Neural networks and hydraulic control - From simple to complex applications

Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering

Volumn 213, Issue 5, 1999, Pages 349-358

  Burton, R T ^a   Ukrainetz, P R ^a   Nikiforuk, P N ^a   Schoenau, G J ^a  

^a UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

Hydraulic application; MIMO electrohydraulic servo system; Neural networks; Pattern reproduction; PID neural controller

Indexed keywords

CONTROL EQUIPMENT; HYDRAULIC CONTROL EQUIPMENT; NONLINEAR SYSTEMS; REAL TIME SYSTEMS; SERVOMECHANISMS;

HYDRAULIC APPLICATION; MIMO ELECTROHYDRAULIC SERVO-SYSTEMS; PATTERN REPRODUCTION; PID NEURAL CONTROLLERS;

NEURAL NETWORKS;

FLUID POWER; FLUID POWER HYDRAULICS; HYDRAULIC CONTROL SYSTEM; NEURAL NETWORK;

EID: 0033405365     PISSN: 09596518     EISSN: None     Source Type: Journal    
DOI: 10.1243/0959651991540205     Document Type: Article

References (12)

1. Edge, K. A. and Figueredo, K. R. A. An adaptively controlled electrohydraulic servomechanism. Proc. Instn Mech. Engrs, 1987, 201, 175-189.
2. Kainou, S. H., Rui, H. and Ikeya, M. Application of model reference adaptive control system for active loading system. In Proceedings of the First International Symposium on Fluid Power Transmission and Control, Beijing, PR China, 1991, pp. 201-204.
3. Ichikawa, Y. and Sawa, T. Neural network application for direct feedback controllers. IEEE Trans. Neural Networks, 1992, 3(2), 224-231.
4. Chen, S. and Billings, S. A. Neural networks for dynamical system modeling and identification. Int. J. Control, 1992, 56(2), 319-346.
5. Ramden, T. Condition monitoring and fault diagnosis of fluid power systems-approaches with neural networks and parameter identification. PhD thesis, Division of Fluid Power, Linkoping University, Sweden, 1998.
6. Khoshzaban-Zavarehi, M. On-line condition monitoring and fault diagnosis in hydraulic system components using parameter estimation and pattern classification. PhD thesis, University of British Columbia, 1997.
7. Lippmann, R. An introduction to computing with neural nets. IEEE Acoust., Speech Signal Processing Mag., 1997, 4(2), 4-22.
8. Narendra, K. S. and Parthasarathy, K. Identification and control of dynamic systems using neural networks. IEEE Trans. Neural Networks, March 1990, 1(1).
9. Burton, R. T., Sargent, C. M., Schoenau, G. J. and Anderson, D. The use of multiple independent gains for a repetitive low frequency duty cycle in a hydraulic system. In Proceedings of the Second Japan Hydraulic and Pneumatic Society International Symposium on Fluid Power, Tokyo, Japan, September 1993.
10. Qian, W. Neural network control of non-linear hydraulic system. MSc thesis, University of Saskatchewan, Canada, 1998.
11. Zhang, H., Ukrainetz, P. R., Nikiforuk, P. N. and Burton, R. T. Implementation of neural network approach in MIMO electrohydraulic servosystem control. In Proceedings of the International Conference on Control, University of Exeter, September 1996, pp. 1468-1473.
12. Jin, L., Nikiforuk, P. N. and Gupta, M. M. Adaptive tracking of SISO nonlinear systems using multilayered neural networks. In Proceedings of the 1992 American Control Conference, Chicago, Illinois, 1992, pp. 56-60.