Precision Motion Control: Intelligent Mechanisms, Morphing Mechanisms

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 234)

Abstract

Engineering advances are often made at the boundary between two fields. This chapter considers synergy between the design of mechanisms used in manufacturing equipment and the design of control systems. Mechanism design often assumes constant velocity of the input shaft, but variations in inertia seen by the driving motor produce speed fluctuations. Typical feedback control cannot fix this, but smart control methods such as iterative learning control and repetitive control can. They can make the mechanism perform in hardware as it was intended to perform. With appropriate sensors they can also fix errors in manufacture and can also make hardware behave as if it were an improved design. The improvement in performance is achieved in software. We call these mechanisms/control systems intelligent mechanism or morphing mechanisms. Examples are discussed.

Keywords

Mechanisms Precision motion control Iterative learning control Repetitive control Intelligent mechanisms Morphing mechanisms 

References

  1. 1.
    Bien Z, Xu J-X (eds) (1998) Iterative learning control: analysis, design, integration and applications. Kluwer Academic Publishers, BostonGoogle Scholar
  2. 2.
    Moore K, Xu J-X, guest editors (2000) Special issue on iterative learning control. Int J Control 73(10): 819–823Google Scholar
  3. 3.
    Longman RW (2000) Iterative learning control and repetitive control for engineering practice. Int J Control 73(10): 930–954. Special issue on iterative learning controlGoogle Scholar
  4. 4.
    Elci H, Longman RW, Phan MQ, Juang J-N, Ugoletti R (2002) Simple learning control made practical by zero-phase filtering: applications to robotics. In: Basu S, Swamy MNS (guest editors) IEEE transactions on circuits and systems I: fundamental theory and applications, Piscataway, June 2002, vol 49(6), pp 753–767. Special issue on multidimensional signals and systemsGoogle Scholar
  5. 5.
    Longman RW (2010) On the theory and design of linear repetitive control systems. Eur J Control 16(5): 447–496. Special section on Iterative Learning Control, Guest Editor: Ahn H-SGoogle Scholar
  6. 6.
    Chew MS, Longman RW, Phan MQ (2004) Intelligent mechanisms. In: Proceedings of the 28th biennial mechanisms and robotics conference. ASME design engineering technical conference DETC2004-57553, Salt Lake City, 28 Sept–3 Oct 2004Google Scholar
  7. 7.
    Phetkong N, Chew MS, Longman RW (2005) Morphing mechanisms part 1: using iterative learning control to morph cam follower motion. Am J Appl Sci 2(5):897–903CrossRefGoogle Scholar
  8. 8.
    Phetkong N, Chew MS, Longman RW (2005) Morphing mechanisms part 2: using repetitive control to morph cam follower motion. Am J Appl Sci 2(5):904–909CrossRefGoogle Scholar
  9. 9.
    Xu J-X, Tan Y (2003) Linear and nonlinear iterative learning control (Lecture notes in control and information sciences), Springer, BerlinGoogle Scholar
  10. 10.
    Longman RW, Chang C-K, Phan M (1992) Discrete time learning control in nonlinear systems. A collection of technical papers, 1992 AIAA/AAS astrodynamics specialist conference, Hilton Head, Aug 1992, pp 501–511Google Scholar
  11. 11.
    Li J, Longman RW, Schulz VH, Bock HG (1998) Implementing time optimal robot maneuvers using realistic actuator constraints and learning control. Adv Astronaut Sci 99:355–374Google Scholar
  12. 12.
    Longman RW, Mombaur KD, Panomruttanarug B (2008) Designing iterative learning control subject to actuator limitations using QP methods. In: Proceedings of the AIAA/AAS astrodynamics specialist conference, Hawaii, Aug 2008Google Scholar
  13. 13.
    Longman RW, Mombaur KD (2009) Iterative learning control in nonlinear systems using state estimation for relinearization. Adv Astronaut Sci 134:1721–1735Google Scholar
  14. 14.
    Giese G, Longman RW, Bock HG (2004) Mechanical assessment of time-optimal robot motion. Comput Mech 33(2):121–128MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Chew M, Freudenstein F, Longman RW (1983) Application of optimal control theory to the synthesis of high-speed cam-follower systems. Part I: optimality criterion. ASME Transactions. J Mech Transm Autom Design 105(3): 577–584, Sept 1983Google Scholar
  16. 16.
    Chew M, Freudenstein F, Longman RW (1983) Application of optimal control theory to the synthesis of high-speed cam-follower systems. Part II: system optimization. ASME Transactions. J Mech Transm Autom Design 105(3): 585–591, Sept 1983Google Scholar
  17. 17.
    Mennicke S, Longman RW, Chew M-S, Bock HG (2004) High speed automotive cam design using direct multiple shooting optimal control techniques. In: Proceedings of the 28th biennial mechanisms and robotics conference, ASME design engineering technical conference DETC2004-57415, Salt Lake City, Sept–Oct 2004Google Scholar
  18. 18.
    Mennicke S, Longman RW, Chew M-S, Bock HG (2004) A CAD package for high-speed cam design based on direct multiple shooting optimal control techniques. In: Proceedings of the 30th ASME design automation conference, ASME design engineering technical conference DETC2004-57410, Salt Lake City, Sept–Oct 2004Google Scholar
  19. 19.
    Chew M, Phan MQ (1994) Application of learning control theory to mechanisms, part 1 – inverse kinematics and parametric error compensation. In: Proceedings of the ASME 23rd biennial mechanisms conference, Minneapolis, 1994, DE-vol 71, ASME 1994, pp 25–32Google Scholar
  20. 20.
    Phan MQ, Chew M, Synthesis of Four-Bar Function Generators by an iterative learning control procedure. In: Proceedings of the ASME 24th biennial mechanisms conference, Irvine, 1996, 96-DETC/MECH-1219Google Scholar
  21. 21.
    Chen HJ, Longman RW, Chew M (2004) Eliminating structure error in real-time adjustable four-bar path generators using iterative learning control. In: ASME DETC 2004, Salt Lake City, 2004Google Scholar
  22. 22.
    Thuemmel T, Brandl M (1996) Active balancing of joint forces in high-speed linkages by redundant drives and learning control. In: Proceedings of the ASME design engineering technical conference, Irvine, 18–22 Aug 1996, 96-DETC/MECH-1572Google Scholar
  23. 23.
    Chew M, Phan MQ (1994) Application of learning control theory to mechanisms, part 2 – reduction of residual vibrations in high-speed electromechanical bonding machines. In: Proceedings of the ASME 23rd biennial mechanisms conference, Minneapolis, 1994, DE-vol 71, ASME 1994, pp 33–40Google Scholar
  24. 24.
    Chew M, Wongratanaphisan T, Lu YC (1998) Learning control of a high-speed cam dynamic system in the presence of viscous damping and Coulomb friction, theory and experiment. In: Proceedings of 1998 ASME design engineering technical conference, Atlanta, 1998, DETC 98/MECH-5971Google Scholar
  25. 25.
    Hsin YP, Longman RW, Solcz EJ, de Jong J (1997) Experiments bridging learning and repetitive control. Adv Astronaut Sci 95:671–690Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringColumbia UniversityNew YorkUSA
  2. 2.Lehigh UniversityBethlehemUSA
  3. 3.Thayer School of EngineeringDartmouth CollegeHannoverUSA

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