A New Approach of Takagi–Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace

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• 作者：Ridong Zhang ; Jili Tao ; Furong Gao
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2016
• 出版时间：June 8, 2016
• 年：2016
• 卷：55
• 期：22
• 页码：6465-6474
• 全文大小：515K
• 年卷期：0
• ISSN：1520-5045

文摘

The oxygen content modeling of the coke furnace is important for advanced control design but not an easy job because of various disturbances and nonlinearity. A novel approach is proposed by using an improved genetic algorithm (IGA) combined with the dynamic autoregressive with exogenous input (ARX) Takagi–Sugeno (T-S) fuzzy model. The IGA algorithm automatically generates the input variable, the appropriate fuzzy if–then rules, and the ARX structure to characterize the dynamic nonlinear feature of the oxygen content by processing the operation data from the industrial coke furnace. And a more comprehensive objective function is constructed considering both the modeling precision and structure simplicity. Hybrid encoding, modified genetic operators, particularly the maintain operator, are designed to obtain the satisfactory optimization performance. The modeling accuracy and system structure of the T-S fuzzy model are compared with a benchmark Box–Jenkins gas furnace and the complex industrial coke furnace. The results show good modeling accuracy and simple structure of the T-S fuzzy model.