scholarly journals Implementing Fuzzy Logic Controller and PID Controller to a DC Encoder Motor – “A case of an Automated Guided Vehicle”

2018 ◽  
Vol 20 ◽  
pp. 219-226 ◽  
Author(s):  
Priyam Parikh ◽  
Saurin Sheth ◽  
Rupesh Vasani ◽  
Jigar Kumar Gohil
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Automated Guided Vehicle

scholarly journals Speed Control for DC Encoder Motor and Path Optimization in an Automated Guided Vehicle

2020 ◽  
Vol 9 (1) ◽  
pp. 977-980
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Optimal Path ◽  
Constant Speed ◽  
Path Optimization ◽  
Destination Node ◽  
Automated Guided Vehicle ◽  
Speed Response

Automated guided vehicle （AGV） has variety of applications in the field of automotive and logistics. For stability of plant AGV should run at a constant speed. The AGV incorporates DC encoder motor which is controlled by Fuzzy controller and PID controller to acquire the constant speed. Response of the system with of fuzzy controller and PID controller is done. . The steady state error and overshoot of the system is reduced by the fuzzy logic controller. Dijkstra’s algorithm is applied to find an optimal path. This algorithm finds the shortest path between the source node and destination node.

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.

Application of Hybrid Fuzzy-PID Control with Coupled Rules in the Hydraulic Positioning System

2012 ◽  
Vol 220-223 ◽  
pp. 402-405
Author(s):  
Li Hong Dong
Keyword(s):  
Fuzzy Logic ◽  
Hydraulic System ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Fast Response ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Positioning Control ◽  
Fuzzy Pid Controller ◽  
Hybrid Fuzzy Pid

According to the nonlinearity and time-variation of the positioning control in hydraulic system, a kind of Hybrid Fuzzy-PID Controller with Coupled Rules (HFPIDCR) is proposed. In this control system, the bulk modulus is considered as a variable. The novelty of this controller is to combine the fuzzy logic and PID controllers in a switching condition. Simulation results of the HFPIDCR are compared with the results of traditional PID, Fuzzy Logic Controller (FLC), and Hybrid Fuzzy-PID Controller (HFPID). It is demonstrated that the HFPIDCR has fast response, short adjustment time, high control precision and other advantages, and it can meet the requirements of the positioning control in hydraulic system.

scholarly journals Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

2021 ◽  
Vol 3 (10) ◽  
Author(s):  
Esmael Adem Esleman ◽  
Gürol Önal ◽  
Mete Kalyoncu
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Optimization Method ◽  
Industrial Applications ◽  
Bees Algorithm ◽  
Membership Functions ◽  
Overhead Crane ◽  
Target Values

AbstractDifferent industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

scholarly journals Modelling and Controller Design for Non- Linear Two Tank Interacting System

2020 ◽  
Vol 9 (6) ◽  
pp. 82-85
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Mathematical Modelling ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Linear Process ◽  
Matlab Simulink ◽  
Non Linear ◽  
And Control

This paper explains the mathematical modelling and controller design of Two Tank Interacting System (TTIS) for a non-linear process. To design the non-linear process using Matlab Simulink and control the process using conventional PID controller and Fuzzy Logic Controller (FLC). A comparative study was conducted extensively made to examine which controller suits well for the non-linear process through the response observed.

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