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A direct approach to modeling an industrial robot from samples of input-output data

Published online by Cambridge University Press:  09 March 2009

Ajit M. Karnik  and
Naresh K. Sinha
Ajit M. Karnik
Affiliation:
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario (Canada)
Naresh K. Sinha
Affiliation:
Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario (Canada)
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Summary

The increased demand on the performance and efficiency of industrial robots, has led to the design of sophisticated control systems. Such control systems require an accurate dynamic model of the system. A commonly used method of modeling an industrial robot, involves the description of a set of dynamic equations, relating actuator torques to loads and accelerations. These equations are generally quite complex and inconvenient for implementation on digital computers.

Another method often used for identification, is the ‘indirect method’, in which the transfer function is obtained in two steps. The discrete time model is first derived from samples of the input and output measurements, which is then transformed to the continuous-time model. A limitation of this method is that it requires the excitation to be of the ‘persistently exciting’ type, thus precluding the application of simple inputs like the step signal.

This paper describes a ‘direct’ method for identification of an ‘industrial robot’ from samples of input and output observations. Results of modeling an industrial robot and two simulations are presented. One of the simulations, and the industrial robot uses the step input as excitation. The other example was excited with an exponential input.

Type
Article
Information
Robotica , Volume 2 , Issue 3 , July 1984 , pp. 161 - 167
Copyright
Copyright © Cambridge University Press 1984

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References

1Koren, Y., Computer Control of Manufacturing Systems (McGraw Hill Book Company, 1983).Google Scholar
2Karnik, A.M. and Sinha, N.K., “The adaptive control of a robot arm for arc welding process” Proc. IEEE International Conference on Systems, Man and Cybernetics (Bombay, India, 1983) pp. 614–618.Google Scholar
3Rafauli, R., Sinha, N.K. and Tlusty, J., “A distributed microprocessor control system for an industrial robot” Proc. IEEE Conference on Mini and Microcomputers (San Francisco, U.S.A., 1981) pp. 319–323.Google Scholar
4Tlusty, J., Szvoboda, G. and Rafauli, R., “A continuous path algorithm for robot control” Proc. Canadian Conference on Robotics, Canada (1982).Google Scholar
5Astrom, K.J. and Eykhoff, P., “System identification – a survey” Automatica 7, 123–162 (1971).Google Scholar
6Sinha, N.K. and Kuzsta, B., Modeling and Identification of Dynamic Systems (Van-Nostrand-Reinhold Publishing Co., New York 1983).Google Scholar
7Eykhoff, P., System Identification (John, Wiley and Sons, , U.K., 1974).Google Scholar
8Isermann, R., Digital Control Systems (Springer-Verlag, New York, 1981).Google Scholar
9Franklin, G.F. and Powell, J.D., Digital Control of Dynamic Systems (Addison-Wesley Publishing Company, U.S.A., 1980).Google Scholar
10Sinha, N.K. and Lastman, G.J., “Identification of continuous time multivariable systems from sampled data” Int. J. Control 35, No. 1, 117–126 (1982).Google Scholar
11Landau, Y.D., Adaptive Control – the Model Reference Approach (Marcel-Dekker, New York, 1979).Google Scholar
12Karnik, A.M., “Report on the measurement of the step response of the UNIMATE–2000”, Department of Electrical and Computer Engineering, McMaster University (1984).Google Scholar
13Prasad, T. and Sinha, N.K., “Modeling of continuous time systems from sampled data using trapezoidal pulse func tions” Proc. IEEE International Conference on SMC (Bombay, India, 1983) pp. 427–430.Google Scholar
14Davies, P. and Hammond, J.K., “A comparison of Fourier and parametric methods for structural system identification” Trans. to the ASME Journal of Vibration, Acoustics, Stress, and Reliability in Design 106, 40–48 (1984).Google Scholar