Fuzzy Logic and Fuzzy Control (original) (raw)
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Comparison of Fuzzy Control and PID Control
International Journal of Scientific Research in Science, Engineering and Technology, 2019
As it knows most of the quotidian types of control, problems are not simply to evaluate and considerate formal modelling based in traditional techniques. The process control makes the evaluation and executions more efficient in the industry. This article was made with the purpose to compare two types of control, one with FUZZY logic and second one PID control. Here we have developed temperature control system using fuzzy logic. The flyback converter with voltage doubler rectifier acts as an output module. To overcome the efficiency degradation during lightload due to load dependent soft switching of the ZVS, a control method using pulse width modulation (PWM)proportional to the load current is us Comparison between Fuzzy logic controller & PID controller based on pulse width modulation is proposed and the results are analysed. Thus, comparing the result PID controller gives more accurate results than first Fuzzy logic controller.
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Fuzzy Logic Temperature Control: A feedback control system implemented by fuzzy logic
World Academy of Research in Science and Engineering , 2020
This paper is meant to highlight and discuss the facts and uses of fuzzy logic in terms of a temperature controller. The purpose of this temperature control system is to heat a room to a specified temperature then tries to maintain it at that temperature in a controlled manner. This paper will highlight different possible concerns done in the system and possible solutions to this fuzzy logic pattern. The applications are far-reaching as the temperature control can be depicted and used in multiple control systems such as control temperature in air conditioning units, generators, temperature for aquariums, etc. All these applications with fuzzy logic can help dictate the amount of energy used constantly to save power while achieving the needed amount. The fuzzy logic in temperature control systems can vary and the truth table for the logic system may vary widely as well. However, the main point of the system is to create and apply control correctly. By making use of built-in functions that can potentially correct errors and predict patterns in terms of date and time of the year, the time of the day and the flow of air, etc. It can effectively correct and make adjustments for the temperature correctness.
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IJERT-Realization of Fuzzy Logic in Temperature Control System over PID
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/realization-of-fuzzy-logic-in-temperature-control-system-over-pid https://www.ijert.org/research/realization-of-fuzzy-logic-in-temperature-control-system-over-pid-IJERTV3IS071275.pdf This paper proposed an artificial intelligent control method for temperature control system and is suitable for low temperature applications such as laboratory equipments (e.g. ovens and incubators).Since we had PID as existing system in temperature control equipment, we are going to the proposed design uses fuzzy logic as a control method that maintains the temperature of simulated heater to the desired point. In this Microcontroller based circuit is built to acquire data from sensor, actuate heat element and communicate with computer workstation. MATLAB fuzzy logic controller is designed, tested, and tuned to control the circuit. The Fuzzy Logic Controller(FLC) performance is evaluated in several situations by comparing it with conventional Proportional Integral Derivative (PID) controller in terms of speed of response to the desired setting value, overshoot in fixed set point and robustness against disturbance. FLC is fast in response to the setting with compare to PID, and more stable against external disturbance. Both of FLC and PID have neglected overshoot value and steady state error, but FLC has noticeable deviation in high set points Key terms-proportional integral-derivative (PID). Fuzzy, Control, Temperature and performance.
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Comparative analysis of PID and fuzzy logic controller: A case of furnace temperature control
2020
Furnace temperature controller has a large overshoot and constant oscillation error. To solve this problem there are several studies done on the PID type furnace temperature controller with different PID parameters, but this method is not efficient because of the nonlinearity of temperature. Due to this reason, the overshoot happens and steady-state errors are observed. Other researchers have shown that the inclusion of one more controller with a PID controller, such as a fuzzy logic controller can improve the results as compared to the use of the PID controller alone. The objective of this research is to experiment on the PID and fuzzy logic controller hardware and compare the results with those obtained from the simulation. In addition to this, the objective also is to find out the type of controller that would be most efficient in terms of settling time and the overshoot. This paper presents the comparison of PID and fuzzy logic controller simulation and experimentation on the ha...
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