contributor author | Lei Pan | |
contributor author | Jiong Shen | |
contributor author | Peter B. Luh | |
date accessioned | 2017-05-09T00:49:08Z | |
date available | 2017-05-09T00:49:08Z | |
date copyright | July, 2012 | |
date issued | 2012 | |
identifier issn | 0022-0434 | |
identifier other | JDSMAA-26589#041008_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/148471 | |
description abstract | 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header | |
type | Journal Paper | |
journal volume | 134 | |
journal issue | 4 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.4006085 | |
journal fristpage | 41008 | |
identifier eissn | 1528-9028 | |
keywords | Algorithms | |
keywords | Predictive control | |
keywords | Sampling (Acoustical engineering) | |
keywords | Boilers AND Control equipment | |
tree | Journal of Dynamic Systems, Measurement, and Control:;2012:;volume( 134 ):;issue: 004 | |
contenttype | Fulltext | |