Iterative Learning of Dynamic Inverse Filters for Feedforward Tracking Control

A novel method to construct dynamic inversion compensation is proposed for feedforward tracking control. In contrast to common approaches involving parametric system identification followed by inversion synthesis, in this article we apply iterative learning control to track an impulse signal, where the converged control input is directly used to construct the inverse filter. The method is applicable to multivariable systems without a diagonalization process. The proposed method is implemented on a linear motor and an active magnetic bearing system, respectively. The experimental results are presented to demonstrate the feedforward tracking performance.

[1]  Jeroen van de Wijdeven,et al.  Using basis functions in iterative learning control: analysis and design theory , 2010, Int. J. Control.

[2]  K. Moore,et al.  An optimal design of PD-type iterative learning control with monotonic convergence , 2002, Proceedings of the IEEE Internatinal Symposium on Intelligent Control.

[3]  Tom Oomen,et al.  Rational Basis Functions in Iterative Learning Control—With Experimental Verification on a Motion System , 2015, IEEE Transactions on Control Systems Technology.

[4]  M Maarten Steinbuch,et al.  Trajectory planning and feedforward design for electromechanical motion systems , 2005 .

[5]  Okko Bosgra,et al.  Fixed Structure Feedforward Controller Design Exploiting Iterative Trials: Application to a Wafer Stage and a Desktop Printer , 2008 .

[6]  Tsu-Chin Tsao,et al.  High-Sampling Rate Dynamic Inversion—Filter Realization and Applications in Digital Control , 2014, IEEE/ASME Transactions on Mechatronics.

[7]  Cheng-Wei Chen,et al.  Dynamic Trajectory Tracking by Synergistic Dual-Stage Actuation and Control , 2017, IEEE/ASME Transactions on Mechatronics.

[8]  Richard W. Longman,et al.  Iterative learning control and repetitive control for engineering practice , 2000 .

[9]  Tom Oomen,et al.  Data-driven optimal ILC for multivariable systems: Removing the need for L and Q filter design , 2015, 2015 American Control Conference (ACC).

[10]  Tsu-Chin Tsao.  Optimal Feed-Forward Digital Tracking Controller Design , 1994 .

[11]  B. Widrow,et al.  Adaptive inverse control , 1987, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[12]  Svante Gunnarsson,et al.  Time and frequency domain convergence properties in iterative learning control , 2002 .

[13]  Cheng-Wei Chen,et al.  Data-based feedforward controller reconstruction from iterative learning control algorithm , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[14]  Tsu-Chin Tsao,et al.  A comparison of inversion based Iterative Learning Control Algorithms , 2015, 2015 American Control Conference (ACC).

[15]  M Maarten Steinbuch,et al.  Iterative motion feedforward tuning : a data-driven approach based on instrumental variable identification , 2015 .

[16]  Masayoshi Tomizuka,et al.  Zero Phase Error Tracking Algorithm for Digital Control , 1987 .

[17]  Masayoshi Tomizuka,et al.  Synchronization of Two Motion Control Axes Under Adaptive Feedforward Control , 1990 .

[18]  T. Tsao,et al.  Design and Implementation of Repetitive Control based Noncausal Zero-Phase Iterative Learning Control , 2014, 1408.2490.

[19]  Lucy Y. Pao,et al.  Nonminimum Phase Dynamic Inversion for Settle Time Applications , 2009, IEEE Transactions on Control Systems Technology.

[20]  Jie Ling,et al.  A model-data integrated iterative learning controller for flexible tracking with application to a piezo nanopositioner , 2018, Trans. Inst. Meas. Control.

[21]  J. Massey,et al.  Invertibility of linear time-invariant dynamical systems , 1969 .

[22]  Samer S. Saab,et al.  A discrete-time stochastic learning control algorithm , 2001, IEEE Trans. Autom. Control..

[23]  Lennart Blanken,et al.  Rational iterative feedforward tuning: Approaches, stable inversion, and experimental comparison , 2016, 2016 American Control Conference (ACC).

[24]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[25]  G.D. Cain,et al.  Approximation of FIR by IIR digital filters: an algorithm based on balanced model reduction , 1992, IEEE Trans. Signal Process..

[26]  Masayoshi Tomizuka,et al.  Adaptive Zero Phase Error Tracking Algorithm for Digital Control , 1987 .

[27]  Qingze Zou,et al.  A review of feedforward control approaches in nanopositioning for high-speed spm , 2009 .

[28]  Tsu-Chin Tsao,et al.  MIMO Repetitive Control of an Active Magnetic Bearing Spindle , 2016 .

[29]  Michael J. Grimble,et al.  Iterative Learning Control for Deterministic Systems , 1992 .

[30]  Toshiharu Sugie,et al.  An iterative learning control algorithm within prescribed input-output subspace , 1999, Autom..