Abstract
Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of the fuzzy logic controls are still implemented on expensive high-performance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low-cost controller applied to a water level control system. The paper also gives a low-cost hardware solution and practical procedure for system identification and control. First, the mathematical model of the process was obtained with the help of Matlab. Then two methods were used to control the system, PI (Proportional, Integral) and fuzzy control. Simulation and experimental results are presented.
Highlights
The extraordinary development of digital processors (Microprocessors, Microcontrollers) and their wide use in control systems in all fields have led to significant changes in the design of control systems
The best-known industrial process controller is the Proportional-Integrate-Derivative controller (PID) controller because of its simplicity, robustness, high reliability and it can be implemented on any processor, but using a PID controller is not fully convenient when it comes to dealing nonlinear systems [3, 4]
The system consists of a water tank, a liquid level sensor, a pump based on a 12V direct current motor, an electronic circuit (Arduino and a DC/DC step-down converter)
Summary
The extraordinary development of digital processors (Microprocessors, Microcontrollers) and their wide use in control systems in all fields have led to significant changes in the design of control systems. The best-known industrial process controller is the PID controller because of its simplicity, robustness, high reliability and it can be implemented on any processor, but using a PID controller is not fully convenient when it comes to dealing nonlinear systems [3, 4]. These systems can be successfully controlled using fuzzy logic controllers because of their independency from the mathematical model of the system.
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