Paper Info
Title
Adaptive fuzzy identification and predictive control for industrial processes
Abstract
This paper proposes a method for adaptive identification and control for industrial applications. The learning of a T-S fuzzy model is performed from input/output data to approximate unknown nonlinear processes by a hierarchical genetic algorithm (HGA). The HGA approach is composed by five hierarchical levels where the following parameters of the T-S fuzzy system are learned: input variables and their respective time delays, antecedent fuzzy sets, consequent parameters, and fuzzy rules. In order to reduce the computational cost and increase the algorithm's performance an initialization method is applied on HGA. To deal with nonlinear plants and time-varying processes, the T-S fuzzy model is adapted online to maintain the quality of the identification/control. The identification methodology is proposed for two application problems: (1) the design of data-driven soft sensors, and (2) the learning of a model for the Generalized predictive control (GPC) algorithm. The integration of the proposed adaptive identification method with the GPC results in an effective adaptive predictive fuzzy control methodology. To validate and demonstrate the performance and effectiveness of the proposed methodologies, they are applied on identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment system; and on control of a simulated continuous stirred tank reactor (CSTR) and on a real experimental setup composed of two coupled DC motors. The results are presented, showing that the developed evolving T-S fuzzy model can identify the nonlinear systems satisfactorily and it can be used successfully as a prediction model of the process for the GPC controller.
Year
DOI
Venue
2013
10.1016/j.eswa.2013.06.057
Expert Syst. Appl.
Keywords
Field
DocType
proposed adaptive identification method,adaptive fuzzy identification,identification methodology,adaptive predictive fuzzy control,antecedent fuzzy set,generalized predictive control,t-s fuzzy system,adaptive identification,fuzzy rule,prediction model,t-s fuzzy model,industrial process
Control theory,Neuro-fuzzy,Control theory,Computer science,Fuzzy logic,Model predictive control,Fuzzy set,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Initialization,Machine learning
Journal
Volume
Issue
ISSN
40
17
0957-4174
Citations 
PageRank 
References 
14
0.98
11
Authors
3
Name
Order
Citations
PageRank
Jérôme Mendes1446.53
Rui Araújo216418.93
Francisco Souza3567.08