T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm
by Zhongda Tian; Shujiang Li; Yanhong Wang
International Journal of Modelling, Identification and Control (IJMIC), Vol. 25, No. 4, 2016

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.

Online publication date: Wed, 01-Jun-2016

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