2003Unpublished venueRequires access

Fuzzy PID control of nonlinear plants

Michail Petrov, Иван Ганчев, Albena Taneva

Open publisher page 102 citations

Abstract

It is difficult to achieve efficient control of time variable and nonlinear plants with conventional PID controllers. A method of designing a nonlinear fuzzy PID controller is presented. The nonlinear fuzzy PID controller could be applied successfully in control systems with various nonlinearities. The fuzzy PID controller can be viewed as a natural similarity to the conventional PID controller. This paper describes the structure and the design aspects of a fuzzy PID controller based on Sugeono's fuzzy technique with fuzzy-neural implementation. There are two possibilities to obtain a three-term fuzzy PID controller similar to the conventional digital PID controller. The first one is a velocity type fuzzy PID controller and the second one is the positioning type fuzzy PID controller. The antecedent part of the applied fuzzy rules contains a linear function, similar to the discrete equation of the corresponding conventional PID controller. The paper contains the structure and description of this implementation as well as investigations toward the applied fuzzy PID control algorithms. The simulations demonstrate satisfactory results of these performances and implementations applied to a nonlinear plant composed by two cascaded water tanks with level control.

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What this paper is about

It is difficult to achieve efficient control of time variable and nonlinear plants with conventional PID controllers. A method of designing a nonlinear fuzzy PID controller is presented. The nonlinear fuzzy PID controller could be applied successfully in control systems with various nonlinearities. The fuzzy PID controller can be viewed as a natural similarity to the conventional PID controller. This paper describes the structure and the design aspects of a fuzzy PID controller based on Sugeono's fuzzy technique with fuzzy-neural implementation. There are two possibilities to obtain a three-term fuzzy PID controller similar to the conventional digital PID controller. The first one is a velocity type fuzzy PID controller and the second one is the positioning type fuzzy PID controller. The antecedent part of the applied fuzzy rules contains a linear function, similar to the discrete equation of the corresponding conventional PID controller. The paper contains the structure and description of this implementation as well as investigations toward the applied fuzzy PID control algorithms. The simulations demonstrate satisfactory results of these performances and implementations applied to a nonlinear plant composed by two cascaded water tanks with level control.

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Available abstract

It is difficult to achieve efficient control of time variable and nonlinear plants with conventional PID controllers. A method of designing a nonlinear fuzzy PID controller is presented. The nonlinear fuzzy PID controller could be applied successfully in control systems with various nonlinearities. The fuzzy PID controller can be viewed as a natural similarity to the conventional PID controller. This paper describes the structure and the design aspects of a fuzzy PID controller based on Sugeono's fuzzy technique with fuzzy-neural implementation. There are two possibilities to obtain a three-term fuzzy PID controller similar to the conventional digital PID controller. The first one is a velocity type fuzzy PID controller and the second one is the positioning type fuzzy PID controller. The antecedent part of the applied fuzzy rules contains a linear function, similar to the discrete equation of the corresponding conventional PID controller. The paper contains the structure and description of this implementation as well as investigations toward the applied fuzzy PID control algorithms. The simulations demonstrate satisfactory results of these performances and implementations applied to a nonlinear plant composed by two cascaded water tanks with level control.

Key concepts: PID controller, Computer science, Nonlinear system, Control theory (sociology), Control engineering, Fuzzy control system, Control (management), Fuzzy logic

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