Optimal control of a CSTR process

• Autor(a) principal: Soukkou,A.
• Data de Publicação: 2008
• Outros Autores: Khellaf,A., Leulmi,S., Boudeghdegh,K.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Chemical Engineering
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-66322008000400017
• topic: Intelligent control; Genetic learning; PPDC; Reduced rule base

Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC). The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO). Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.