Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-24T04:11:50.616Z Has data issue: false hasContentIssue false
  • English
  • Français

D-type iterative learning control without resetting condition for robot manipulators

Published online by Cambridge University Press:  30 March 2011

Farah Bouakrif
Farah Bouakrif*
Affiliation:
LAMEL Laboratory, University of Jijel, BP. 98, Ouled Aissa, Jijel 18000, Algeria
*
*Corresponding author. E-mail: f.bouakrif@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

This paper deals with iterative learning control (ILC) design to solve the trajectory tracking problem for rigid robot manipulators subject to external disturbances, and performing repetitive tasks. A D-type ILC is presented with an initial condition algorithm, which gives the initial state value in each iteration automatically. Thus, the resetting condition (the initial state error is equal to zero) is not required. The λ-norm is adopted as the topological measure in our proof of the asymptotic stability of this control scheme, over the whole finite time-interval, when the iteration number tends to infinity. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Keywords

Iterative learning controlInitial condition algorithmλ-normRobot manipulators
Type
Articles
Information
Robotica , Volume 29 , Issue 7 , December 2011 , pp. 975 - 980
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control (Wiley, New York, 1989).Google Scholar
2.Asada, H. and Slotine, J. J. E., Robot Analysis and Control (Wiley, New York, 1986).Google Scholar
3.Arimoto, S. and Miyazaki, F., “Stability and Robustness of PID Feedback Control for Robots Manipulators of Sensory Capability,” in Robotics Research, 1st Int. Symp. (Brady, M. and Paul, R. P., eds.) (Cambridge, MA: MIT Press, 1984).Google Scholar
4.Luh, J. Y. S., Walker, M. W. and Paul, R. C. P., “Resolved-acceleration control of mechanical manipulators,” IEEE Trans. Autom. Control AC-25 (3), 468–474 (1980).CrossRefGoogle Scholar
5.Ortega, R. and Spong, M. W., “Adaptive motion control of rigid robots: A tutorial,” Automatica 25 (6), 877–888 (1989).CrossRefGoogle Scholar
6.Slotine, J. J. E. and Sastry, S. S., “Tracking control of nonlinear systems using sliding surface with application to robot manipulator,” Int. J. Control 38, 465–492 (1983).CrossRefGoogle Scholar
7.Chang, Y. C. and Chen, B. S., “Robust tracking designs for both holonomic and nonholonomic constrained mechanical systems: Adaptive fuzzy approach,” IEEE Trans. Fuzzy Syst., 8, 46–66 (2000).CrossRefGoogle Scholar
8.Bouakrif, F., Boukhetala, D. and Boudjema, F., “Passivity based controller- observer for robot manipulators,” Int. J. Robot. Autom. 25 (1), 1–8, (2010).Google Scholar
9.Uchiyama, M., “Formation of high-speed motion pattern of a mechanical arm by trial,” Trans. Soc. Instrum. Control Eng. (Japan) 14 (6), 706–712 (1978).CrossRefGoogle Scholar
10.Arimoto, S., Kawamura, S. and Miyazaki, F., “Bettering operation of robots by learning,” J. Robot. Syst. 1 (2), 123–140 (1984).CrossRefGoogle Scholar
11.Xu, J. X. and Tan, Y., “Linear and nonlinear iterative learning control,” In: Lecture Notes in Control and Information Sciences (Springer Verlag, Berlin, 2003).Google Scholar
12.Tayebi, A., “Adaptive iterative learning control for robot manipulators,” Automatica 40, 1195–1203 (2004).CrossRefGoogle Scholar
13.Sun, M., Ge, S. S. and Mareels, I. M. Y., “Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning,” IEEE Trans. Robot. 22 (3), 563–568 (2006).Google Scholar
14.Bouakrif, F., Boukhetala, D. and Boudjema, F., “Iterative learning control for robot manipulators,” Arch. Control Sci. 17 (1), 5–17, (2007).Google Scholar
15.Bouakrif, F., “Reference Model Iterative Learning Control for Linear Systems,” Proceedings of Mediterranean Conference on Control and Automation, Marrakech, Morocco (June 23–25, 2010), pp. 1637–1642.CrossRefGoogle Scholar
16.Bouakrif, F., “Reference model iterative learning control for nonlinear systems with repeatable and non repeatable uncertainties,” Int. J. Adv. Manuf. Technol. 51, 1159–1169, (2010).CrossRefGoogle Scholar
17.Tayebi, A., “Analysis of two particular iterative learning control schemes in frequency and time domains,” Automatica 43, 1565–1572 (2007).CrossRefGoogle Scholar
18.Cai, Z., Freeman, C. T., Lewin, P. L. and Rogers, R., “Iterative learning control for a non-minimum phase plant based on a reference shift algorithm,” Control Eng. Pract. 16, 633–643 (2008).CrossRefGoogle Scholar
19.Sadegh, N., Horwitz, R., Kao, W. and Tomizuka, M., “A unified approach to the design of adaptive and repetitive controllers for robotic manipulators,” ASME J. Dyn. Syst. Meas. Control 112, 618–629 (1990).CrossRefGoogle Scholar
20.Lee, K. H. and Bien, Z., “Initial condition problem of learning control,” Proc. Inst. Electr. Eng. D, Control Theor. Appl. 138 (6), 525–528 (1991).CrossRefGoogle Scholar
21.Porter, B. and Mohamed, S. S., “Iterative learning control of partially irregular multivariable plants with initial state shifting,” Int. J. Syst. Sci. 22 (2), 229–235 (1991).CrossRefGoogle Scholar
22.Jang, T., Choi, C. and Ahn, H., “Iterative learning control in feedback systems,” Automatica 31, 243–248 (1995).CrossRefGoogle Scholar
23.Park, K. H. and Bien, Z., “A generalized iterative learning controller against initial state error,” Int. J. Control 73 (10), 871–881 (2000).CrossRefGoogle Scholar
24.Sun, M. and Wang, D., “Iterative learning control with initial rectifying action,” Automatica 38 (7), 1177–1182 (2002).CrossRefGoogle Scholar
25.Chen, Y., Wen, C., Gong, Z. and Sun, M., “An iterative learning controller with initial state learning,” IEEE Trans. Autom. Control 44 (2), 371–376 (1999).CrossRefGoogle Scholar
26.Xu, J. X. and Yan, R., “On initial conditions in iterative learning control,” IEEE Trans. Autom. Control 50 (9), 1349–1354 (2005).Google Scholar
27.Berghuis, H., Model Based Control: From Theory to Practice Ph.D. Thesis (The Netherlands: University of Twent, 1993).Google Scholar
28.Choi, J. Y., Uh, J. and Lee, J. S., “Iterative Learning Control of Robot Manipulator with I-Type Parameter Estimator,” Processing of the American Control Conference, Arlington, VA, USA (June 25–27, 2001) pp. 646–651.Google Scholar
29.Shim, H., Son, Y. and Seo, J., “Semi-Global Observer for Multi-Output Nonlinear Systems,” Syst. Control Lett. 42 (3), 233–44 (2001).CrossRefGoogle Scholar
30.Horowitz, R., “Learning control of robot manipulators,” ASME J. Dyn. Syst. Meas. Control 115, 402–411 (1993).CrossRefGoogle Scholar
31.Nicosia, S. and Tomei, P., “Robot control by using only joint position measurements,” IEEE Trans. Autom. Control 35 (9), 1058–1061 (1990).CrossRefGoogle Scholar
32.Berghuis, H. and Nijmeijer, H., “A passivity approach to controller observer design for robots,” IEEE Trans. Robot. Autom. 9 (6), 740–734 (1993).CrossRefGoogle Scholar
33.Vidyasagar, M., Nonlinear Systems Analysis (Prentice-Hall, Englewood Cliffs, NJ, USA, 1993).Google Scholar
34.Lozano, R. and Taoutaou, D., Commande Adaptative et Application (Hermes Science Publications, Paris, 2001).Google Scholar