scholarly journals PID and Fuzzy Logic Controller Design for Balancing Robot Stabilization

2018 ◽  
pp. 1-10
Keyword(s):  
Fuzzy Logic ◽  
Tilt Angle ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Controller Design ◽  
Point Of View ◽  
Relative Advantage ◽  
Balancing Robot ◽  
Time Required

This paper addresses the problem of position control and stabilization for the two wheeled balancing robot. A mathematical model is derived based on the robot’s position and tilt angle and a fuzzy logic control is proposed for the balancing robot control. The fuzzy logic controller performance is compared with a conventional PID controller to show the difference between them. Both controllers were tested on the balancing robot in simulation using MATLAB software and the results were put together for a comparative point of view. The simulations shows a relative advantage for the fuzzy logic controller over the conventional PID controller especially in reducing the time required for stabilization which takes about 2 seconds and almost without overshoot while in the PID case the robot will have about 10% overshoot in position and about 20 degrees in tilt angle.

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.

scholarly journals Modelling and Simulation of Stepper Motor For Position Control Using LabVIEW

1970 ◽  
Vol 5 (1.) ◽  
Author(s):  
Ahmet Mehmet Karadeniz ◽  
Malek Alkayyali ◽  
Péter Tamás Szemes
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Bond Graph ◽  
Industrial Applications ◽  
Modelling And Simulation ◽  
Stepper Motor ◽  
Stepping Motor ◽  
Better Than

This paper presents hybrid stepper motor (is a type of stepping motor) modelling and simulation which is widely used a kind of motor in industrial applications. In this study, the stepper motor was modelled using bond graph technique and simulation for desired position was executed on LabVIEWgraphical interface. Then, firstly a convenient PID controller was designed for position, speed and current and PID close loopresponse was obtained for position control. Then, PID parameters for each controller were arranged separately to obtain good response Secondly, Fuzzy Logic controller applied to the system and its response was obtained. Finally, both responses are compared. According to comparison, it was observed that Fuzzy Logic controller’s response is better than PID’s. (In this paper, all shown responses were observed for 120 degree desired position)

Development of Servo Drive Positioning Control System Using PID Controller and Fuzzy Logic Controller

2021 ◽  
Vol 26 (6) ◽  
pp. 583-588
Author(s):  
Zaw Myo Naing ◽  
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Settling Time ◽  
Fuzzy Pid ◽  
Servo Drive ◽  
Positioning Control ◽  
Fuzzy Pid Controller

Servo drives are one of the most widely utilized devices in various mechanical systems and industrial applications to provide precise position control. The study of servo driver produc-tiveness and performance index is the important task. In this work, PID controller and fuzzy log-ic controller (FLC) were developed to control the position of a DC servo drive. The MATLAB Simulink program was investigated and implemented to calculate the values of servo drive pa-rameters, and a scheme for simulating the operation of a servo drive using different controllers was presented. A mathematical model of a DC servo drive for a positioning control system has been proposed. The control characteristics of the PID controller, fuzzy logic controller and fuzzy PID controller are compared. The simulation results have shown that the PID controller allows for an overshoot of about 1 % with a settling time of about 4 sec. The use of the fuzzy PID con-troller reduces the maximum overshoot to 1 % and decreases the settling time to 2 sec. As a re-sult, the fuzzy PID controller allows for better performance and efficiency compared to other controllers.

scholarly journals Fuzzy logic controller design for PUMA 560 robot manipulator

2020 ◽  
Vol 9 (2) ◽  
pp. 73
Author(s):  
Abdel-Azim S. Abdel-Salam ◽  
Ibrahim N. Jleta
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Position Control ◽  
Controller Design ◽  
Robot Manipulator ◽  
Tracking Error ◽  
Pid Controllers ◽  
Fuzzy Logic Controllers ◽  
Better Than ◽  
Computed Torque

The dynamic model of the robot manipulator contain from equations, these equations are nonlinear and contained from variations parameters due to variations in load, friction, and disturbance. The conventional computed torque (PD and PID) controllers are not highly suitable for nonlinear, complex, time-variant systems with delay. In this paper, the fuzzy logic controllers (FLC) has been used because it is efficient tools for control of nonlinear and uncertain parameters systems. This paper aims to design a fuzzy logic controller for position control of a PUMA 560 robot manipulator. Based on simulation results we conclude that the performance of the fuzzy logic controller in term of position tracking error in case of disturbance or load is better than the conventional computed torque (PD-CTC and PID-CTC) controllers.

scholarly journals Implementation of a Fuzzy Logic Controller for an Aerothermic Process

2020 ◽  
Vol 9 (3) ◽  
pp. 3584-3588
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Control Methods ◽  
Industrial Systems ◽  
Control Techniques ◽  
Logic Control ◽  
Linear Behaviour ◽  
Time Required

The objective of any controller design is to maintain the set point value despite its variation, with a good rejection of various disturbances that can infect the system to be controlled and to minimize the energy consumption. To achieve these objectives, several control methods are proposed in the literature. Nevertheless, due to the non-linear behaviour of most industrial systems, fuzzy logic control remains the most appropriate method to control this type of system. This article compares two fuzzy logic control techniques having two inputs and one output. These methods are applied to an aerothermic process in our laboratory. The obtained experimental results allowed us to achieve the main control objectives, such asset-point trackingand regulation. These results encouraged us to exploit the advantage of each technique, in order to make the controller design simpler and to minimize the time required to calculate the command applied to the process.

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