Frequency estimation of a sinusoidal signal with time-varying amplitude and phase

作     者：Vedyakov, Alexey A. Vediakova, Anastasiia O. Bobtsov, Alexey A. Pyrkin, Anton A. Kakanov, Mikhail A. 

作者机构：ITMO University Kronverkskiy pr. 49 St. Petersburg197101 Russia Department of Computer Applications and Systems St.Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg199178 Russia 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2018年第51卷第32期

页      面：663-668页

核心收录：

基　　金：Russian Science Foundation: proprTohjectisctw18-19-00627  whwherereA1  prThisojectw18-19-00627 

主　　题：Frequency estimation Continuous time systems Inverse problems Permanent magnets Regression analysis Angular velocity estimation Continuous time estimations Frequency estimation errors Globally convergent Identification method Linear regression models Permanent Magnet Synchronous Motor Trigonometric functions 

摘      要：This paper is devoted to frequency estimation of a non-stationary sinusoidal signal. The amplitude is supposed to be a known function within a constant factor, the phase should be known. Example of such problem statement is sensorless angular velocity estimation for permanent magnet synchronous motors. On the first step by reparametrization, a third order linear regression model is obtained. On the next step, an estimation algorithm is constructed based on a standard gradient approach. The frequency estimate can be computed from one of the model parameters using inverse trigonometric functions. To improve estimates quality for noisy measurements we propose a new identification method, which can be tuned to attenuate the noise influence. It is shown that the frequency estimation error converges to zero exponentially fast. The described algorithm does not require measuring or calculating derivatives of the input signal. The efficiency of the proposed approach is demonstrated through the set of numerical simulations. © 2018