GA-Based Fuzzy Logic Control for a Smart Fin of a Projectile

2007 ◽  
Author(s):  
Venkat Mudupu ◽  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht
Keyword(s):  
Fuzzy Logic ◽  
System Identification ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Operating Conditions ◽  
Piezoelectric Bimorph ◽  
Logic Control ◽  
Identification Technique ◽  
Simulation Results ◽  
Design And Testing

This paper presents the design and testing of a smart fin for a subsonic projectile. The smart fin is activated using a piezoelectric bimorph with a substrate that is completely enclosed within the fin. A linear model of the actuator and fin system is created using the frequency response identification technique within MATLAB System Identification Toolbox. A procedure for designing a GA-based fuzzy logic controller for the fin is presented. Experimental and simulation results show that the proposed controller achieved the fin angle control under different operating conditions.

Design and Validation of Fuzzy Logic Control for a Smart Projectile Fin

2007 ◽  
Author(s):  
Venkat Mudupu ◽  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Experimental Testing ◽  
Operating Conditions ◽  
Chirp Signal ◽  
Piezoelectric Bimorph ◽  
Subsonic Wind Tunnel ◽  
Logic Control ◽  
The University

This paper deals with the design and validation of the fuzzy logic control for a smart fin of a projectile. The fin is actuated by a cantilevered piezoelectric bimorph that is completely enclosed within the fin. A linear model of the actuator and fin is identified experimentally by exciting the system using a chirp signal. A procedure for designing a GA-based fuzzy logic controller for the fin is presented. The controller is verified using simulation and experimental testing that is conducted in the subsonic wind tunnel at the University of Nevada, Las Vegas (UNLV). Results illustrate that the proposed controller can track the desired fin angle control under various operating conditions.

Adaptive Bandwidth PLL Design Based on Fuzzy Logic Control

2014 ◽  
Vol 543-547 ◽  
pp. 1393-1396 ◽  
Author(s):  
Lan Ying Zhang ◽  
Hai Yang Liu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Loop Filter ◽  
Logic Control ◽  
Loop Bandwidth ◽  
Simulation Results ◽  
Adaptive Bandwidth ◽  
Stability And Accuracy ◽  
Poor Stability

Based on fuzzy logic control adaptive bandwidth PLL design is presented for the problem of tracking poor stability and low accuracy when a certain type of radar tracking dynamic spacecraft. This method is mainly through fuzzy logic controller, adaptive level is determined by control rule of input respectively, and the outputs of rules are weighted combined to control the coefficient of loop filter, thus adjusting automatically the loop bandwidth, and enhancing the tracking stability of radar equipment and improving ranging accuracy. The simulation results show that the fuzzy logic control adaptive bandwidth PLL has higher tracking stability and accuracy.

Type-2 Fuzzy Logic Control of a Doubly-Fed Induction Machine (DFIM)

2015 ◽  
Vol 4 (4) ◽  
pp. 139 ◽  
Author(s):  
Loukal Keltoum ◽  
Benalia Leila
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Induction Machine ◽  
Operating Conditions ◽  
Logic Control ◽  
Doubly Fed Induction Machine ◽  
Doubly Fed ◽  
Interval Type

The fuzzy controllers have demonstrated their effectiveness in the control of nonlinear systems, and in many cases have established their robust and that their performance is less sensitive to parameter variations over conventional controllers. In this paper, Interval Type-2 Fuzzy Logic Controller (IT2FLC) method is proposed for controlling the speed with a direct stator flux orientation control of doubly-fed induction motor (DFIM), we made a comparison between the Type-1 Fuzzy Logic Control (T1FLC) and IT2FLC of the DFIM, first a modeling of DFIM is expressed in a (d-q) synchronous rotating frame. After the development and the synthesis of a stabilizing control laws design based on IT2FLC. We use this last approach to the control of the DFIM under different operating conditions such as load torque and in the presence of parameter variation. The obtained simulation results show the feasibility and the effectiveness of the suggested method.

Fuzzy Logic Control of Knee-FES-Ergometer for Stroke Patients Knee Swinging Exercise

2013 ◽  
Vol 431 ◽  
pp. 282-286 ◽  
Author(s):  
Rozan Boudville ◽  
Zakaria Hussain ◽  
Saiful Zaimy Yahaya
Keyword(s):  
Fuzzy Logic ◽  
Electrical Stimulation ◽  
Muscle Fatigue ◽  
Functional Electrical Stimulation ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Quadriceps Muscle ◽  
Stroke Patients ◽  
Logic Control ◽  
Simulation Results

This paper presents the development of a fuzzy logic controller for a knee-FES-ergometer for stroke patients knee swinging exercise. The knee-FES-ergometer is introduced as a hybrid exercise for a long repetitive knee swinging exercise for stroke patient through the application of a knee swinging ergometer and functional electrical stimulation. The specially designed ergometer is used to reduce the required electrical stimulation and hence avoids early muscle fatigue. A knee swinging ergometer model, humanoid model and a stimulated quadriceps muscle model are developed to simulate the FES-assisted knee swinging exercise. A fuzzy logic controller is then designed to control the repetitive knee swinging. Simulation results verifying the knee swinging trajectories and the reduced electrical stimulation required are presented and discussed.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Comparison of a Fuzzy Logic Controller With a P + D Control Law

1989 ◽  
Vol 111 (2) ◽  
pp. 128-137 ◽  
Author(s):  
S. Daley ◽  
K. F. Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Good Control ◽  
Control Law ◽  
Dynamic Coupling ◽  
Process Knowledge ◽  
Logic Control ◽  
Limited Process

A study is described that compares the performance of a self-organizing fuzzy logic control law (SOC) with that of the more traditional P + D algorithm. The multivariate problem used for the investigation is the attitude control of a flexible satellite that has significant dynamic coupling of the axes. It is demonstrated that the SOC can provide good control, requires limited process knowledge and compares favorably with the P + D algorithm.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

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.

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

Inverse Dynamics-Based Fuzzy Logic Control of a Projectile Smart Fin

2005 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Surya Kiran Parimi ◽  
Woosoon Yim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Attractive Alternative ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
Novel Approach ◽  
External Moment ◽  
Element Approach

A smart fin for a subsonic projectile should be able to produce maneuvering force and moment that can control its rotation during flight. Piezoelectric actuator is an attractive alternative to usual hydraulic actuators due to its simplicity. The cantilever-shaped actuator can also be fully enclosed within the hollow fin. It has an end fixed to the rotation axle of the fin while the other end is pinned at the tip of the fin. A dynamic model of the system, including external moment due to aerodynamic effects, is obtained using the finite element approach. This paper presents a novel approach for automatically creating fuzzy logic controllers for the fin. This approach uses the inverse dynamics of the smart fin system to determine the ranges of the variables of the controllers. Simulation results show that the proposed controller can successfully drive smart fin under various operating conditions.

Sign in / Sign up

Export Citation Format

Share Document