Abstract
This paper is concerned with the identification of turntable servo system through the usage of a reframed multi-innovation least-squares scheme. A Wiener–Hammerstein model is employed in this paper to depict the dynamic characteristics of the turntable system. In the test bed, the stabilized platform can be considered as a linear dynamic subsystem. The motor is also a linear dynamic subsystem. And the major nonlinearity characteristic between motor and platform is captured by a continuously differentiable friction model. A new reframed multi-innovation least-squares approach (RMILS) is proposed to identify the Wiener–Hammerstein model. By introducing the intermediary step updating, the innovation updating is decomposed into sub-innovations updating, which can solve the inverse of covariance matrix and improve the identification performance. Then, the consistency nature of the RMILS method is discussed by using the theoretical analysis. Finally, the simulation and experiment results explain that the developed approach produces an outstanding performance in convergence speed and identification precision comparing to the conventional multi-innovation least-squares approach.
Similar content being viewed by others
References
J. Chen, X. Wang, Identification of Hammerstein systems with continuous nonlinearity. Inf. Process. Lett. 115, 822–827 (2015)
R. Dong, Y. Tan, Y. Xie, Identification of micropositioning stage with piezoelectric actuators. Mech. Syst. Signal Process. 75, 618–630 (2016)
V. Filipovic, Recursive identification of block-oriented nonlinear systems in the presence of outliers. J. Process Control 78, 1–12 (2019)
M. Gan, X. Chen, F. Ding, G. Chen, C.L.P. Chen, Adaptive RBF-AR models based on multi-innovation least squares method. IEEE Signal Process. Lett. 26, 1182–1186 (2019)
M. Gilson, J.S. Welsh, H. Garnier, A frequency localizing basis function-based IV method for wideband system identification. IEEE Trans. Control Syst. Technol. 26, 329–335 (2018)
G. Giordano, S. Gros, J. Sjöberg, An improved method for Wiener–Hammerstein system identification based on the fractional approach. Automatica 94, 349–360 (2018)
G. Goodwin, K. Sin, Adaptive Filtering Prediction and Control (Prentice Hall, Englewood Clifs, 1984)
M. Han, S. Zhang, M. Xu, T. Qiu, N. Wang, Multivariate chaotic time series online prediction based on improved kernel recursive least squares algorithm. IEEE Trans. Cybern. 49, 1160–1172 (2019)
K. Kostoglou, R. Schondorf, G.D. Mitsis, Modeling of multiple-input, time-varying systems with recursively estimated basis expansions. Signal Process. 155, 287–300 (2019)
L. Lennart, System Identification: Theory for the User, 2nd edn. (PTR Prentice Hall, Upper Saddle River, 1999)
L. Li, X. Ren, Decomposition-based recursive least-squares parameter estimation algorithm for Wiener–Hammerstein systems with dead-zone nonlinearity. Int. J. Syst. Sci. 48, 2405–2414 (2017)
L. Li, X. Ren, Identification of nonlinear Wiener–Hammerstein systems by a novel adaptive algorithm based on cost function framework. ISA Trans. 80, 146–159 (2018)
L. Li, X. Ren, F. Guo, Modified multi-innovation stochastic gradient algorithm for Wiener–Hammerstein systems with backlash. J. Frankl. Inst. 355, 4050–4075 (2018)
Z. Liu, X. Dang, B. Jing, A novel open circuit voltage based state of charge estimation for lithium-ion battery by multi-innovation Kalman filter. IEEE Access 7, 49432–49447 (2019)
Q. Liu, F. Ding, Auxiliary model-based recursive generalized least squares algorithm for multivariate output-error autoregressive systems using the data filtering. Circuits Syst. Signal Process. 38, 590–610 (2019)
Q. Liu, F. Ding, Y. Wang, T. Hayatc, Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique. J. Frankl. Inst. 355, 7643–7663 (2018)
L. Liu, F. Ding, C. Wang, A. Alsaedi, T. Hayat, Maximum likelihood multi-innovation stochastic gradient estimation for multivariate equation-error systems. Int. J. Control Autom. Syst. 16, 2528–2537 (2018)
J. Liu, S. Kumar, D.P. Palomar, Parameter estimation of heavy-tailed ar model with missing data via stochastic EM. IEEE Trans. Signal Process. 67, 2159–2172 (2019)
C. Makkar, G. Hu, W.G. Sawyer, W.E. Dixon, Lyapunov-based tracking control in the presence of uncertain nonlinear parameterizable friction. IEEE Trans. Autom. Control 52, 1988–1994 (2007)
G. Mzyk, P. Wachel, Kernel-based identification of Wiener–Hammerstein system. Automatica 83, 275–281 (2017)
J. Na, Q. Chen, X. Ren, Y. Guo, Adaptive prescribed performance motion control of servo mechanisms with friction compensation. IEEE Trans. Ind. Electron. 61, 486–494 (2014)
J. Na, G. Herrmann, X. Ren, Neural network control of nonlinear time-delay system with unknown dead-zone and its application to a robotic servo system, in 2010 FIRA RoboWorld Congress (Springer, Berlin, 2010), pp. 338–345
M. Schoukens, R. Pintelon, Y. Rolain, Identification of Wiener–Hammerstein systems by a nonparametric separation of the best linear approximation. Automatica 50, 628–634 (2014)
Y. Shekofteh, S. Jafari, K. Rajagopal, V. Pham, Parameter identification of chaotic systems using a modified cost function including static and dynamic information of attractors in the state space. Circuits Syst. Signal Process. 38, 2039–2054 (2019)
Q. Shen, J. Chen, X. Ma, Multi-innovation stochastic gradient algorithms for input nonlinear time-varying systems based on the line search strategy. Circuits Syst. Signal Process. 38, 2023–2038 (2019)
K. Tiels, M. Schoukens, J. Schoukens, Initial estimates for Wiener–Hammerstein models using phase-coupled multisines. Automatica 60, 201–209 (2015)
J. Vörös, Identification of nonlinear cascade systems with output hysteresis based on the key term separation principle. Appl. Math. Model. 39, 5531–5539 (2015)
L. Wan, F. Ding, Decomposition- and gradient-based iterative identification algorithms for multivariable systems using the multi-innovation theory. Circuits Syst. Signal Process. 38, 2971–2991 (2019)
S. Wang, H. Yu, J. Yu, Robust adaptive tracking control for servo mechanisms with continuous friction compensation. Control Eng. Pract. 87, 76–82 (2019)
Y. Xie, Y. Tan, R. Dong, Nonlinear modeling and decoupling control of XY micropositioning stages with piezoelectric actuators. IEEE/ASME Trans. Mechatron. 18, 821–832 (2013)
Q. Zhang, Q. Wang, G. Li, Nonlinear modeling and predictive functional control of Hammerstein system with application to the turntable servo system. Mech. Syst. Signal Process. 72, 383–394 (2016)
Q. Zhang, Q.J. Wang, G.L. Li, Switched system identification based on the constrained multi-objective optimization problem with application to the servo turntable. Int. J. Control Autom. Syst. 14, 1153–1159 (2016)
L. Zhou, X. Li, H. Xu, P. Zhu, Multi-innovation stochastic gradient method for harmonic modelling of power signals. IET Signal Process. 10, 737–742 (2016)
Z. Zhou, Y. Tan, Y. Xie, R. Dong, State estimation of a compound non-smooth sandwich system with backlash and dead zone. Mech. Syst. Signal Process. 83, 439–449 (2017)
Acknowledgements
This paper was supported by the National Natural Science Foundation of China (Nos. 61433003, 61973036 and 61873246).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Li, L., Zhang, H. & Ren, X. A Modified Multi-innovation Algorithm to Turntable Servo System Identification. Circuits Syst Signal Process 39, 4339–4353 (2020). https://doi.org/10.1007/s00034-020-01392-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-020-01392-z