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Performance of generalized predictive control with on-line model order determination for a hydraulic robotic manipulator

Published online by Cambridge University Press:  09 March 2009

A. Kotzev ,
D. B. Cherchas  and
P. D. Lawrence
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Summary

The research results described present the performance of the Generalized Predictive Control (GPC) algorithm with a changing estimator and predictor model order for a specific application. The application is a hydraulically actuated heavy duty manipulator. Hydraulically actuated robotic manipulators, used in the large resource based industries, have a complex dynamic response in which, primarily due to the hydraulic actuator subsystems, the order of the dynamic model is not initially known and can change as the manipulator is operated. A nonlinear simulation model of the manipulator system is utilized in the work and the GPC controller is implemented with a CARIMA estimator together with an on-line, gradient based estimator model order determination technique. The results given show that with proper use of the order determination technique cost function and tuning of the GPC parameters, good performance and stability can be achieved.

Keywords

Hydraulic robotGPC algorithmSimulation modelCARIMA estimation
Type
Articles
Information
Robotica , Volume 13 , Issue 1 , January 1995 , pp. 55 - 64
Copyright
Copyright © Cambridge University Press 1995

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References

1.Kotzev, A., Cherchas, D.B., Lawrence, P.D. & Sepehri, N., “Generalized Predictive Control of a Robotic Manipulator with Hydraulic Actuators” Robotica 10, part 5, 447–459 (1992).CrossRefGoogle Scholar
2.Vaha, P., “Application of Parameter Adaptive Approach to Servo Control of a Hydraulic Manipulator” Acta Polytechnka Scandlnavica, Mathematics and Computer Science 51, 3–86 (1988).Google Scholar
3.Sepehri, N., Dumont, G.A.M., Lawrence, P.D. and Sassani, F., Cascade Control of Hydraulic Actuated Manipulators (Advanced System Institute, 1990).Google Scholar
4.Clarke, D.W., Mohtadi, C., and Tuffs, P.S. “Generalized Predictive Control—Part I the Basic Algorithm” Automatica 23 (2), 137–148 (1987).Google Scholar
5.Clarke, D.W., Mohtadi, C., and Tuffs, P.S. “Generalized Predictive Control—Part II Extensions and Interpretations” Automatica 23 (2) 149–160 (1987).CrossRefGoogle Scholar
6.Edgart, b., Stability of Adaptive Controllers (Springer-Verlag, Berlin Heidelberg, New York, 1979).Google Scholar
7.Astrom, K.J. and Wittenmark, B., Adaptive Control ((Addison-Wesley Publishing Company, Reading, Massachusetts, 1989).Google Scholar
8.Fu, K.S., Gonzalez, R.C. and Lee, C.S.G., Robotics; Control, Sensing, Vision, and Intelligence (McGraw-Hill Inc., New York, NY, 1987).Google Scholar
9.Watton, J., “The Dynamic Performance of an Electro-Hydraulic Servovalve/Motor System with Transmission Line Effects” ASME Journal of Basic Engineering 190, 14–18 (03, 1987).Google Scholar
10.Kotzev, A., “Automatic Model Structure Determination for Adaptive Control” Ph.D. Thesis (Department of Mechanical Engineering, University of British Columbia, and Computer Aided Manufacturing and Robotics Laboratory (CAMROL) R.92–3, 1992).Google Scholar
11.Akaike, H., “Information Theory and Extension of the Maximum Likelihood Principle” In: Proceedings Int. Symp. Information Theory, Supp. to Problems of Control and Information Theory (1972) pp. 267–281Google Scholar
12.Isermann, R., “Practical Aspects of Process Identification” Automatica 16, 575–587 (1980).CrossRefGoogle Scholar
13.Schwarz, G., “Estimating the Dimension of a Model. Stat. 6, 2 (1978).Google Scholar
14.Rissanen, J., “Modeling by Shortest Data Description” Automatica 14, 465–471 (1978).Google Scholar
15.Guidovzi, R.P., “Canonical Structures in the Identification of Multivariable System” Automatica 11, 117–133 (1975).Google Scholar
16.Guidovzi, R.P., “Invariant Canonical Forms for System Structural and Parametric Identification” Automatica 17 (1), 117–133 (1981).CrossRefGoogle Scholar
17.Davison, E.J., “A Method for Simplifying Linear Dynamic Systems” IEEE Transactions on Automatic Control AC–17 (1), 93–101 (01, 1981).CrossRefGoogle Scholar
18.Overbeek, A.J.M. and Ljung, L., “On-Line structure Selection for Multivariable State-Space Models” Automatica 14, 529–543 (1982).Google Scholar
19.Xiao, D., Niu, S., and Fisher, D.G., “A Recursive Algorithm for Simultaneous Identification of Model Order and Parameters” IEEE Transactions on Acoustics, Speech and Signal Processing 38 (5, 884–886 (05, 1990).Google Scholar
20.Niu, S. and Fisher, D.G., “Mimo System Identification using Augmented UD Factorization” in: Proceedings of the 1991 IFAC International Symposium on Intelligent Tuning and Adaptive Control, (1991) pp. 699–703.Google Scholar
21.Wulich, D. and Kaufman, Y., “Order Estimation of Linear Time Invariant Systems” J. Franklin Institute 322 (3), 43–150 (1986).CrossRefGoogle Scholar
22.Katsikas, S.K., Likothanassis, S.D. and Laniotis, D.G., “A Model Identification with unknown Process Order” IEEE Transactions on Acoustics, Speech and Signal Processing 38 (5), 872–876 (05, 1990).CrossRefGoogle Scholar
23.Hemerly, E.M., “Order and Identification of Industrial Processes using the PLS Criterion” in: Proceedings of the 1991 IFAC International Symposium on Intelligent Tuning and Adaptive Control, (1991) pp. 293–297.Google Scholar