scholarly journals Hybrid IWOPSO optimization based marine engine rotational speed control automatic system

2020 ◽  
Vol 10 (1) ◽  
pp. 840
Author(s):  
Ahmed Samawi Ghthwan ◽  
Abdulrasul A. Al-hayder ◽  
Ali Shaban Hassooni
Keyword(s):  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Automatic System ◽  
Environmental Changes ◽  
Step Response ◽  
Marine Transport ◽  
Logic Control ◽  
Optimum Selection ◽  
Rotational Speed Control ◽  
Selection Of

Transporting industry is having an important influence on the nations’ progress. The states that having long shoreline are taking advantage of their locations in using sea transportations which is more economical than other types of transportations. One of the most economical marine transport is the diesel fueling engines. This paper is to optimize the PID controller to control the speed of the engine overcoming the navigation environmental changes such as waves, winds and other effective external factors as well as the vessel internal changes such as the shipment load, equipment’s conditions …etc. PID is optimized through the optimum selection of its parameters (KP, KI and KD). A Simulink/MatLab model of the system is designed for this purpose. The Hybrid IWOPSO (HIWOPSO) algorithm is used for finding the optimum values of the PID parameters. The engine step response with these parameters is compared to the responses with those obtained by the IWO and PSO besides the Fuzzy Logic Control (FLC).

scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

Implementing Fuzzy Logic in the Control of a Biologically Inspired Robotic Cat Leg

2008 ◽  
Author(s):  
Anthony L. Crawford ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Vertical Displacement ◽  
Skeletal Structure ◽  
Governing Equations ◽  
Biologically Inspired ◽  
Logic Control ◽  
Forcing Function ◽  
Force Range

This research discusses the implementation of a fuzzy logic control system to drive the movement of a simplified cat leg model. The system’s movement in this paper addresses a planar motion where the model experiences a fixed horizontal velocity and a harmonic vertical displacement. The fuzzy logic (FL) controller applies membership functions to fuzzify the position and velocity errors and applies height defuzzification to generate the time dependant forcing function for the system’s horizontal and vertical governing equations. A PID controller is also applied as a benchmark for this research. Both controllers are optimized using the simplex method for which the FL controller performed just as well as the PID controller with more promise of accounting for the nonlinear influences that were neglected in this simplified cat leg model and requiring actuators with a lower required force range. This research provides the skeletal structure for which an effective total controller can be built on.

Intelligent Control for Visual Servoing System

2017 ◽  
Vol 6 (1) ◽  
pp. 72
Author(s):  
Dwi Pebrianti ◽  
Ong Ying Peh ◽  
Rosdiyana Samad ◽  
Mahfuzah Mustafa ◽  
N. R.H Abdullah ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Visual Servoing ◽  
Settling Time ◽  
Experimental Result ◽  
Threshold Method ◽  
Logic Control ◽  
Different Types ◽  
Tracking And Recognition

<p>This paper presents intelligent control for visual servoing system. The proposed system consists of a camera placed on a Pan Tilt Unit (PTU) which consists of two different servo motors. Camera and PTU are connected to a personal computer for the image processing and controlling purpose. Color threshold method is used for object tracking and recognition. Two different control methods, PID and Fuzzy Logic Control (FLC) are designed and the performances are compared through simulation. From the simulation result, the settling time of PID controller is 40 times faster than FLC. Additionally, the rise time of PID is about 20 times faster than FLC. However, the overshoot percentage of PID controller is 4 times higher than FLC. High overshoot value is not preferable in a control system, since it will cause the damage to the system. Real implementation of FLC on a home-built visual servoing system is conducted. Two different types of FLC, 9 and 11 rules of FLC are designed and implemented on the system. The experimental result shows that FLC with different total number of rules give different system performance. The settling time of FLC with 11 rules is 2 times faster than FLC with 9 rules. Additionally, the overshoot percentage of FLC with 11 rules is 2 times lower than FLC with 9 rules.</p>

Interval Type-2 Fuzzy Logic Control of Maglev Test Stand

2015 ◽  
Vol 759 ◽  
pp. 71-76
Author(s):  
Ireneusz Dominik
Keyword(s):  
Magnetic Field ◽  
Fuzzy Logic ◽  
Computational Complexity ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Test Stand ◽  
Logic Control ◽  
Interval Type

The paper contains a description of a research into applying classic algorithm of PID controller as well as advanced Type-2 Fuzzy logic controller to ensure stability of the levitating object in magnetic field. The implemented algorithm can handle uncertainties without increasing drastically the computational complexity, which is crucial in case of PLCs. The issues concerning the construction of the unit, where the experiments were carried out, are presented, as well as the characteristics of the object for different controllers.

Fuzzy Logic Controller Sebagai Pengatur Kecepatan Motor Shunt

2017 ◽  
Vol 8 (1) ◽  
pp. 11-16
Author(s):  
Machrus Ali ◽  
Budiman ◽  
Yanuangga Gala Hartlambang ◽  
4 Dwi Ajiatmo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Logic Control

Telah banyak penelitian pada motor shunt, karena kumparan penguat medan diparalel terhadap kumparan armatur. Motor DC shunt tidak terlalu membutuhkan banyak ruangan karena diameter kawat kecil, tetapi daya keluaran yang dihasilkan kecil karena arus penguatnya kecil. Metode Fuzzy Logic Control (FLC) telah banyak digunakan untuk optimasi suatu system. Penelitian ini membandingkan antara desain tanpa controller, dengan PID controller, dan FLC controller. Dari ketiga desain, menunjukkan bahwa desain control Fuzzy Logic Controller terbaik dari ketiga desain dengan besar putaran 300.0 rpm dengan settling time 1.702 detik dan besar Arus Rotor Motor Shunt (A) sebesar 1.9598 A, dengan setling time 1.323 detik. Penelitian ini akan dikembangkan menggunakan metode kecerdasan buatan lainnya

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