Robust identification of discrete-time linear systems with unknown time-varying disturbance

Abstract In this paper, one robust identification method is proposed for the discrete-time linear systems with unknown time-varying disturbance. The disturbance is considered as a time-varying parameter for tracking estimation. A robust recursive least squares method is proposed using a forgetting factor. Moreover a new forgetting scheme to update the covariance matrix is developed to improve the stable convergence property of the time-invariant model parameters and the tracking performance of time-varying disturbance. The convergence performance of parameter estimation is analyzed with a proof. Two examples with different types of time-varying disturbance are shown to illustrate the effectiveness and advantage of the proposed method.

[1]  Peter C. Young,et al.  Recursive Estimation and Time-Series Analysis: An Introduction , 1984 .

[2]  Zheng Bao,et al.  An extended recursive least-squares algorithm , 2001, Signal Process..

[3]  Feng Yu,et al.  Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity , 2013 .

[4]  S. Huffel,et al.  Total Least Squares and Errors-in-Variables Modeling : Analysis, Algorithms and Applications , 2002 .

[5]  Jari M. Böling,et al.  System identification in the presence of trends and outliers using sparse optimization , 2016 .

[6]  J. Holst,et al.  Recursive forgetting algorithms , 1992 .

[7]  Alexander Medvedev,et al.  Stationary behavior of an anti-windup scheme for recursive parameter estimation under lack of excitation , 2006, Autom..

[8]  Lennart Ljung Perspectives on System Identification , 2008 .

[9]  Er-Wei Bai,et al.  Bounded-error parameter estimation: Noise models and recursive algorithms , 1996, Autom..

[10]  Tao Liu,et al.  Identification of dual-rate sampled systems with time delay subject to load disturbance , 2017 .

[11]  Jacob Benesty,et al.  A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification , 2008, IEEE Signal Processing Letters.

[12]  Feng Ding,et al.  Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model , 2015, Digit. Signal Process..

[13]  Le Yi Wang,et al.  Persistent identification of systems with unmodeled dynamics and exogenous disturbances , 2000, IEEE Trans. Autom. Control..

[14]  Lennart Ljung,et al.  Adaptation and tracking in system identification - A survey , 1990, Autom..

[15]  Paolo Bolzern,et al.  Convergence and exponential convergence of identification algorithms with directional forgetting factor , 1990, Autom..

[16]  Anna G. Stefanopoulou,et al.  Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments , 2005 .

[17]  Wei Wu,et al.  Identification and Control of a Fuel Cell System in the Presence of Time-Varying Disturbances , 2015 .

[18]  Maciej Niedzwiecki,et al.  Identification of nonstationary multivariate autoregressive processes - Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order , 2018, Digit. Signal Process..

[19]  F. Ding,et al.  Convergence of the auxiliary model-based multi-innovation generalized extended stochastic gradient algorithm for Box–Jenkins systems , 2015 .

[20]  Wei Lin,et al.  Robust passivity and feedback design for minimum-phase nonlinear systems with structural uncertainty , 1999, Autom..

[21]  Robin J. Evans,et al.  Sinusoidal disturbance rejection with application to helicopter flight data estimation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[22]  Neil E. Goodzeit,et al.  System Identification in the Presence of Completely Unknown Periodic Disturbances , 2000 .

[23]  S. Hwang,et al.  Robust identification of continuous parametric models based on multiple sinusoidal testing under slow or periodic disturbances , 2004 .

[24]  Srinivas Karra,et al.  Alternative model structure with simplistic noise model to identify linear time invariant systems subjected to non-stationary disturbances , 2009 .

[25]  Tao Liu,et al.  Identification of Hammerstein systems with time delay under load disturbance , 2018 .

[26]  Martin B. Zarrop Variable forgetting factors in parameter estimation , 1983, Autom..

[27]  Feng Yu,et al.  Recursive parameter identification of Hammerstein-Wiener systems with measurement noise , 2014, Signal Process..

[28]  Peter C. Young,et al.  Refined instrumental variable estimation: Maximum likelihood optimization of a unified Box-Jenkins model , 2015, Autom..

[29]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[30]  Torsten Söderström,et al.  A generalized instrumental variable estimation method for errors-in-variables identification problems , 2011, Autom..

[31]  Rafael M. Canetti,et al.  Convergence analysis of the least-squares identification algorithm with a variable forgetting factor for time-varying linear systems , 1989, Autom..