Research Papers

Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

[+] Author and Article Information
Lei Pan1

Jiong Shen

School of Energy and Environment,  Southeast University, Nanjing 210096, China

Peter B. Luh

Department of Electrical and Computer Engineering,  University of Connecticut, Storrs, CT 06269


Corresponding author.

J. Dyn. Sys., Meas., Control 134(4), 041008 (Apr 27, 2012) (9 pages) doi:10.1115/1.4006085 History: Received May 15, 2011; Revised December 29, 2011; Published April 26, 2012; Online April 27, 2012

An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.

Copyright © 2012 by American Society of Mechanical Engineers
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Figure 1

PUUH with control system

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Figure 2

A DLD optimal control system with OSMM-GPC algorithm

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Figure 3

The inputs of the water levels

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Figure 4

Online estimated outputs of the water levels: (a) global optimal estimation, (b) local optimal estimation, and (c) mixed optimal estimation

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Figure 5

Pressure control performance comparison between the OSMM-GPC and the standard GPC while tracking DLD



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