,李擎.果蝇算法和5种群智能算法的寻优性能研究[J].火力与指挥控制,2013,38(4):17-20.WU X W,LI Q.Research of Optimizing Performance of Fruit Fly Optimization Algorithm and Five Kinds of Intelligent Algorithm [J].Fire Control and Command Control,2013,38(4):17-20.(in Chinese)
[12] YUAN X F,DAI X S,ZHAO J G,et al.On a Novel Multi-Swarm Fruit Fly Optimization Algorithm and Its Application[J].Applied Mathematics and Computation,2014,233(1):260-271.
[13] 谢国波,苏本卉.一种新的基于混沌的彩色图像加密算法[J].计算机应用与软件,2016,33(9):324-327.XIE G B,SU B H.A New Chao-Based Color Image Encryption Algorithm[J].Computer Applications and Software,2016,33(9):324-327.(in Chinese)
[14] ZHANG Y W,CUI G M,WU J T,et al.A Novel Multi- Scale Cooperative Mutation Fruit Fly Optimization Algorithm[J].Knowledge-Based Systems,2016,114:24-35.
[15] MITIC M,VUKOVIC N,PETROVIC M,et al.Chaotic Fruit Fly Optimization Algorithm [J].Knowledge-Based Systems,2015,89:446-458.
[16] 廖光煌,李秋红,卢晨昊,等.涡轴发动机LQ/H∞抗扰控制方法[J].航空动力学报,2012,27(9):2140-2146.LIAO G H,LI Q H,LU C H,et al.Disturbance Rejection Control Method for Turboshaft Engines Based on LQ/H∞[J].Journal of Aerospace Power,2012,27(9):2140-2146.(in Chinese)
[17] 秦艳平,李斌,梁俊龙,等.面向控制的燃油调节器动态特性研究[J].火箭推进,2012,38(5):7-12.QIN Y P,LI B,LIANG J L,et al.Control-Oriented Research on Dynamic Characteristics of Fuel Regulator[J].Journal of Rocket Propulsion,2012,38(5):7-12.(in Chinese)