A Modified Multi-innovation Algorithm to Turntable Servo System Identification

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.

[1]  Xuemei Ren,et al.  Identification of nonlinear Wiener-Hammerstein systems by a novel adaptive algorithm based on cost function framework. , 2018, ISA transactions.

[2]  Guoqiang Hu,et al.  Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction , 2007, IEEE Transactions on Automatic Control.

[3]  Marion Gilson,et al.  A Frequency Localizing Basis Function-Based IV Method for Wideband System Identification , 2018, IEEE Transactions on Control Systems Technology.

[4]  Feng Ding,et al.  Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering , 2018, Circuits Syst. Signal Process..

[5]  Yonghong Tan,et al.  State estimation of a compound non-smooth sandwich system with backlash and dead zone , 2017 .

[6]  Feng Ding,et al.  Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique , 2018, J. Frankl. Inst..

[7]  Xiangli Li,et al.  Multi-innovation stochastic gradient method for harmonic modelling of power signals , 2016, IET Signal Process..

[8]  Yonghong Tan,et al.  Identification of micropositioning stage with piezoelectric actuators , 2016 .

[9]  V. Filipovic.  Recursive identification of block-oriented nonlinear systems in the presence of outliers , 2019, Journal of Process Control.

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

[11]  Guido Herrmann,et al.  Neural Network Control of Nonlinear Time-Delay System with Unknown Dead-Zone and Its Application to a Robotic Servo System , 2010, FIRA RoboWorld Congress.

[12]  Georgios D. Mitsis,et al.  Modeling of multiple-input, time-varying systems with recursively estimated basis expansions , 2019, Signal Process..

[13]  Jing Chen,et al.  Identification of Hammerstein systems with continuous nonlinearity , 2015, Inf. Process. Lett..

[14]  Sandeep Kumar,et al.  Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM , 2018, IEEE Transactions on Signal Processing.

[15]  Yu Guo,et al.  Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation , 2014, IEEE Transactions on Industrial Electronics.

[16]  Giuseppe Giordano,et al.  An improved method for Wiener-Hammerstein system identification based on the Fractional Approach , 2018, Autom..

[17]  J. Vörös.  Identification of nonlinear cascade systems with output hysteresis based on the key term separation principle , 2015 .

[18]  Xuemei Ren,et al.  Decomposition-based recursive least-squares parameter estimation algorithm for Wiener-Hammerstein systems with dead-zone nonlinearity , 2017, Int. J. Syst. Sci..

[19]  Cheng Wang,et al.  Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems , 2018 .

[20]  Guoli Li,et al.  Nonlinear modeling and predictive functional control of Hammerstein system with application to the turntable servo system , 2016 .

[21]  Xuemei Ren,et al.  Modified multi-innovation stochastic gradient algorithm for Wiener-Hammerstein systems with backlash , 2018, J. Frankl. Inst..

[22]  Shubo Wang,et al.  Robust adaptive tracking control for servo mechanisms with continuous friction compensation , 2019, Control Engineering Practice.

[23]  Zheng Liu,et al.  A Novel Open Circuit Voltage Based State of Charge Estimation for Lithium-Ion Battery by Multi-Innovation Kalman Filter , 2019, IEEE Access.

[24]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[25]  Jing Chen,et al.  Multi-innovation Stochastic Gradient Algorithms for Input Nonlinear Time-Varying Systems Based on the Line Search Strategy , 2018, Circuits Syst. Signal Process..

[26]  Guoli Li,et al.  Switched system identification based on the constrained multi-objective optimization problem with application to the servo turntable , 2016, International Journal of Control, Automation and Systems.

[27]  Feng Ding,et al.  Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method , 2019, IEEE Signal Processing Letters.

[28]  Johan Schoukens,et al.  Initial estimates for Wiener-Hammerstein models using phase-coupled multisines , 2015, Autom..

[29]  Grzegorz Mzyk,et al.  Kernel-based identification of Wiener-Hammerstein system , 2017, Autom..

[30]  Feng Ding,et al.  Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory , 2019, Circuits Syst. Signal Process..

[31]  Yonghong Tan,et al.  Nonlinear Modeling and Decoupling Control of XY Micropositioning Stages With Piezoelectric Actuators , 2013, IEEE/ASME Transactions on Mechatronics.

[32]  Yves Rolain,et al.  Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation , 2014, Autom..

[33]  Tie Qiu,et al.  Multivariate Chaotic Time Series Online Prediction Based on Improved Kernel Recursive Least Squares Algorithm , 2019, IEEE Transactions on Cybernetics.

[34]  Karthikeyan Rajagopal,et al.  Parameter Identification of Chaotic Systems Using a Modified Cost Function Including Static and Dynamic Information of Attractors in the State Space , 2018, Circuits, Systems, and Signal Processing.