A new approach to fuzzy control using the distending function. (February 2020)

• Record Type: Journal Article
• Authors: Dombi, József; Hussain, Abrar
• Is Part Of: Journal of process control. Volume 86(2020)
• Page Start: 16
• Page End: 29
• Publication Date: 2020-02
• Subjects: Fuzzy arithmetic -- Adaptive fuzzy control -- Distending function -- Membership function -- Continuously stirred tank reactor; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.12.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A novel technique for the design of a fuzzy controller has been proposed. It is simple, fast and computationally efficient, compared to the classical techniques (Mamdani, Takagi Sugeno). Using the proposed approach, an algorithm for the design of an adaptive fuzzy controller is outlined. A new type of parametric membership function called Distending Function is introduced. It is analytical and covers most of the input space. The design process is simplified by handling the antecedent and consequent parts of the fuzzy rule separately. A general parametric fuzzy operator is used to calculate the firing strengths of the rules. The proposed approach does not include implication. Aggregation is performed using fuzzy arithmetic operations. Defuzzification is a single step calculation. So it is computationally efficient. The effectiveness of the proposed methodology is demonstrated on two industrial processes (a water tank system and continuously stirred tank reactor system). Abstract: Here, we develop a fuzzy controller using fuzzy arithmetics and a new type of membership function. The proposed new fuzzy control technique is simple, fast and computationally efficient, compared to the classical techniques (Mamdani, Takagi Sugeno) and it can also adapt to the process dynamics. The unique features are: 1) A new class of parametric membership function called the Distending Function (DF) is introduced; 2) A general parametric operator system is used. It utilizes most of the fuzzy operator systems for evaluating the knowledge base; 3) Inference is based on fuzzy arithmetic operations; 4) This leads to a computationally efficient single-step defuzzification. With these concepts, the paradigm of fuzzy control design changes radically. Using this technique with an optimization method, an adaptive fuzzy controller is designed. This adaptive controller adjusts to the changing dynamics of the non-linear processes by tuning our new type of membership function. The effectiveness of the proposed methodology is demonstrated on two industrial processes (a water tank system and continuously stirred tank reactor system).