Comparison of Fuzzy Control Rules Using MATLAB Toolbox and Simulink for DC Induction Motor-Speed Control

Aras, Mohd Shahrieel Mohd; Salim, Syed Najib Bin Syed; Hoo, Eric Chee Sai; Razak, Intan Azmira binti Wan Abd; Hairi, Mohd Hendra

doi:10.1109/socpar.2009.143

Cited by 21 publications

(6 citation statements)

References 3 publications

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“…Through the product investigation, it empowers to do the capacity of swaying and show the info and yield reaction. From the framework, it likewise can confirm the boundary of the model which can be gotten from the scientific displaying [3][4][5][6][7]. This yield reaction from the displaying capacity can be taken as the seat imprint to accomplish great reaction after executed it in the CTS.…”

Section: Introductionmentioning

confidence: 66%

Development of PID Controller Tuned by using Ziegler Nichols Method for Controlling the Fluid Level in Coupled Tank System.

2023

International Conference on Innovative Academic Studies

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Coupled Tank system is largely used nowadays in chemical process industries. Tanks are used to transfer the fluids from one tank to the other. In this process, flow of fluids between tanks should be monitored because of the perfect mixing of chemicals in the tanks and to reach the optimal mixing of fluids. In this research paper, PID controller is used to control the fluid level between two tanks. Tuning constants of PID is tunned by using Ziegler Nichols method. All the work is done in Matlab environment. After applying the strategy, performance characteristics i.e., Settling Time (Ts), Rise Time (Tr), Overshoot (O.S) and Steady State Error (S.S.E) was checked and hence concludes that results of CTS system with ZN-PID controller was good and can be used in chemical Process industry.

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Section: Introductionmentioning

confidence: 66%

Development of PID Controller Tuned by using Ziegler Nichols Method for Controlling the Fluid Level in Coupled Tank System.

2023

International Conference on Innovative Academic Studies

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“…This method turns controller inputs into information that can be easily used by the inference mechanism for activating and implementing rules. By this phase, the values of crisp inputs are fully changed to the values of fuzzy inputs [31]. The input for this fuzzy logic controller is voltage.…”

Section: Methodsmentioning

confidence: 99%

Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control

Rahman

Yahya

Sabari

2023

IJRA

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Direct current (DC) motor speed control is useful. Speed can be modified based on needs and operations. DC motors cannot control their speed. To control the DC motor’s speed, a dependable controller is needed. The DC motor speed will be controlled by a fuzzy logic proportional integral derivative controller (FLC-PID). The DC motor circuit’s electrical and mechanical components have been modeled mathematically. Ziegler-Nichols is used to tune the PID controller’s gain parameters. The FLC controller employs 3×3 membership function rules in conjunction with the MATLAB/Fuzzy Simulink toolbox. Real hardware was attached to the simulation to evaluate the DC motor speed control using the fuzzy logic PID controller. DC motors with FLC PID controllers, FLC controllers, and DC motors alone will be compared for the transient response. The DC motor with an FLC PID controller performed better in this study.

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“…The fuzzification change the raw data into membership functioned, knowledge based create rules for decision making, inference engine which act as intelligent system and defuzzification change the fuzzy decision data into real output raw data. Figure 6 shows the basic configuration of FLC component in a block diagram [39].…”

Section: Flc Controllermentioning

confidence: 99%

New lambda tuning approach of single input fuzzy logic using gradient descent algorithm and particle swarm optimization

Zohedi

Aras

Kasdirin

et al. 2022

IJEECS

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<span>Underwater remotely operated vehicle (ROV) is important in underwater industries as well as for safety purposes. It can dive deeper than humans and can replace humans in a hazardous underwater environment. ROV depth control is difficult due to the hydrodynamic of the ROV itself and the underwater environment. Overshoot in the depth control may cause damage to the ROV and its investigated location. This paper presenting a new tuning approach of single input fuzzy logic controller (SIFLC) with gradient descent algorithm (GDA) and particle swarm optimization (PSO) implementation for ROV depth control. The ROV was modeled using system identification to simulate the depth system. Proportional integral derivative (PID) controller was applied to the model as a basic controller. SIFLC was then implemented in three tuning approaches which are heuristic, GDA, and PSO. The output transient was simulated using MATLAB Simulink and the percent overshoot (OS), time rise (Tr), and settling time (Ts) of the systems without and with controllers were compared and analyzed. The result shows that SIFLC GDA output has the best transient result at 0.1021% (OS), 0.7992s (Tr), and 0.9790s (Ts).</span>

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Comparison of Fuzzy Control Rules Using MATLAB Toolbox and Simulink for DC Induction Motor-Speed Control

Cited by 21 publications

References 3 publications

Development of PID Controller Tuned by using Ziegler Nichols Method for Controlling the Fluid Level in Coupled Tank System.

Development of PID Controller Tuned by using Ziegler Nichols Method for Controlling the Fluid Level in Coupled Tank System.

Design of a fuzzy logic proportional integral derivative controller of direct current motor speed control

New lambda tuning approach of single input fuzzy logic using gradient descent algorithm and particle swarm optimization

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