Advanced motion control for precision mechatronics: control, identification, and learning of complex systems

Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. An increase in such requirements necessitates enhanced control performance. The aim of this paper is to identify several challenges for advanced motion control originating from these increasing accuracy, speed, and cost requirements. For instance, flexible mechanics must be explicitly addressed through overactuation, oversensing, inferential control, and position-dependent control. This in turn requires suitable models of appropriate complexity. One of the main advantages of such motion systems is the fact that experimenting and collecting large amounts of accurate data is inexpensive, paving the way for identifying and learning of models and controllers from experimental data. Several ongoing developments are outlined that constitute part of an overall framework for control, identification, and learning of complex motion systems. In turn, this may pave the way for new mechatronic design principles, leading to fast lightweight machines where spatio-temporal flexible mechanics are explicitly compensated through advanced motion control.

[1]  Ruud J. P. Schrama.  Accurate identification for control: the necessity of an iterative scheme , 1992 .

[2]  Richard W. Longman,et al.  Active Control Technology for Large Space Structures , 1993 .

[3]  Lennart Blanken,et al.  Optimal estimation of rational feedforward controllers: An instrumental variable approach , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[4]  Santosh Devasia,et al.  Guest Editorial Introduction to the Special Issue on Dynamics and Control of Micro- and Nanoscale Systems , 2007 .

[5]  Tom Oomen,et al.  Identification of High-Tech Motion Systems: An Active Vibration Isolation Benchmark , 2015 .

[6]  L.Y. Pao,et al.  A Tutorial on the Mechanisms, Dynamics, and Control of Atomic Force Microscopes , 2007, 2007 American Control Conference.

[7]  Tom Oomen,et al.  Optimality and flexibility in Iterative Learning Control for varying tasks , 2016, Autom..

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

[9]  Andrew J. Fleming,et al.  Design, Modeling and Control of Nanopositioning Systems , 2014 .

[10]  B. Arnold.  Shrinking possibilities , 2009, IEEE Spectrum.

[11]  P. V. D. Hof,et al.  Suboptimal feedback control by a scheme of iterative identification and control design , 1997 .

[12]  G. Stein,et al.  Multivariable feedback design: Concepts for a classical/modern synthesis , 1981 .

[13]  Masayoshi Tomizuka,et al.  Robust motion controller design for high-accuracy positioning systems , 1996, IEEE Trans. Ind. Electron..

[14]  Goele Pipeleers,et al.  Optimal Linear Controller Design for Periodic Inputs , 2009 .

[15]  Lennart Blanken,et al.  Flexible ILC : towards a convex approach for non-causal rational basis functions , 2017 .

[16]  Goele Pipeleers,et al.  A Data-Driven Constrained Norm-Optimal Iterative Learning Control Framework for LTI Systems , 2013, IEEE Transactions on Control Systems Technology.

[17]  Lennart Ljung,et al.  Comparing different approaches to model error modeling in robust identification , 2002, Autom..

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

[19]  Tom Oomen,et al.  Controlling aliased dynamics in motion systems? An identification for sampled-data control approach , 2014, Int. J. Control.

[20]  Mohammed Chadli,et al.  Multivariable control systems - an engineering approach , 2003, Autom..

[21]  Marcel François Heertjes,et al.  Constrained Iterative Feedback Tuning for Robust Control of a Wafer Stage System , 2016, IEEE Transactions on Control Systems Technology.

[22]  Paul M.J. Van den Hof,et al.  Closed-Loop Issues in System Identification , 1997 .

[23]  Tae Tom Oomen,et al.  On Optimal Feedforward and ILC: The Role of Feedback for Optimal Performance and Inferential Control , 2017 .

[24]  M Maarten Steinbuch,et al.  Model-Based Control for High-Tech Mechatronic Systems , 2020 .

[25]  Tom Oomen,et al.  Well-Posed Model Uncertainty Estimation by Design of Validation Experiments , 2009 .

[26]  S. O. Reza Moheimani,et al.  Spatial Control of Vibration: Theory and Experiments , 2003 .

[27]  M Maarten Steinbuch,et al.  MIMO feed-forward design in wafer scanners using a gradient approximation-based algorithm , 2010 .

[28]  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.

[29]  Håkan Hjalmarsson,et al.  Iterative Data-Driven ${\cal H}_{\infty}$ Norm Estimation of Multivariable Systems With Application to Robust Active Vibration Isolation , 2014, IEEE Transactions on Control Systems Technology.

[30]  G Dan Hutcheson,et al.  The first nanochips. , 2004, Scientific American.

[31]  Frank Boeren,et al.  Frequency-Domain ILC Approach for Repeating and Varying Tasks: With Application to Semiconductor Bonding Equipment , 2016, IEEE/ASME Transactions on Mechatronics.

[32]  Håkan Hjalmarsson,et al.  From experiment design to closed-loop control , 2005, Autom..

[33]  Alexander Lanzon,et al.  Feedback Control of Negative-Imaginary Systems , 2010, IEEE Control Systems.

[34]  T.F. Edgar,et al.  Control of lithography in semiconductor manufacturing , 2006, IEEE Control Systems.

[35]  M Maarten Steinbuch,et al.  Industrial perspective on robust control: Application to storage systems , 1997 .

[36]  André Preumont,et al.  Vibration Control of Active Structures: An Introduction , 2018 .

[37]  Cristian R. Rojas,et al.  Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility , 2017, ArXiv.

[38]  Frank Boeren,et al.  Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning , 2017, Int. J. Control.

[39]  Tom Oomen,et al.  An approach to stable inversion of LPTV systems with application to a position-dependent motion system , 2017, 2017 American Control Conference (ACC).

[40]  Tom Oomen,et al.  Data‐driven multivariable ILC: enhanced performance by eliminating L and Q filters , 2018 .

[41]  S. O. Reza Moheimani,et al.  Dynamics and Control of Micro- and Nanoscale Systems: An Introduction to the Special Issue , 2013, IEEE Control Systems.

[42]  Frank Boeren,et al.  Joint input shaping and feedforward for point-to-point motion : automated tuning for an industrial nanopositioning system , 2014 .

[43]  M Maarten Steinbuch,et al.  Non-diagonal H∞ weighting function design: Exploiting spatio-temporal deformations in precision motion control , 2015 .

[44]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[45]  Sippe G. Douma,et al.  Relations between uncertainty structures in identification for robust control , 2005, Autom..

[46]  Kouhei Ohnishi,et al.  Motion control for advanced mechatronics , 1996 .

[47]  Tae Tom Oomen,et al.  Estimating structural deformations for inferential control: a disturbance observer approach , 2016 .

[48]  Henrik Niemann.  Dual Youla parameterisation , 2003 .

[49]  Keith Glover,et al.  Robust control design using normal-ized coprime factor plant descriptions , 1989 .

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

[51]  Maarten Steinbuch,et al.  Control Relevant Blind Identification of Disturbances With Application to a Multivariable Active Vibration Isolation Platform , 2010, IEEE Transactions on Control Systems Technology.

[52]  P. Hughes,et al.  Space structure vibration modes: How many exist? Which ones are important? , 1984, IEEE Control Systems Magazine.

[53]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[54]  Tom Oomen,et al.  Accurate FRF Identification of LPV Systems: nD-LPM With Application to a Medical X-Ray System , 2017, IEEE Transactions on Control Systems Technology.

[55]  Lennart Blanken,et al.  Batch-to-Batch Rational Feedforward Control: From Iterative Learning to Identification Approaches, With Application to a Wafer Stage , 2017, IEEE/ASME Transactions on Mechatronics.

[56]  V. Sima,et al.  High-performance numerical software for control , 2004, IEEE Control Systems.

[57]  Simone Formentin,et al.  A comparison of model‐based and data‐driven controller tuning , 2014 .

[58]  Mi-Ching Tsai,et al.  Robust and Optimal Control , 2014 .

[59]  Wodek Gawronski,et al.  Advanced Structural Dynamics and Active Control of Structures , 2004 .

[60]  Santosh Devasia,et al.  A Survey of Control Issues in Nanopositioning , 2007, IEEE Transactions on Control Systems Technology.

[61]  Tom Oomen,et al.  Bi-Orthonormal Polynomial Basis Function Framework With Applications in System Identification , 2016, IEEE Transactions on Automatic Control.

[62]  Okko H. Bosgra,et al.  System identification for achieving robust performance , 2012, Autom..

[63]  M Maarten Steinbuch,et al.  Exploiting additional actuators and sensors for nano-positioning robust motion control , 2014 .

[64]  Maarten Steinbuch,et al.  Optimally conditioned instrumental variable approach for frequency-domain system identification , 2014, Autom..

[65]  W. P. M. H. Heemels,et al.  On the potential of lifted domain feedforward controllers with a periodic sampling sequence , 2016, 2016 American Control Conference (ACC).

[66]  Andrew Packard,et al.  The complex structured singular value , 1993, Autom..

[67]  Johan Schoukens,et al.  Tensor methods for MIMO decoupling and control design using frequency response functions , 2017 .

[68]  Richard D. Braatz,et al.  Control on a molecular scale: A perspective , 2016, 2016 American Control Conference (ACC).

[69]  M. Steinbuch,et al.  Data-based control of motion systems , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[70]  Brian D. O. Anderson,et al.  Model validation for control and controller validation in a prediction error identification framework - Part I: theory , 2003, Autom..

[71]  Raymond A. de Callafon,et al.  Multivariable feedback relevant system identification of a wafer stepper system , 2001, IEEE Trans. Control. Syst. Technol..

[72]  Lennart Blanken,et al.  Design and modeling aspects in multivariable iterative learning control , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[73]  Okko H. Bosgra,et al.  LPV control for a wafer stage: beyond the theoretical solution , 2005 .

[74]  Ir.J.C. Compter.  Electro-dynamic planar motor , 2004 .

[75]  Hans Butler,et al.  Position Control in Lithographic Equipment [Applications of Control] , 2011, IEEE Control Systems.

[76]  Robert Kosut,et al.  Closed-Loop Identification via the Fractional Representation: Experiment Design , 1989, 1989 American Control Conference.

[77]  Andrew J. Fleming,et al.  Measuring and predicting resolution in nanopositioning systems , 2014 .

[78]  Andrew G. Alleyne,et al.  Basis Task Approach to Iterative Learning Control With Applications to Micro-Robotic Deposition , 2011, IEEE Transactions on Control Systems Technology.

[79]  Tom Oomen,et al.  Enhancing Flatbed Printer Accuracy and Throughput: Optimal Rational Feedforward Controller Tuning Via Iterative Learning Control , 2017, IEEE Transactions on Industrial Electronics.

[80]  Tae Tom Oomen,et al.  Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: enabling large feedforward tasks on a position-dependent printer , 2016 .

[81]  Gary J. Balas,et al.  Control of lightly damped, flexible modes in the controller crossover region , 1994 .

[82]  M Maarten Steinbuch,et al.  Identification for robust control of complex systems : algorithm and motion application , 2015 .

[83]  B. Paden,et al.  Nonlinear inversion-based output tracking , 1996, IEEE Trans. Autom. Control..

[84]  Gerd Vandersteen,et al.  Nonparametric preprocessing in system identification: A powerful tool , 2009, 2009 European Control Conference (ECC).

[85]  M Maarten Steinbuch,et al.  Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer , 2014 .

[86]  Tae Tom Oomen,et al.  Inverting nonminimum-phase systems from the perspectives of feedforward and ILC , 2017 .

[87]  Paul M. J. Van den Hof,et al.  Identification and control - Closed-loop issues , 1995, Autom..

[88]  Jan Swevers,et al.  Interpolation-Based Modeling of MIMO LPV Systems , 2011, IEEE Transactions on Control Systems Technology.

[89]  Emre Kural,et al.  96 A Survey of Iterative Learning Control Al earning-based method for high-performance tracking control , 2006 .

[90]  Michel Gevers,et al.  Towards a Joint Design of Identification and Control , 1993 .

[91]  Matthew C. Turner,et al.  A tutorial on modern anti-windup design , 2009, 2009 European Control Conference (ECC).

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

[93]  Chung Choo Chung,et al.  Guest Editorial Introduction to the Special Section on Advanced Servo Control for Emerging Data Storage Systems , 2012, IEEE Trans. Control. Syst. Technol..

[94]  Tae Tom Oomen,et al.  Synchronizing decentralized control loops for overall performance enhancement : a Youla framework applied to a wafer scanner , 2017 .

[95]  Johan Schoukens,et al.  Enhancing ℋ∞ Norm Estimation using Local LPM/LRM Modeling: Applied to an AVIS , 2014 .

[96]  Tom Oomen,et al.  Inferential Iterative Learning Control: A 2D-system approach , 2016, Autom..

[97]  C.C.H. Ma.  Comments on "A necessary and sufficient condition for stability of a perturbed system" by Q. Huang and R. Liu , 1988 .

[98]  M Maarten Steinbuch,et al.  Advanced feedforward and learning control for mechatronic systems , 2016 .

[99]  Mf Marcel Heertjes,et al.  Variable gain motion control of wafer scanners , 2016 .

[100]  Tae Tom Oomen,et al.  Advanced motion control for next-generation precision mechatronics: Challenges for control, identification, and learning , 2017 .

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

[102]  Yves Rolain,et al.  Numerically robust transfer function modeling from noisy frequency domain data , 2005, IEEE Transactions on Automatic Control.

[103]  Brian D. O. Anderson,et al.  From Youla-Kucera to Identification, Adaptive and Nonlinear Control , 1998, Autom..

[104]  R. Nordmann,et al.  A H/sub /spl infin//-weighting scheme for PID-like motion control , 2002, Proceedings of the International Conference on Control Applications.

[105]  George Papageorgiou,et al.  Distance Measures for Uncertain Linear Systems: A General Theory , 2009, IEEE Transactions on Automatic Control.

[106]  Roy S. Smith,et al.  CLOSED-LOOP IDENTIFICATION OF FLEXIBLE STRUCTURES : AN EXPERIMENTAL EXAMPLE , 1998 .

[107]  Atsuo Kawamura,et al.  Perfect tracking control based on multirate feedforward control with generalized sampling periods , 2001, IEEE Trans. Ind. Electron..

[108]  Marc M. J. van de Wal,et al.  Connecting System Identification and Robust Control for Next-Generation Motion Control of a Wafer Stage , 2014, IEEE Transactions on Control Systems Technology.

[109]  Tae Tom Oomen,et al.  On inversion-based approaches for feedforward and ILC , 2017 .

[110]  Marc M. J. van de Wal,et al.  Design framework for high-performance optimal sampled-data control with application to a wafer stage , 2007, Int. J. Control.

[111]  Mikael Norrlöf,et al.  Estimation-based ILC applied to a parallel kinematic robot , 2014 .

[112]  Raymond A. de Callafon,et al.  Filtering And Parametrization Issues In Feedback Relevant Identification Based On Fractional Model R , 1995 .

[113]  Tae Tom Oomen,et al.  Non-parametric identification of multivariable systems: A local rational modeling approach with application to a vibration isolation benchmark , 2018 .

[114]  Maarten Steinbuch,et al.  Advanced Motion Control: An Industrial Perspective , 1998, Eur. J. Control.

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