Extension of Subspace Identification to LPTV Systems: Application to Helicopters
نویسندگان
چکیده
In this paper, we focus on extending the subspace identification to the class of linear periodically time-varying (LPTV) systems. The Lyapunov-Floquet transformation is first applied to the system’s state-space model in order to get the monodromy matrix (MM) and, thus, a necessary and sufficient condition for system stability. Then, given two successive covariance-driven Hankel matrices, the MM matrix is extracted by some calculus of a simultaneous singular value decomposition (SVD) and a least square optimization. The method is illustrated by a simulation application to the model of a hinged-blades helicopter.
منابع مشابه
Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...
متن کاملIdentification of Linear Periodically Time-Varying (LPTV) Systems
A linear periodically time-varying (LPTV) system is a linear time-varying system with the coefficients changing periodically, which is widely used in control, communications, signal processing, and even circuit modeling. This thesis concentrates on identification of LPTV systems. To this end, the representations of LPTV systems are thoroughly reviewed. Identification methods are developed accor...
متن کاملThe Application of Numerical Analysis Techniques to Pattern Recognition of Helicopters by Area Method
In this paper, a new method to selecting different viewing angles feature vector is introduced to recognition different types of Helicopters. Feature vector 32 components based on characteristics of the shape, Area and a length to describe a binary two-dimensional image was created, shape feature and length feature not only effective but area features effective and were used. New features vecto...
متن کاملFrequency Domain Total Least Squares Identification of Linear, Periodically Time-Varying Systems from Noisy Input-Output Data
This paper presents an extension of the well known linear time invariant identification theory to Linear, Periodically Time-Varying (LPTV) systems. The considered class of systems is described by ordinary differential equations with coefficients that vary periodically over time, making use of multisines both for excitations as well as for the time-varying system parameters. To solve the model e...
متن کاملSubspace system identification
We give a general overview of the state-of-the-art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First, the basis of linear subspace identification are summarized. Different algorithms one finds in literature (Such as N4SID, MOESP, CVA) are discussed and put into a unifyin...
متن کامل