Математические модели многих технических систем имеют вид интегрального уравнения Вольтерра I рода с разностным ядром. Для таких систем задача идентификации заключается в построении оценки для импульсной переходной функции системы по измеренным (с шумами) значениям входного и выходного сигналов и является некорректно поставленной. В недавней работе авторов предложен устойчивый алгоритм идентификации, использующий аппарат сглаживающих кубических сплайнов для вычисления первых производных входного и выходного сигналов. К сожалению, сглаживающие кубические сплайны неудовлетворительно фильтруют аномальные измерения. Поэтому предложен двухшаговый алгоритм идентификации, на первом шаге которого аномальные измерения удаляются с использованием пространственно-локального фильтра, а затем строятся сглаживающие сплайны
Volterra integral equation of the first kind often represents stationary dynamic systems. For such a model, the non-parametric identification problem reduces to the estimation of pulse transition characteristics (that is the kernel of integral equation) from the registered noise-contaminated values of input and output signals. To formulate stable solution for identification problem authors propose algorithm that estimates pulse transition characteristics by solving Volterra integral equation of the second kind and involving first derivatives of input and output signals application that corresponds to non-stable problem. Smoothing cubic splines employed in robust calculation of first derivatives allow finding a stable solution of identification problem even when input and output signals of system identified are essentially noise-contaminated. Unfortunately, measured values of input and output signals also contain anomalous measurements such as pulse noises, glitches, etc. Such measurements are poorly smoothable by splines that cause high levels of first derivatives errors and, conversely, significant pulse transition characteristics identification errors of dynamic system. For all the reasons aforementioned, in this paper authors present the new stable two-step identification algorithm in case of anomalous measurements. The first step of the algorithm is for non-linear local-spatial combined filtration procedure of input and output signals that helps to effectively remove anomalous measurements. At the second step, smoothing cubic splines are used to calculate stable first derivatives of previously filtered signals. An extensive computational experiment showed the effectiveness of the proposed algorithm, which allows solving the identification problem with acceptable accuracy in practice even at high intensity of anomalous measurements. The experimental results give reason to recommend this algorithm for solving practical problems of identifying stationary systems, the mathematical model of which is the Voltaire integral equation of the first kind with a difference kernel
Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO
