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TECHNICAL PAPERS

State-Space Dynamic Performance Preview-Predictive Controller

[+] Author and Article Information
Andrzej W. Ordys1

Faculty of Engineering, Kingston University in London, Roehampton Vale, Friars Avenue, London SW15 3DW, United Kingdoma.ordys@kingston.ac.uk

Masayoshi Tomizuka

Mechanical Engineering Department, University of California, Berkeley, 5100B Etcheverry Hall, Berkeley, CA 94720-1740tomizuka@me.berkeley.edu

Michael J. Grimble

Industrial Control Centre, University of Strathclyde, 50 George Street, Glasgow G1 1QE, United Kingdomm.grimble@eee.strath.ac.uk

1

Corresponding author.

J. Dyn. Sys., Meas., Control 129(2), 144-153 (Aug 02, 2006) (10 pages) doi:10.1115/1.2431810 History: Received August 05, 2004; Revised August 02, 2006

The paper discusses state-space generalized predictive control and the preview control algorithms. The optimization procedure used in the derivation of predictive control algorithms is considered. The performance index associated with the generalized predictive controller (GPC) is examined and compared with the linear quadratic (LQ) optimal control formulation used in preview control. A new performance index and consequently a new algorithm is proposed dynamic performance predictive controller (DPPC) that combines the features of both GPC and preview controller. This algorithm minimizes the performance index through a dynamic optimization. A simple example illustrates the features of the three algorithms and prompts a discussion on what is actually minimized in predictive control. The DPPC algorithm, derived in this paper, provides for a minimum of the predictive performance index. The differences and similarities between the preview control and the predictive control have been discussed and optimization approach of predictive control has been explained.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Mean values of the output signals for the three controllers

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Figure 2

Mean values of the input signals for the three controllers

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Figure 3

Variances of the output signals for the three controllers

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Figure 4

Variances of the input signals for the three controllers

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Figure 5

Value of the LQG performance index for the three controllers

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Figure 6

Weighted sum of variances over the respective horizons for the three controllers

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Figure 7

Weighted sum of variances for the settings: N1=1, N2=11, Nu=0, λu=0.01, λe=1.0

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Figure 8

Schematic diagram of the closed-loop system with a predictive controller, indicating input and output signals

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Figure 9

Predictive performance index for the three controllers

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