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Abstract

The report describes a digital simulation study of the application of discrete prediction filters, designed from Taylor series expansion or the method of least squares, to direct digital control, where the prediction filter is inserted in the feedback path. The effects on system performance to set-point changes and load disturbances are investigated. In addition, the effects of process noise and system stability are considered. It is concluded that accuracy of prediction is not the most important property which affects control performance. Best performance is obtained using a filter which has a low sensitivity to process noise and predicts as far as possible into the future while remaining within stability constraints.

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References

Tsypkin

Ia S.

‘Sampled-data systems with extrapolating devices’, Automatic and Remote Control, Vol 19, No 5, May 1958, pp. 383–94.

Ermakov

S S

Esipovich

E M

. ‘A way of forming transfer functions of sampled data systems with extrapolating devices’, Automatic and Remote Control, Vol 19, No 5, May 1958, pp 395–401.

Steeg

C W

. ‘The design of optimum filters and predictors’, IRE Nat Convention Record, Vol 5, pt 4, 1957, pp 62–73.

Mittra

R A J

. ‘Optimum linear least-square smoothing and prediction’, Proc Nat Electronics Conf, Vol 15, 1959, pp 422–30.

Kalman

R E

Bucy

R S

. ‘New results in linear filtering and prediction theory’, J Basic Engrg, Trans ASME, Ser D.83, March 1961, pp 95–108.

Wiener

‘The extrapolation, interpolation and smoothing of stationary time series with engineering applications’ (Wiley, 1959).

Graupe

Cassir

G R

. ‘Adaptive control by predictive identification and optimisation’, IEEE Trans on Auto Control, Vol AC-12, No 2, April 1967, pp 191–4.

Coales

J F

Noton

A R M

. ‘An on-off servomechanism with predicted change over’, Proc IEE, Vol 103, Pt B, No 1, July 1956, pp 449–60.

Douce

J L

. Discussion contribution to Reference 8, Proc IEE, Vol 103, Pt B, No 10, July 1956, p 461.

10.

Mills

‘An output prediction system to improve the performance of on-off and saturating control systems’, Proc IEE, Vol 108, Pt B, Nov 1961, pp. 667–71.

11.

Chestnut

Sollecito

W E

Troutman

P H

. ‘Prediction-control system application’, Trans AIEE, Applic and Industr, Vol 80, No 55, July 1961, pp 128–35.

12.

Jonsson

J J

. ‘Discussion contribution to Reference 11, Trans AIEE, Applic and Industr, Vol 80, No 55, July 1961, pp 136–38.

13.

Bailey

M J

. ‘On line dynamic modelling for improved control’, IEE Conference on Industrial Applications of Dynamic Modelling, Sept 1969, pp. 177–82.

14.

Dey

. ‘Prediction displays: a simple way of modelling’, Control Engineering, Vol 16, No 7, July 1969, pp. 82–5.

15.

Briggs

P A N

Hammond

P H

Hughes

M T G

Plumb

G O

. ‘Correlation analysis of process dynamics using pseudo-random binary test perturbations’, Proc IMechE, Vol 179, Pt 3H, 1964, pp 37–51.

16.

Clarke

D W

. ‘Generalised-least-squares estimation of the parameters of a dynamic model’, IF AC Symposium, Prague, 1967, Paper 3.17.

17.

Becker

P W

. ‘A stability criterion for sampled data control systems’, IRE Trans on Automatic Control, Jan 1963, pp. 74–6.

18.

Roberts

P D

. ‘Stability regions of linear continuous or discrete data control systems’, Proc IEE, Nov 1969, pp 1937–9.

An Investigation into the Application of Digital Prediction Filters to Direct Digital Control Systems

Abstract

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References