Panel Discussion on Perspectives on Iterative Learning Control

1. Panel Discussion on Perspectives on Iterative Learning Control Chen, YangQuan *Singapore Science Park Design Center, Seagate Technology International Speaker:Dr Chen,YangQuan <yqchen@ieee.org> http://www.crosswinds.net/~yqchen

2. Outline of this brief talk: • Important Recent ILC Results and Future Topics • ILC-Related Patents • ILC on the Web • Sample ILC Application in Industry

3. Arimoto’s 6 Postulations on ILC • P1.Every cycle (pass, trial, batch, iteration, repetition) ends in a fixed time of duration T>0. • P2.A desired output yd(t) is given a priori over [0,T]. • P3.Repetition of the initial setting is satisfied. • P4.Invariance of the system dynamics is ensured throughout these repeated iterations. • P5.Output can be measured and the tracking error can be utilized in the construction of the next input. • P6.The system dynamics are invertible, that is, for a given desired output yd(t)with a piecewise continuous derivative, there exists a unique input ud(t) that drives the system to produce yd(t)

4. Attacks against Arimoto’s 6 Postulations on ILC • P1.S. Kawamura,Xu’s DLC (different time scale), Arif’s Local DB Querry etc • P2.varying yd over iteration(Saab, Chen, Xu, ...) • P3.Bien’s conditioning method; Chen’s ISL; Sun’s IRA (initial rectifying action) • P4.Bounded changes. • P5. Limited measurement authority. (Terminal ILC, cyclic ILC) • P6.Partially invertible - NMP system.(Amann et al, Roh et al etc) Question:What is your prior knowledge? How to best design your ILC based on this (limited!) prior knowledge? - ILC design issues have to be explicitly addressed!

5. Recent Results General Trend: from “Analysis” to “Design” • Analysis: • Attack the Arimoto’s classical 6 Postulates for ILC. • Structurally known uncertain nonlinear systems. System class… Combined Feedforward-Feedback analysis! • Add practical constraints in analysis: changing delay, anti-windup • Spatial ILC (state-dependent repetitiveness), distributed parameter system, redundancy in control authorities... • Design: • How to explicitly use the available (assumed) prior knowledge? • Systematic design method - e.g. via noncausal filtering, Local Symmetrical Integration (LSI) etc. • Supervisory Iterative Learning Control (e.g. planning while tracking via ILC)

6. ILC Peripherals Related topics and possible Future ILC peripherals: • Iterative Feedback Tuning • Iterative Dynamic Programming • Run-to-Run (Batch) Process Control • Real time SPC (Statistic Process Control) • Multi-rate Sampling and Signal Processing • Fundamental Performance Limitations (for ILC) • Parsimoniousness (less memory, less CPU budget etc)

7. ILC Patents • US03555252 (1967) : Learning control of actuators in control system. (ICARCV’2000, a paper will discuss US03555252 with possible relationship with ILC and new research opportunities motivated) • US05220265 (1993) : Discrete-type repetitive control method and an apparatus therefor • US05563794 (1996) : Repetitive control of thermal shock disturbance • US05666034 (1997) : Method for controlling velocity of a rotary motor and an apparatus therefor • US05740090 (1998) : Filter, repetitive control system and learning control system both provided with such filter. • … more at http://cicserver.ee.nus.edu.sg/~ilc/ILC/ilc_patents/ or http://www.patents.ibm.com/

8. ILC on the WWW • ILC Web Server:http://cicserver.ee.nus.edu.sg/~ilc • ILC Reference Library: Searchable and Contributable (ready soon) • Hyperlinks to ILC researchers and groups. • ILC UPDATES 2000: http://www.crosswinds.net/~learningcontrol • ILC Application Stories : coming soon! Send your ILC application stories to Dr YangQuan Chen (yqchen@ieee.org)

9. Sample Industrial Apllication Seagate solutions to written-in repeatable runout due to STW (Servo-track-writer): • J. Mooris et al. Compensations of written-in errors in servo.US Patent 6,069,764 • B. Qiang, K. Gomez, Y. Chen, K. Ooi, Repeatable runout compensation using iterative learning control in a disc storage system.US Patent Pending Serial No. 60/132,992. • Y. Chen et al. Repeatable runout compensation using a learning algorithm with scheduled parameters. US Patent Pending Serial No. 60/132,992.

10. Typical hard disk drive

11. Typical embedded servo

12. RRO: repeatable runout 1) spindle motor: AFC 2) WI-RRO: ZAP better PES, better track following for higher TPI... Why ZAP (Zero-Acceleration-Path)?

13. Disturbances: d: torque disturbances,D/A & poweramp noise, spindle motor vibration, disk flutter, eccentricity, bending. n: measurement noise (PES, electrical, A/D quantization noise) WI-Track is the reference ZAP Table ZAP d + y + + e P C RRO Before ZAP _ PES/RRO PEADC RRO After ZAP + + + + WI_Track n How to compensation? RROZAP table + d ZAP + + y e P C + PES/RRO - PES_ADC + + + + n WI-Track Servo Objective: Reduce PES RRO Solutions: • AFC, Repetitive Control, disturbance observer • ZAP

14. How to get the compensation table?

15. RRO before and after ZAP RRO before ZAP RRO after ZAP

16. RRO before and after ZAP RRO before ZAP RRO after ZAP

17. ZAP table Spectrum ZAP table

18. Practical Challenges Every track needs a table ILC at every track For 21KTPI U10 drive, total track # 19,000 Suppose each table learning needs 10 rev. (1 revolution=11.1msec). Total process time = 35min * #headers Extra 2 hours for 4 hdrs!! Where to store the tables without affecting HDD’s capacity? So, It’s a curve (or, profile) identification problem! Long term stability of ILC is not an issue Maximum reduction in WI-RRO with minimum possible time How? Ideas hidden in our patent claims :)

19. Thank you! Thank you! Q/A Session Please visit ILC website: http://cicserver.ee.nus.edu.sg/~ilc or, http://www.crosswinds.net/~learningcontrol