Minimizing cross-talk in high-precision motion systems using data-based dynamic decoupling

To minimize cross-talk in high-precision motion systems, the possibilities of data-based dynamic decoupling are studied. Atop a model-based and static decoupling, a multi-input multi-output (MIMO) and finite impulse response (FIR) dynamic decoupling structure is considered for machine-specific and performance-driven fine tunings. The coefficients of the FIR filters are obtained via data-based optimization, whilst the machine operates under nominal and closed-loop conditions. The FIR filters provide the ability to generate zeros outside the origin. These zeros are needed in the description of the low-frequency inverted plant dynamics. In addition, a low-pass filter structure supports the ability to generate poles outside the origin as to account for plant zeros. Both filter structures are effectively used in the high-precision motion control of a state-of-the-art scanning stage system and an industrial vibration isolation system.

[1]  M. Tomizuka,et al.  Iterative learning control design for synchronization of wafer and reticle stages , 2008, 2008 American Control Conference.

[2]  Stefan Hurlebaus,et al.  Control concepts for an active vibration isolation system , 2007 .

[3]  M. Boerlage MIMO jerk derivative feedforward for motion systems , 2006, 2006 American Control Conference.

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

[5]  P. Sas,et al.  Optimal decoupling for MIMO-controller design with robust performance , 2004, Proceedings of the 2004 American Control Conference.

[6]  James C. Spall Feedback and Weighting Mechanisms for Improving Jacobian Estimates in the Adaptive Simultaneous Perturbation Algorithm , 2009, IEEE Trans. Autom. Control..

[7]  Yu-Chi Ho On centralized optimal control , 2005, IEEE Transactions on Automatic Control.

[8]  R. Kamidi,et al.  Data-based feed-forward control in MIMO motion systems , 2008, 2008 American Control Conference.

[9]  Cheng Li,et al.  Dynamic decoupling and compensating methods of multi-axis force sensors , 2000, IEEE Trans. Instrum. Meas..

[10]  John T. Wen,et al.  High Performance Motion Tracking Control for Electronic Manufacturing , 2007 .

[11]  Manfred Morari,et al.  Interaction measures for systems under decentralized control , 1986, Autom..

[12]  Michel Gevers,et al.  Correlation-based tuning of decoupling multivariable controllers , 2007, Autom..

[13]  Diego Eckhard,et al.  Optimizing the convergence of data-based controller tuning , 2009 .

[14]  Okko H. Bosgra,et al.  Multivariable feedback control design for high-precision wafer stage motion , 2002 .

[15]  Marcel François Heertjes,et al.  Dynamic decoupling in motion systems using a gradient approximation-based algorithm , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[16]  S. Gunnarsson,et al.  A convergent iterative restricted complexity control design scheme , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[17]  Svante Gunnarsson,et al.  Tuning of a decoupling controller for a 2×2 system using iterative feedback tuning , 2003 .

[18]  M. Tomizuka,et al.  Iterative tuning of feedforward controller with force ripple compensation for wafer stage , 2008, 2008 10th IEEE International Workshop on Advanced Motion Control.

[19]  N. K. Poulsen,et al.  Improving Convergence of Iterative Feedback Tuning , 2009 .