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Abstract

Real-time learning control of a hydraulic actuator is studied using a cerebellar model articulation controller (CMAC) neural network architecture. Experiments are conducted on a one-degree-of-freedom hydraulic cylinder. The main applications considered are the earth-moving equipment applications. The electrohydraulic valve used to control the flow in the hydraulic circuit is an open-centre non-pressure-compensated type of valve, the type used in most earth-moving vehicles. Large hysteresis and dead band are observed in the hydraulic system which result in a large delay and poor dynamic tracking performance. A comparison of the experimental results on the control of the hydraulic actuator for the CMAC controller and a conventional proportional-integral controller with a model-based feedforward valve transform is made.

Keywords

control hydraulic neural network CMAC earth-moving equipment

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References

Petersen

I. R.

Quadratic stabilizability of uncertain linear systems: Existence of a nonlinear stabilizing control does not imply existence of a linear stabilizing control. IEEE Trans. Autom. Control, 1985, 30, 291–293.

Plummer

A. R.

Vaughan

N. D.

Robust adaptive control for hydraulic servosystems. Trans. ASME, J. Dynamic Systems, Measmt, Control, 1996, 118, 237–244.

Sherman

S. T.

Lance

G. M.

A design study of a positioning system with electrohydraulic actuation. In Proceedings of the American Control Conference, 1987, pp. 1853–1858.

Vossoughi

Donath

Dynamic feedback linearization for electrohydraulically actuated control system. Trans. ASME, J. Dynamic Systems, Measmt, Control, 1995, 117, 468–477.

Finney

J. M.

de Pennington

Bloor

M. S.

Gill

G. S.

A pole placement controller for an electrohydraulic cylinder drive. Trans. ASME, J. Dynamic Systems, Measmt, Control, 1985, 107, 145–150.

Vaughan

N. D.

Whiting

I. M.

Microprocessor control appliedto a nonlinear electrohydraulic position servo system. In Proceedings of the Seventh Fluid Power Symposium, UK, 1986, pp. 187–198.

Daley

Application of a fast self-tuning control algorithm to a hydraulic test rig. Proc. Instn Mech. Engrs, Part C, Journal of Mechanical Engineering Science, 1987, 201(C4), 285–295.

Hwang

C. L.

Lan

C. H.

The position control of electrohydraulic servomechanism via a novel variable structure control. Mechatronics, 1994, 4(4), 369–391.

Zhao

Virvalo

Fuzzy control of a hydraulic position servo with unknown load. In Proceedings of the Second IEEE International Conference on

Fuzzy Systems, San Francisco, California, 1993, pp. 785–788 (IEEE, New York).

10.

Corbet

Sepehri

Lawrence

P. D.

Fuzzy control of a class of hydraulically actuated industrial robots. IEEE Trans. Control Systems Technol., 1996, 4(4), 419–426.

11.

Cobo

Velocity servo control system design for wheel type loader earthmoving equipment. MS thesis, University of Illinois at Chicago, 1996.

12.

Hunt

K. J.

Sbarbaro

Zbikowski

Gawthrop

P. J.

Neural networks for control systems—a survey. Automatica, 1995.

13.

Merrit

H. E.

Hydraulic Control Systems, 1967 (John Wiley, New York).

14.

Albus

J. S.

Brains, Behaviour, and Robotics, 1981 (Byte Books).

Hydraulic actuator control with open-centre electrohydraulic valve using a cerebellar model articulation controller neural network algorithm

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