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KSME International Journal

, 12:1104 | Cite as

Implementation of a robust dynamic control for SCARA robot

  • Jang Myung Lee
  • Min-Cheol Lee
  • Kwon Son
  • Man Hyung Lee
  • Sung Hyun Han
Article

Abstract

A control system for SCARA robot is designed for implementing a robust dynamic control algorithm. This study focuses on the use of DSPs in the design of joint controllers and interfaces in between the host controller and four joint controllers and in between the joint controllers and four servo drives. The mechanical body of SCARA robot and the servo drives, are selected from the commercially available products. The four joint controllers, assigned to each joint separately, are combined into a common system through the mother board hardwarewise and through the global memory softwarewise. The mother board is designed to connect joint controllers onto the board through the slots adopting PC/104 bus structures. The global memory stores the common data which can be shared by joint controllers and used by the host computer directly, and it virtually combines the whole system into one. To demonstrate the performance and efficiency of the system, a robust inverse dynamic algorithm is proposed and implemented for a faster and more precise control. The robust inverse dynamic algorithm is basically derived from an inverse dynamic algorithm and a PID compensator. Based upon the derived dynamic equations of SCARA robot, the inverse dynamic algorithm is initially implemented with l msec of control cycle—0.3 msec is actually used for the control algorithm—in this system. The algorithm is found to be inadequate for the high speed and precision tasks due to inherent modelling errors and time-varying factors. Therefore a variable PID algorithm is combined with the inverse dynamic algorithm to reinforce robustness of control. Experimental data using the proposed algorithm are presented and compared with the results obtained from the PID and the inverse dynamic algorithms.

Key Words

Robust Dynamic Control System PID Disturbance DSP 

Nomenclature

D(q)

is ann×n inertia matrix

\(C(q, \dot q)\)

represents the centrifugal and Coriolis terms

G(q)

is ann×1 gravity vector

\(N(q, \dot q)\)

is equal to\(C(q, \dot q)\)+G(q)

τ

is then×1 torque vector

Jm

is an inertia matrix of motor

Bm

is a friction matrix of motor

R

is a gear ratio matrix

πm

represents the torque supplied by the actuator

u

represents a control input vector

KP

is a proportional gain coefficient

K1

is an integral gain coefficient

DD

is a differential gain coefficient

KD

is equal toq m d -q m

η

is equal to -\([J_m + \overline {D_m } (q_m )]^{ - 1} \varepsilon \)

is defined by\(\left\| {\ddot e_i } \right\|\) and non diagonal terms ofD m (q m )

\(\overline {D_m } (q_m )\)

is a diagonal submatrix ofD m (q m )

L

is the acronym of large

M

is the acronym of medium

H

is the acronym of high

μE(x)

is the input membership function of position error for axes 1, 2, and 4

μV(y)

is the input membership function of velocity error for axes 1, 2, and 4

μKD(z)

is the output membership function ofK D for axes 1, 2, and 4

KI(z)

is the output membership function ofK 1 for axes 1, 2, and 4

μPE(x)

is the input membership function of positive z-directional position error for axis 3

μPV(y)

is the input membership function of positive z-directional velocity error for axis 3

μPKD(z)

is the output membership function of positive z-directionalK D for axis 3

μPKI(z)

is the output membership function of positive z-directionalK 1 for axis 3

μME(x)

is the input membership function of negative z-directional position error for axis 3

μMV(y)

is the input membership function of negative z-directional velocity error for axis 3

μMKD(z)

is the output membership function of negative z-directionalK D for axis 3

μMKI(z)

is the output membership function of negative z-directionalK 1 for axis 3

λi

represents weights for the input membership functions

References

  1. Chen, C. L. and Chang, F. Y., 1996, “Design and Analysis of Neural/Fuzzy Variable Structural PID Control System,”IEE Proc. Control Theory Appl., Vol. 143, No. 2, pp. 200–208.MATHCrossRefGoogle Scholar
  2. Digital Signal Processing Products, 1989,Digital Signal Processing Applications with the TMS320 Family, Vol. 1, 2, 3, Texas Instruments Inc.Google Scholar
  3. Digital Signal Processing Products, 1993,TMS320C5X User’s Guide, Texas Instruments Inc.Google Scholar
  4. Franklin, G. F., Powell, J. D., and Enami-Naeini, A., 1986,Feedback Control of Dynamic System, Addison-Wesley.Google Scholar
  5. Kelly, R. and Salgado, R., 1994, “PD Control with Computed Feedforward of Robot Manipulators: A Design Procedure,”IEEE Trans. Robo. and Auto., Vol. 10, No. 4.Google Scholar
  6. Lewis, F. L., Abdallak, C. T., and Dawson, D. M., 1993,Control of Robot Manipulators, Macmillan Publishing Company.Google Scholar
  7. Lin, K. S., Frantz, G. A., and Simar, R. Jr., 1987, “The TMS320 Family of Digital Signal Processors,”IEEE Proc., Vol. 75, No. 9.Google Scholar
  8. Maeno, T., and Kobata, M., 1972, “AC Commulatorless and Brushless Motor,”IEEE Trans. Power Appl. Syst., Vol. PAS-91, pp. 1476–1484.CrossRefGoogle Scholar
  9. Mamdani, E. H., 1977, “Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Systems,”IEEE Trans. Com., C-26, pp. 1182–1191.MATHCrossRefGoogle Scholar
  10. Pillay, P., and Krishnan, R., 1989, “Modeling, Simulation, and Analysis of Permanent-Magnet Motor Drives, Part II: The Brushless DC Motor Drive,”IEEE Trans. Ind. Appl., Vol. 25, No. 2.Google Scholar
  11. Rocco, P., 1996, “Stability of PID Control for Industrial Robot Arms,”IEEE Trans. Robo. and Auto., Vol. 12, No. 4.Google Scholar
  12. Shyu, K. K., Chu, P. H., and Shang, L. J., 1996, “Control of Rigid Robot Manipulators via Combination of Adaptive Sliding Mode Control and Compensated Inverse Dynamics Approach,”IEE Proc. Control Theory Appl., Vol. 143, No. 3.Google Scholar
  13. Song, Y. D., Mitchell, T. L., and Lai, H. Y., 1994, “Control of a Class of Nonlinear Uncertain Systems via Compensated Inverse Dynamics Approach,”IEEE Trans., AC-39, pp. 1866–1871.MATHMathSciNetGoogle Scholar
  14. Spong, M. W., and Ortega, R., 1994, “On Adaptive Inverse Dynamics Control of Rigid Robots,”IEEE Trans., AC-39, pp. 1866–1871.Google Scholar
  15. Takegaki, M., and Arimoto, S., 1981, “A New Feedback Method for Dynamic Control of Manipulator,”ASME Trans. of Dynamic System, Measurement, and Control, Vol. 103, pp. 119–125.MATHCrossRefGoogle Scholar
  16. Zubek, J., Abbondanti, A., and Nordby, C. J., 1975, “Pulsewidth Modulated Inverter Motor Drives with Improved Modulation,”IEEE Trans. Ind. Appl., Vol. 1A-11, pp. 695–703.CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers (KSME) 1998

Authors and Affiliations

  • Jang Myung Lee
    • 1
  • Min-Cheol Lee
    • 2
  • Kwon Son
    • 2
  • Man Hyung Lee
    • 2
  • Sung Hyun Han
    • 3
  1. 1.Department of Electronics Engineering and Research Institute of Mechanical TechnologyPusan National UniversityPusanKorea
  2. 2.School of Mechanical Engineering and Research Institute of Mechanical TechnologyPusan National UniversityPusanKorea
  3. 3.Department of Mechanical EngineeringKyungnam UniversityMasanSouth Korea

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