A new fast online identification method for linear time-varying systems

A novel online identification algorithm is proposed, which addresses the problem of convergence rate in dynamic parameter estimation in the presence of abrupt variations as well as noise in time-varying systems. The proposed identification technique optimizes a mean fourth error cost function by virtue of steepest descent (SD) method. It is proven that a unique solution for the optimal correcting gain of the SD update law exists and a closed-form solution is derived. To obtain high sensitivity to parameter variations, a block-wise version of the proposed technique, that incorporates only a finite length window of data, is developed. The performance of the proposed method is compared to those of two benchmark identification techniques.

[1]  O. Nelles.  Nonlinear System Identification , 2001 .

[2]  Wayne J. Book,et al.  Environment estimation for enhanced impedance control , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[3]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[4]  Dong-Jo Park,et al.  Selfperturbing recursive least squares algorithm with fast tracking capability , 1992 .

[5]  L. Childs.  A concrete introduction to higher algebra , 1978 .

[6]  T. H. Hunt.  A comparative study of system identification techniques in the presence of parameter variation, noise, and data anomalies , 1996, Proceedings of the 39th Midwest Symposium on Circuits and Systems.

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

[8]  Bernard Widrow,et al.  The least mean fourth (LMF) adaptive algorithm and its family , 1984, IEEE Trans. Inf. Theory.

[9]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[10]  Ali H. Sayed,et al.  Mean-square performance of data-reusing adaptive algorithms , 2005, IEEE Signal Processing Letters.

[11]  C. S. Burrus,et al.  Least p-power error design of FIR filters , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[12]  Rao Yarlagadda,et al.  Lp normed minimization with applications to linear predictive modeling for sinusoidal frequency estimation , 1991, Signal Process..

[13]  Chien-Cheng Tseng,et al.  Least mean p-power error criterion for adaptive FIR filter , 1994, IEEE J. Sel. Areas Commun..

[14]  Septimiu E. Salcudean,et al.  Evaluation of Impedance and Teleoperation Control of a Hydraulic Mini-Excavator , 1997, ISER.

[15]  Andrew H. Jazwinski,et al.  Adaptive filtering , 1969, Autom..

[16]  Binwei Weng,et al.  Nonlinear system identification in impulsive environments , 2005, IEEE Transactions on Signal Processing.

[17]  Youmin Zhang,et al.  A novel variable‐length sliding window blockwise least‐squares algorithm for on‐line estimation of time‐varying parameters , 2004 .

[18]  R. Sutton.  Gain Adaptation Beats Least Squares , 2006 .

[19]  Lennart Ljung,et al.  Adaptation and Tracking in System Identification , 1988 .