Advanced motion control for next-generation precision mechatronics: Challenges for control, identification, and learning

Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. Increasing requirements necessitate enhanced control performance. The aim of this paper is to identify several challenges for advanced motion control originating from these increasing accu- racy, 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, which are identified and learned from inexpensive experimental data. Several ongoing developments are outlined that constitute a 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 the spatio-temporal flexible mechanics are explicitly compensated through advanced motion control.

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

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

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

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

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

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

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

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

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

[10]  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.

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

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

[13]  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.

[14]  Daniel Y. Abramovitch,et al.  Trying to keep it real: 25 Years of trying to get the stuff I learned in grad school to work on mechatronic systems , 2015, 2015 IEEE Conference on Control Applications (CCA).

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

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

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

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

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

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

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

[24]  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.

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

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

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

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

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

[30]  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).

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

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

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

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

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

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

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

[38]  Johan Schoukens,et al.  Tensor methods for MIMO decoupling using frequency response , 2016 .

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

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

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

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

[43]  B. Pasik-Duncan.  Control-oriented system identification: An H∞ approach , 2002 .

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

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

[46]  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.

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

[48]  Marc M. J. van de Wal,et al.  Exploiting additional actuators and sensors for nano-positioning robust motion control , 2014, 2014 American Control Conference.

[49]  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.

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

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

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

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

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

[55]  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.

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

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

[58]  Graham C. Goodwin,et al.  Fundamental Limitations in Filtering and Control , 1997 .

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

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

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

[62]  Rainer Nordmann,et al.  A X ,-weighting scheme for PID-like motion control , 2004 .

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

[64]  M Maarten Steinbuch,et al.  Iterative Learning Control of Industrial Motion Systems , 2000 .

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

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

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

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

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

[70]  Suresh M. Joshi,et al.  Advanced Structural Dynamics and Active Control of Structures , 2005, IEEE Transactions on Automatic Control.

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

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

[73]  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.

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

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

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

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

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

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

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

[81]  Carlo Novara,et al.  Linear Parameter-Varying System Identification: New Developments and Trends , 2011 .

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

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