scholarly journals Flutter Suppression of an Airfoil Using Neuro-Fuzzy Control

2021 ◽  
Author(s):  
Chun Meng
Keyword(s):  
Fuzzy Controller ◽  
Equations Of Motion ◽  
Fuzzy Rules ◽  
Matlab Simulation ◽  
Modeling And Analysis ◽  
Flutter Suppression ◽  
Flutter Speed ◽  
Neuro Fuzzy ◽  
Excited Vibration ◽  
And Control

Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.

scholarly journals Flutter Suppression of an Airfoil Using Neuro-Fuzzy Control

2021 ◽  
Author(s):  
Chun Meng
Keyword(s):  
Fuzzy Controller ◽  
Equations Of Motion ◽  
Fuzzy Rules ◽  
Matlab Simulation ◽  
Modeling And Analysis ◽  
Flutter Suppression ◽  
Flutter Speed ◽  
Neuro Fuzzy ◽  
Excited Vibration ◽  
And Control

Flutter, a self-excited vibration of wings and control surfaces, can lead to catastrophic failure of aircraft structures. Classical methods have been applied successfully for flutter suppression and for increasing the flutter critical speed. With the demand of higher speed and more flexible aircraft, more advanced active flutter control techniques are required. In this study, a neuro-fuzzy methodology for flutter suppression of a two dimensional airfoil is explored. A MATLAB simulation environment is used for the modeling and analysis. The airfoil model is simulated according to a set of aeroelastic equations of motion. A neuro-fuzzy controller, called NEFCON, is then embedded in the airfoil model for increasing the flutter speed. NEFCON learns from the motion of the airfoil and automatically produces fuzzy rules. The simulation results show that these fuzzy rules can successfully increase the critical flutter speed. The performance of the fuzzy rules is tested with differential airfoil parameters.

Linguistic Fuzzy Logic Control of a Double Inverted Pendulum With Destabilizing Fractional Dampers

2016 ◽  
Author(s):  
Arman Dabiri ◽  
Morad Nazari ◽  
Eric A. Butcher
Keyword(s):  
Human Brain ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Equations Of Motion ◽  
A Priori ◽  
Fuzzy Rules ◽  
Brain Functions ◽  
Logic Control ◽  
Linguistic Rules ◽  
Two Degree Of Freedom

In this paper, a fuzzy controller is designed for a mechanical system with fractional damping without a priori knowledge of the system dynamics. Because of the constitutive equation of the damping, equations of motion of the system consist of fractional order terms. In the process of developing the fuzzy controller, the fuzzy rules are selected based on the human brain functions. The controller is first implemented for the case of a single inverted pendulum with destabilizing fractional dampings mounted on a cart, i.e. a two degree of freedom (DOF) system, where the functions of human brain in balancing a stick on a fingertip are used to train the fuzzy rules. Then, by extending the linguistic rules, the controller is applied to a double inverted pendulum with destabilizing fractional dampings mounted on a cart, i.e. a three DOF system. Since the linguistic rules are based on qualitative motion of the pendulums, the controller is capable bringing the system to rest at the unstable equilibrium point despite the fractional destabilizing damping in the system. Finally, the numerical results of the both examples are discussed.

Intelligent Target Visual Tracking and Control Strategy for Open Frame Underwater Vehicles

Robotica ◽  
2021 ◽  
pp. 1-15
Author(s):  
Chaoyu Sun ◽  
Zhaoliang Wan ◽  
Hai Huang ◽  
Guocheng Zhang ◽  
Xuan Bao ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Fitness Function ◽  
Underwater Vehicles ◽  
Fuzzy Rules ◽  
Correlation Filter ◽  
State Action ◽  
Training Samples ◽  
And Control

SUMMARY Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

scholarly journals Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

2022 ◽  
Vol 12 (2) ◽  
pp. 541
Author(s):  
Helbert Espitia ◽  
Iván Machón ◽  
Hilario López
Keyword(s):  
Control System ◽  
Design Process ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Fuzzy Controller ◽  
Initial Configuration ◽  
Control Action ◽  
Linear Controller ◽  
Neuro Fuzzy ◽  
And Control

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.

Dynamic Modeling and Analysis of a Circular Track-Guided Tripod

2009 ◽  
Vol 5 (1) ◽  
Author(s):  
Yuwen Li ◽  
Fengfeng Xi ◽  
Allan Daniel Finistauri ◽  
Kamran Behdinan
Keyword(s):  
Equations Of Motion ◽  
Influence Factors ◽  
Parallel Robot ◽  
Strong Impact ◽  
Simulation Studies ◽  
Modeling And Analysis ◽  
Aircraft Fuselage ◽  
Circular Track ◽  
Curved Track ◽  
And Control

To enlarge the workspace and improve the motion capability of a parallel robot, the base of the robot can be guided to move along a linear or curved track. This paper aims at analyzing how the motion of the base affects the dynamics of a parallel robot. For this purpose, kinematic and dynamic equations are developed for a circular track-guided tripod parallel robot. For kinematics, the motion of the base is incorporated into the analytical formulations of the position and velocity of the tripod. For dynamics, equations of motion are derived using the Lagrangian formulation, and influence factors are defined to provide a quantitative means to measure the effects of the velocity and acceleration of the base on the actuator forces of the tripod. As an application of the above method, a circular track-guided tripod is proposed for the automatic riveting in the assembly of an aircraft fuselage. Simulation studies are carried out to investigate the tripod dynamics. It is found that the motion of the base has a strong impact on the actuator forces. The dynamic model provides a useful tool for the design and control of the circular track-guided tripod.

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