Journal
ISA TRANSACTIONS
Volume 56, Issue -, Pages 241-251Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2014.11.018
Keywords
Superheater steam temperature control; Power plant; Subspace identification; Data-driven modeling; TS-fuzzy model; Stable model predictive control
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Funding
- National Natural Science Foundation of China (NSFC) [51036002, 51476027]
- Doctoral Fund of the Ministry of Education of China [20130092110061]
- Natural Science Foundation of Jiangsu Province, China [BK20141119]
- Cooperative Innovation Foundation of Jiangsu Province-Prospective Joint Research Project [BY2013073-07]
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This paper develops a stable fuzzy model predictive controller (SFMPC) to solve the superheater steam temperature (SST) control problem in a power plant. First, a data-driven Takagi-Sugeno (TS) fuzzy model is developed to approximate the behavior of the SST control system using the subspace identification (SID) method. Then, an SFMPC for output regulation is designed based on the TS-fuzzy model to regulate the SST while guaranteeing the input-to-state stability under the input constraints. The effect of modeling mismatches and unknown plant behavior variations are overcome by the use of a disturbance term and steady-state target calculator (SSTC). Simulation results for a 600 MW power plant show that an offset-free tracking of SST can be achieved over a wide range of load variation. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.
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