On recursive Markov parameters estimation for MIMO systems

被引:0
|
作者
da Silva, Gustavo R. Goncalves [1 ]
Lazar, Mircea [1 ]
机构
[1] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 07期
关键词
Markov parameters; estimation; multivariable systems; recursive least-squares;
D O I
10.1016/j.ifaco1.2021.08.385
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work develops a recursive algorithm to estimate a given size sequence of Markov parameters for linear discrete-time systems, which is related to FIR models estimation. The discussion on FIR models in identification literature tends to be brief due to its poor prediction error for low order models, although Markov parameter sequence of shorter length can be used, e.g., as the input for data-driven MPC based on FIR models and for system identification combined with realization theory. Estimation of Markov parameters sequence of larger length can also be used in applications in which the prediction itself is not relevant, such as stability assessment or norm computations. The formulation is derived for SISO systems and then we extended it to the MIMO case. An analysis of the overall truncation and bias errors is also developed and illustrative examples are given to highlight the method's performance. In the examples we also further illustrate the difference in estimation results for different inputs, since the input choice is affected by the identification method utilised. Copyright (C) 2021 The Authors.
引用
收藏
页码:357 / 362
页数:6
相关论文
共 50 条
  • [21] Recursive Identification of MIMO Wiener Systems
    Mu, Bi-Qiang
    Chen, Han-Fu
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2013, 58 (03) : 802 - 808
  • [22] Recursive Identification for MIMO Hammerstein Systems
    Chen Xing-Min
    Chen Han-Fu
    PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, : 6215 - 6220
  • [23] On a recursive method for the estimation of unknown parameters of partially observed chaotic systems
    Marino, Ines P.
    Miguez, Joaquin
    PHYSICA D-NONLINEAR PHENOMENA, 2006, 220 (02) : 175 - 182
  • [24] Recursive estimation for Markov jump linear systems with unknown transition probabilities: A compensation approach
    Zhao, Shunyi
    Liu, Fei
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (07): : 1494 - 1517
  • [25] High-Order Moment Recursive State Estimation of Markov Jump Linear Systems
    Zhou, Ziheng
    Luan, Xiaoli
    Liu, Fei
    IEEE ACCESS, 2018, 6 : 70788 - 70793
  • [26] CALCULATION OF MARKOV PARAMETERS FROM THE TRANSFER-FUNCTION MATRIX OF MIMO SYSTEMS
    ALMUTHAIRI, NF
    BINGULAC, S
    INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1995, 26 (04) : 787 - 798
  • [27] Moving Target Parameters Estimation in Noncoherent MIMO Radar Systems
    Hassanien, Aboulnasr
    Vorobyov, Sergiy A.
    Gershman, Alex B.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (05) : 2354 - 2361
  • [28] Recursive estimation of time-varying channel and frequency offset in MIMO OFDM systems
    Roman, T
    Enescu, N
    Koivunen, V
    PIMRC 2003: 14TH IEEE 2003 INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS PROCEEDINGS, VOLS 1-3 2003, 2003, : 1934 - 1938
  • [29] Online inertia estimation for power systems with high penetration of RES using recursive parameters estimation
    Makolo, Peter
    Zamora, Ramon
    Lie, Tek-Tjing
    IET RENEWABLE POWER GENERATION, 2021, 15 (12) : 2571 - 2585
  • [30] On the stability of the recursive Kalman filter with Markov jump parameters
    Gomes, Maria J. F.
    Costa, Eduardo F.
    2010 AMERICAN CONTROL CONFERENCE, 2010, : 4159 - 4163