Trajectory Tracking of a Quadcopter Using Fuzzy Logic and Neural Network Controllers

2018 ◽  
Author(s):  
Burak Celen ◽  
Yesim Oniz
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Neural Network Controllers

Electric Machines Excitation Control via Higher Order Neural Networks

2010 ◽  
pp. 366-396 ◽  
Author(s):  
Yannis L. Karnavas
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Real Time ◽  
Fuzzy Logic Controller ◽  
Synchronous Generator ◽  
Higher Order ◽  
Excitation Control ◽  
Time Operation ◽  
Real Time Operation ◽  
Neural Network Controllers

This chapter is demonstrating a practical design of an intelligent type of controller using higher order neural network (HONN) concepts, for the excitation control of a practical power generating system. This type of controller is suitable for real time operation, and aims to improve the dynamic characteristics of the generating unit by acting properly on its original excitation system. The modeling of the power system under study consists of a synchronous generator connected via a transformer and a transmission line to an infinite bus. For comparison purposes and also for producing useful data in order for the demonstrating neural network controllers to be trained, digital simulations of the above system are performed using fuzzy logic control (FLC) techniques, which are based on previous work. Then, two neural network controllers are designed and applied by adopting the HONN architectures. The first one utilizes a single pi-sigma neural network (PSNN) and the significant advantages over the standard multi layered perceptron (MLP) are discussed. Secondly, an enhanced controller is designed, leading to a ridge polynomial neural network (RPNN) by combining multiple PSNNs if needed. Both controllers used, can be pre-trained rapidly from the corresponding FLC output signal and act as model dynamics capturers. The dynamic performances of the fuzzy logic controller (FLC) along with those of the two demonstrated controllers are presented by comparison using the well known integral square error criterion (ISE). The latter controllers, show excellent convergence properties and accuracy for function approximation. Typical transient responses of the system are shown for comparison in order to demonstrate the effectiveness of the designed controllers. The computer simulation results obtained show clearly that the performance of the developed controllers offers competitive damping effects on the synchronous generator’s oscillations, with respect to the associated ones of the FLC, over a wider range of operating conditions, while their hardware implementation is apparently much easier and the computational time needed for real-time applications is drastically reduced.

Partitioned Neural Network Control and Hierarchical Fuzzy Logic Control for Space Flexible Manipulator With Unknown Parameters

2010 ◽  
Author(s):  
Dengfeng Huang ◽  
Li Chen
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Rigid Motion ◽  
Flexible Manipulator ◽  
Coordinated Motion ◽  
Fast Subsystem ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
Slow Subsystem

The trajectory tracking and vibration suppression control of free-floating space flexible manipulator with an attitude controlled base are discussed. With the law of conservation of momentum, the Lagrangian principle is utilized to model the dynamic function of the space flexible manipulator incorporating the assumed modes method. Using singular perturbation theory, a slow subsystem describing the rigid motion and a fast subsystem corresponding to the flexible motion are obtained. Then, a two-time scale controller for coordinated motion between the base’s attitude and the manipulator’s joints of space flexible manipulator system is designed. The slow-subsystem partitioned neural network controller dominates the trajectory tracking of coordinated motion in the presence of unknown parameters. The fast-subsystem controller damps out the vibration of the flexible link by hierarchical fuzzy logic controller. Numerical simulation results illustrate that the proposed controller is reliable and effective.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

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