Synthesizing Fuzzy Based Model Predictive Controller

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Synthesizing Fuzzy Based Model Predictive Controller

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Ebrahim A. Mattar ;

Khaled H. Al Mutib

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Abstract: The article presents a Fuzzy structure for a Model Predictive Control (MPC) system. MPC theorem has earlier been incorporated with fuzzy models. Such an integration provides controller design methods for an MPC control system. The paper concentrates on aspects of fuzzy based MPC for multivariable systems. Mathematical formulation of linearized MPC is utilized to introduce the concept of fuzzy based MPC scheme, then fuzzy MPC is constructed based on a modeled pH reactor. Results have shown that although the plant was nonlinear in characteristics, but still the employed Neuro-fuzzy system was able to model the plant into three different linear regions. Published in: Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on Date of Conference: 20-22 Sept. 2011

INSPEC Accession Number: 12360617

Date Added to IEEE Xplore: 15 November 2011

DOI: 10.1109/CIMSim.2011.29

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Publisher: IEEE Conference Location: Langkawi, Malaysia

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I. Introduction Traditionally, fuzzy controllers have been designed without an explicit model of the process being controlled. However, in fuzzy systems, mathematical models are explicitly used [1]. It has been found, that predictive control principle has recently been incorporated with fuzzy models. This provides design methods for fuzzy model based controllers, since predictive methods have several advantages that make them good candidates for industrial applications. An important requirement for any system identification technique is the ability to exploit available priori knowledge. Many conventional approaches rely on depth physical knowledge describing the system. However, for complex ill-defined systems such knowledge is unavailable or limited. In these instances, an expert can often describe the behavior of the system using natural language. Since Zadeh's article [2], fuzzy algorithms behavior has been used to build models based on such humanistic descriptions. Despite an apparent success of fuzzy systems, there are many aspects of their behavior which are unsuited to system identification and modeling. The major criticism is that these models are mathematically opaque, and there is no formal mathematical representation of the system's behavior. In addition, due to the vagueness and subjectivity of natural language statements, fuzzy systems based on qualitative knowledge alone are unlikely to adequately model simple system. To circumvent these inadequacies, as in conventional empirical modeling, available data should be used to adjust and validate the model's behavior. Efforts to combine both empirical and qualitative modeling have lead to the development of fuzzy modeling techniques. Such techniques allow both linguistic system description and empirical data to be fully utilized during system identification cycle. Read document

6/1/17, 2:59 PM

Synthesizing Fuzzy Based Model Predictive Controller - IEEE Xplore...

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http://ieeexplore.ieee.org/document/6076342/

Keywords IEEE Keywords Mathematical model, Computational modeling, Predictive models, Fuzzy systems, Adaptation models, Predictive control, Inductors INSPEC: Controlled Indexing predictive control, control system synthesis, fuzzy control, multivariable control systems, nonlinear control systems INSPEC: Non-Controlled Indexing neuro-fuzzy system, fuzzy controller, model predictive controller, multivariable system, linearized MPC system, pH reactor

Authors Ebrahim A. Mattar Dept. of Electr. Electron. Eng., Univ. of Bahrain, Sakhir, Bahrain Khaled H. Al Mutib Dept. of Comput. Sci., King Saud Univ., Riyadh, Saudi Arabia

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Synthesizing Fuzzy Based Model Predictive Controller - IEEE Xplore...

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http://ieeexplore.ieee.org/document/6076342/

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