Title: T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm

Authors: Zhongda Tian; Shujiang Li; Yanhong Wang

Addresses: College of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China ' College of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China ' College of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China

Abstract: How to control burning zone temperature of the lime rotary kiln is an important problem. In order to improve the control performance of burning zone temperature in lime rotary kiln, a predictive control method based on improved hierarchical genetic algorithm and T-S fuzzy neural network was proposed. This control method utilised T-S fuzzy neural network to build a nonlinear predictive model of burning zone temperature in rotary kiln. The predictive error is corrected through predictive output burning temperature, output feedback error and error correction. A fitness function is established by deviation and control variable. An improved hierarchical genetic algorithm was used for rolling optimisation of the optimal control variable. Simulation results show that the proposed predictive method has good control effect.

Keywords: lime rotary kilns; burning zone temperature; Takagi-Sugeno; T-S fuzzy modelling; neural networks; genetic algorithms; predictive control; nonlinear modelling; rolling optimisation; simulation.

DOI: 10.1504/IJMIC.2016.076825

International Journal of Modelling, Identification and Control, 2016 Vol.25 No.4, pp.323 - 334

Received: 01 Apr 2015
Accepted: 17 Jul 2015

Published online: 01 Jun 2016 *

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