Air motor system modelling using extended neural network algorithms

• Autorzy: Marumo R. , Tokhi M. O.
• Języki publikacji: EN

Abstrakty

EN

This paper investigates the development of neuro-modelling approaches for a highly non-linear system. The work is motivated by the fact that the response of a pneumatic drive is very slow, which leads to inability of the system to attain set points due to high hysteresis. Also the dynamic model of the pneumatic system is highly non-linear, which greatly complicates controller design and development. To address these problem areas, two streams of research efforts have evolved. These are: using conventional methods to develop a modelling and control strategy and adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three main regions, namely low speed, medium speed and high speed. The system is highly non-linear in the low speed region and hence a neuro-modelling approach is proposed.

Słowa kluczowe

EN

control system analysis; intelligent control; air motor control; air motor system modeling; neural network algorithm; pneumatic drives; neural networks

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2005
• Tom: Vol. 31, no 3
• Strony: 87--101
• Opis fizyczny: Bibliogr. 21 poz., wykr.

Twórcy

autor

Marumo R.

• Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.

autor

Tokhi M. O.

• Department of Automatic Control and Systems Engineering, The University of Sheffield, UK.

Bibliografia

• [1] Tokhi M. O., Al-Miskiry M., Brisland M., Real-time control of air motors using a pneumatic H- bridge, Control Engineering Practice, Vol. 9, No. 4, 2001, 449-457.
• [2] Tokhi M. O., Reynolds I. N., Brisland M., Real-time control of a radial piston air motor, 1FAC World Congress, Barcelona, 21-26 July 2002.
• [3] Marumo R., Tokhi M. O., Modelling and Control of a Pneumatic Motor, Proceedings of the First African Control Conference, December, Cape Town, South Africa, 2003, 100-112.
• [4] Sanville F. E., A new method of specifying the flow capacity of pneumatic fluid power valves, Hydraulic Pneumatic Power, Vol. 17, No. 195, 1971.
• [5] Anderson B. W., The analysis and design of pneumatic systems, Robert E. Krieger Publishing Company, Florida, 1985.
• [6] Sorli M., PARSTORELLI S., Performance of a pneumatic force controlling servo system: influence of valves conductance, Robotic and Autonomous Systems, Vol. 30, 2000, 283-300.
• [7] Choi S. H., Lee C. O., Cho H. S, Friction compensation control of an electropneumatic servo valve by using an evolutionary algorithm, Proc. of the Institute of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, Vol. 214, No. 3, 2000, 173-184.
• [8] Backe W., Ohligschlaeger O., Model of heat transfer in pneumatic chambers, Journal of Fluid Control, Vol. 20, No. 1, 1989, 61-78.
• [9] Masaaki T., Toshiharu K., Toshiharu F., Kazutoshi S., Measurements of temperature rot pneumatic cylinder in action and the thermodynamic analysis, Proceedings of the fifth Scandinavian International Conference on Fluid Power, Vol. 2, Linkoping, Sweden, 1997, 335-345.
• [10] Belforte G., D’Alfio N., Raparelli T., Experimental analysis of friction force in pneumatic cylinder, The Journal of Fluid Control, Vol. 20, No. 1, 1989, 42-60.
• [11] Nouri B. M. Y. et al., Modelling a pneumatic servo positioning system with friction, Proceedings of the American Control Conference, Vol. 2, 2000, 1067-1071.
• [12] Wang J., Pu J., Moore P., Zhang Z., Modelling study and servo-control of air motor systems, International Journal of Control, Vol. 71, No. 3, 1998, 459-476.
• [13] Wang H., Mo J., Chen N., Hybrid fuzzy logic algorithm for position control of pneumatic actuator with 3/2-way solenoid valves, Proceedings of the Institution of Mechanical Engineers, Part C, .Journal of Mechanical Engineering Science, Vol. 210, No. C2, 1996, 167-176.
• [14] Hltchcox A. L., Performance Insurance for air motors, Hydraulics & Pneumatics, Vol. 48, No. 10, 1995, 63-68.
• [15] Marumo R., Tokhi M. O., Intelligent modelling and control of a pneumatic motor, Proc. 17th Canadian Conf. on Electrical and Computer Engineering, Niagara Falls, Canada, 2004, 1163-1166.
• [16] Liu G. P., Kadirrkamanathan V., Billings S. A., Variable neural networks for adaptive control of non-linear systems, IEEE Trans. Syst., Man, Cybern. C, Vol. 29, 1999, 34—43.
• [17] Narendra K. S., Parthasarathy K., Identification and control of dynamics systems using neural networks, IEEE Trans. Neural Networks, Vol. 1, 1990, 4-27.
• [18] Garrett A., Dobbs R., Defining neural network parameters for prediction of head movements in virtual environment applications [online], available from world wide web: http://cnts.uia.ac.be/ conll98/pdf/073078sc.pdf
• [19] Billings S. A., Voon W., Correlation-based model validity tests for non-linear models, International Journal of Control, 44, 1986, 235-244.
• [20] http://osiris.sunderland.ac.uk/~cs0kmc/357-044.pdf
• [21] Demuth H., Beale M., Neural networks toolbox user’s guide v.4, The Math Works, Inc, 2000.