基于动态遗忘因子递推最小二乘算法的船舶航向模型辨识
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摘要
为提高遗忘因子递推最小二乘(RLS)算法辨识船舶航向运动数学模型参数的快速性和鲁棒性,在分析遗忘因子大小对算法特性影响的基础上,提出一种基于模糊控制的动态遗忘因子RLS算法。该算法从理论模型输出与实际模型输出之间的残差入手来构造评估参数辨识误差大小的评价函数,并将评价函数及其变化率作为模糊控制器的输入,利用模糊控制器结合制定的规则表进行模糊推理并计算遗忘因子的修正量,从而实现遗忘因子的动态调整。仿真结果表明,与恒定遗忘因子RLS算法的对比,该算法能够根据参数辨识误差实时调整遗忘因子的大小,使算法在模型参数平稳时有更高的辨识精度,在模型参数突变时有更快的收敛速度,验证了所提算法的优越性。
To improve the speed and robustness of Recursive Least Squares(RLS) algorithm with forgetting factor in the parameter identification of ship course motion mathematical model, an RLS algorithm with dynamic forgetting factor based on fuzzy control was proposed. Firstly, the residual between the theoretical model output and actual model output was calculated.Secondly, an evaluation function was constructed on the basis of the residual, to assess the parameter identification error.Then, a fuzzy controller with evaluation function and its change rate as two inputs was adopted to realize the dynamic adjustment of the forgetting factor. Combined with designed fuzzy control rule table, the modification of the forgetting factor was obtained by the fuzzy controller at last. Simulation results show that the forgetting factor can be adjusted according to the parameter identification error in the presented algorithm, which achieves higher precision and faster parameter identification than RLS algorithm with constant forgetting factor.
To improve the speed and robustness of Recursive Least Squares(RLS) algorithm with forgetting factor in the parameter identification of ship course motion mathematical model, an RLS algorithm with dynamic forgetting factor based on fuzzy control was proposed. Firstly, the residual between the theoretical model output and actual model output was calculated.Secondly, an evaluation function was constructed on the basis of the residual, to assess the parameter identification error.Then, a fuzzy controller with evaluation function and its change rate as two inputs was adopted to realize the dynamic adjustment of the forgetting factor. Combined with designed fuzzy control rule table, the modification of the forgetting factor was obtained by the fuzzy controller at last. Simulation results show that the forgetting factor can be adjusted according to the parameter identification error in the presented algorithm, which achieves higher precision and faster parameter identification than RLS algorithm with constant forgetting factor.
引文
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[3]ZHANG H,GONG S J,DONG Z Z.On-line parameter identification of induction motor based on RLS algorithm[C]//Proceedings of the 2013 International Conference on Electrical Machines and Systems.Piscataway,NJ:IEEE,2013:2132-2137.
[4]刘慧婷,冯金金,张明.水下机器人操纵系统优化控制研究[J].计算机仿真,2016,33(5):299-303.(LIU H T,FENG J J,ZHANG M.Research on optimal control of underwater robot control system[J].Computer Simulation,2016,33(5):299-303.)
[5]石刚,赵伟,刘珊珊.基于无迹卡尔曼滤波估算电池SOC[J].计算机应用,2016,36(12):3492-3498.(SHI G,ZHAO W,LIU SS.Battery SOC estimation based on unscented Kalman filtering[J].Journal of Computer Applications,2016,36(12):3492-3498.)
[6]陈涵,刘会金,李大路,等.可变遗忘因子递推最小二乘法对时变参数测量[J].高电压技术,2008,34(7):1474-1477.(CHEN H,LIU H J,LI D L,et al.Time-varying parameters measurement by least square method with variable forgetting factors[J].High Voltage Engineering,2008,34(7):1474-1477.)
[7]SALEHI R,DEHGHAN M.A generalized moving least square reproducing kernel method[J].Journal of Computational&Applied Mathematics,2013,249(6):120-132.
[8]CHEN C,LI Y.An orthogonal least-square-based method for DEMgeneralization[J].International Journal of Geographical Information Science,2013,27(1):154-167.
[9]何开锋,钱炜祺,张勇,等.偏最小二乘法在气动数据建模中的应用[J].宇航学报,2014,35(3):277-282.(HE K F,QIAN W Q,ZHANG Y,et al.Application of partial least squares regression method in modeling of aerodynamic data[J].Journal of Astronautics,2014,35(3):277-282.)
[10]YANG Z,LI X,BOWERS C P,et al.An efficient evolutionary approach to parameter identification in a building thermal model[J].IEEE Transactions on Systems,Man and Cybernetics Part C:Applications and Reviews,2012,42(6):957-969.
[11]张来福,田赟,姜敏,等.基于遗传算法的伺服系统摩擦参数辨识与仿真[J].计算机应用,2016,36(S1):110-112.(ZHANG LF,TIAN Y,JIANG M,et al.Friction parameter identification and simulation of servo system based on genetic algorithm[J].Journal of Computer Applications,2016,36(S1):110-112.)
[12]孙黎霞,倪瑶,马骙军.混沌遗传算法在励磁系统参数辨识中的应用[J].电气技术,2012(9):9-14.(SUN L X,NI Y,MA K J.Study on parameter identification of excitation system based on chaos genetic algorithm[J].Electrical Engineering,2012,(9):9-14.)
[13]ALFI A,MODARES H.System identification and control using adaptive particle swarm optimization[J].Applied Mathematical Modelling,2011,35(3):1210-1221.
[14]YANG S,WU M,YAO X,et al.Load modeling and identification based on ant colony algorithms for EV charging stations[J].IEEETransactions on Power Systems,2014,30(4):1997-2003.
[15]魏彤,田双彪.基于RLS-DE算法的多变量径向磁轴承系统辨识[J].机械工程学报,2016,52(3):143-150.(WEI T,TIAN SB.The identification of multivariable radial magnetic bearing system based on RLS-DE algorithm[J].Journal of Mechanical Engineering,2016,52(3):143-150.)
[16]王兵,李盼池,许少华.一种基于过程神经元网络辨识的PID控制模型及方法[J].计算机应用,2010,30(1):233-235.(WANGB,LI P C,XU S H.PID control model and method based on process neural network identification[J].Journal of Computer Applications,2010,30(1):233-235.)
[17]ZHAO H,GAO S,HE Z,et al.Identification of nonlinear dynamic system using a novel recurrent wavelet neural network based on the pipelined architecture[J].IEEE Transactions on Industrial Electronics,2014,61(8):4171-4182.
[18]AL-DHAIFLLAH M,WESTWICK D T.Identification of auto-regressive exogenous hammerstein models based on support vector machine regression[J].IEEE Transactions on Control Systems Technology,2013,21(6):2083-2090.
[19]BANAZADEH A,GHORBANI M T.Frequency domain identification of the Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel[J].Ocean Engineering,2013,72(4):344-355.