嵌入式大气数据传感系统的模糊逻辑建模方法
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摘要
为克服传统的大气数据传感系统的不足,对嵌入式大气数据系统展开了研究。以某飞翼布局飞行器为研究对象,通过风洞试验和CFD数据,研究了针对嵌入式大气数据系统的模糊逻辑建模方法。以模型表面若干测压点的压力或压力系数作为模糊逻辑系统的输入,以迎角、侧滑角、来流速度和海拔高度作为输出,分别采用自适应和固定形状参数的隶属函数作为模型组成部分,混合使用梯度下降法和最小二乘法来识别模糊逻辑系统的参数,从而建立针对该嵌入式大气数据系统的模糊逻辑模型。建模结果表明,相比以往仅使用梯度下降法和固定形状参数的隶属函数的模糊逻辑模型,自适应隶属函数的引入使得模型精度与求解速度得到提高。
To overcome the disadvantages of traditional air data sensing system,the embedded flush air data sensing(FADS)system is investigated.Based on wind tunnel experiments and computational fluid dynamics(CFD)simulation results of an all-wing aircraft,the fuzzy logic modeling algorithm for the FADS system is studied.By using pressure or pressure coefficients on the model surface as input data while angle of attack,angle of sideslip,inlet velocity and altitude as output data,a fuzzy logic structure is built for the FADS system with adaptive and fixed shape parameters membership functions as the fuzzy model's component and parameters of the system solved by a hybrid learning procedure(gradient descent and least squares estimate method).The modeling results show that,compared with the methods with fixed shape parameters membership functions and inner functions parameters solved by gradient descent,the method with adaptive membership functions has higher modeling accuracy and efficiency.
To overcome the disadvantages of traditional air data sensing system,the embedded flush air data sensing(FADS)system is investigated.Based on wind tunnel experiments and computational fluid dynamics(CFD)simulation results of an all-wing aircraft,the fuzzy logic modeling algorithm for the FADS system is studied.By using pressure or pressure coefficients on the model surface as input data while angle of attack,angle of sideslip,inlet velocity and altitude as output data,a fuzzy logic structure is built for the FADS system with adaptive and fixed shape parameters membership functions as the fuzzy model's component and parameters of the system solved by a hybrid learning procedure(gradient descent and least squares estimate method).The modeling results show that,compared with the methods with fixed shape parameters membership functions and inner functions parameters solved by gradient descent,the method with adaptive membership functions has higher modeling accuracy and efficiency.
引文
[1] COBLEIGH B R,WHITMORE S A,Jr HAERING E A,et al.Flush airdata sensing(FADS)system calibration procedures and results for blunt forebodies[R].AIAA 99-4816,1999.
[2]李其畅,刘劲帆,刘昕,等.嵌入式大气数据三点解算方法初步研究[J].空气动力学学报,2014,32(3):360-363.LI Q C,LIU J F,LIU X,et al.The primary study of 3-point calculation method for the flush air data system[J].Acta Aerodynamica Sinica,2014,32(3):360-363.(in Chinese)
[3] ARTZ E J,DONA N W,YECHOUT T R.NASA orion flush air datasensingsystemfeasibilitydeterminationand development[R].AIAA 2014-1115.
[4] ROHLOFF T J,CATTON I,WHITMORE S A,et al.Air data sensing from surface pressure measurements using a neural network method[J].AIAA Journal,1998,36(11):2094-2101.
[5] ROHLOFF T J,WHITMORE S A,CATTON I,et al.Faulttolerant neural network algorithm for flush air data sensing[J].Journal of Aircraft,1999,36(3):541-549.
[6] SAMY I,POSTLETHWAITE I,GU D W,et al.Neuralnetwork-based flush air data sensing system demonstrated on a mini air vehicle[J].Journal of Aircraft,2010,47(1):18-31.
[7] CHEN G Q,CHEN B Y,LI P F,et al.Study on algorithms of flush air data sensing system for hypersonic vehicle[C]//AsiaPacific International Symposium on Aerospace Technology,2015,99:860-865.
[8] CHELI F,ROCCHI D,SCHITO P,et al.Neural network algorithm forevaluatingwingvelocityfrompressure measurements performed on a train’s surface[J].Journal of Rail and Rapid Transit,2016,230(3):961-970.
[9] QUINDLEN J F,LANGELAAN J W.Flush air data sensing for soaring-capable UAVs[C]//51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition,2013.
[10]史志伟,吴根兴.多变量非线性非定常气动力的模糊逻辑模型[J].空气动力学学报,2001,19(1):103-108.SHI Z W,WU G X.Fuzzy logic model of nonlinear unsteady aerodynamics with multiple variables[J].Acta Aerodynamica Sinica,2001,19(1):103-108.(in Chinese)
[11] WANG Z J,LAN C E,BRANDON J M.Fuzzy logic modeling of nonlinear unsteady aerodynamics[C]//23rd Atmospheric Flight Mechanics Conference,1998.
[12] WANG Z J,LAN C E,BRANDON J M.Fuzzy logic modeling of lateral-directionalunsteadyaerodynamics[C]//24th Atmospheric Flight Mechanics Conference,1999.
[13]TAKAGI T,SUGENO M.Fuzzy identification of systems and its applications to modeling and control[J].IEEE Transactions on Systems,Man,and Cybernetics,1985,15(1):116-132.
[14]JANG J S.ANFIS:Adaptive-network-based fuzzy inference system[J].IEEE Transactions on Systems, Man,and Cybernetics,1993,23(3):665-685.
[2]李其畅,刘劲帆,刘昕,等.嵌入式大气数据三点解算方法初步研究[J].空气动力学学报,2014,32(3):360-363.LI Q C,LIU J F,LIU X,et al.The primary study of 3-point calculation method for the flush air data system[J].Acta Aerodynamica Sinica,2014,32(3):360-363.(in Chinese)
[3] ARTZ E J,DONA N W,YECHOUT T R.NASA orion flush air datasensingsystemfeasibilitydeterminationand development[R].AIAA 2014-1115.
[4] ROHLOFF T J,CATTON I,WHITMORE S A,et al.Air data sensing from surface pressure measurements using a neural network method[J].AIAA Journal,1998,36(11):2094-2101.
[5] ROHLOFF T J,WHITMORE S A,CATTON I,et al.Faulttolerant neural network algorithm for flush air data sensing[J].Journal of Aircraft,1999,36(3):541-549.
[6] SAMY I,POSTLETHWAITE I,GU D W,et al.Neuralnetwork-based flush air data sensing system demonstrated on a mini air vehicle[J].Journal of Aircraft,2010,47(1):18-31.
[7] CHEN G Q,CHEN B Y,LI P F,et al.Study on algorithms of flush air data sensing system for hypersonic vehicle[C]//AsiaPacific International Symposium on Aerospace Technology,2015,99:860-865.
[8] CHELI F,ROCCHI D,SCHITO P,et al.Neural network algorithm forevaluatingwingvelocityfrompressure measurements performed on a train’s surface[J].Journal of Rail and Rapid Transit,2016,230(3):961-970.
[9] QUINDLEN J F,LANGELAAN J W.Flush air data sensing for soaring-capable UAVs[C]//51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition,2013.
[10]史志伟,吴根兴.多变量非线性非定常气动力的模糊逻辑模型[J].空气动力学学报,2001,19(1):103-108.SHI Z W,WU G X.Fuzzy logic model of nonlinear unsteady aerodynamics with multiple variables[J].Acta Aerodynamica Sinica,2001,19(1):103-108.(in Chinese)
[11] WANG Z J,LAN C E,BRANDON J M.Fuzzy logic modeling of nonlinear unsteady aerodynamics[C]//23rd Atmospheric Flight Mechanics Conference,1998.
[12] WANG Z J,LAN C E,BRANDON J M.Fuzzy logic modeling of lateral-directionalunsteadyaerodynamics[C]//24th Atmospheric Flight Mechanics Conference,1999.
[13]TAKAGI T,SUGENO M.Fuzzy identification of systems and its applications to modeling and control[J].IEEE Transactions on Systems,Man,and Cybernetics,1985,15(1):116-132.
[14]JANG J S.ANFIS:Adaptive-network-based fuzzy inference system[J].IEEE Transactions on Systems, Man,and Cybernetics,1993,23(3):665-685.