scholarly journals Design of a Control System for an Organic Flight Array Based on a Neural Network Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bokyoung Oh ◽  
Junho Jeong ◽  
Jinyoung Suk ◽  
Seungkeun Kim
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flight Control ◽  
Attitude Control ◽  
Adaptive Controller ◽  
Model Inversion ◽  
Operational Conditions ◽  
Ducted Fan ◽  
Neural Network Controller ◽  
The Neural Network

This paper presents a flight control system for an organic flight array (OFA) with a new configuration consisting of multimodularized ducted-fan unmanned aerial vehicles. The OFA has a distinguished advantage of assembling or separating with respect to its missions or operational conditions because of its reconfigurable structure. Therefore, designing a controller that can be flexibly applied in each situation is necessary. First, a dynamic modeling of the OFA based on a single ducted-fan vehicle is performed. Second, the inner loop for attitude control is designed through dynamic model inversion and a PD controller. However, an adaptive control component is needed to flexibly cope with the uncertainty because the operating environment of the OFA is varied, and uncertainty exists depending on the number of modules to be assembled and disturbances. In addition, the performance of the neural network adaptive controller is verified through a numerical simulation according to two scenarios.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss

2018 ◽  
Vol 233 (5) ◽  
pp. 1611-1628 ◽  
Author(s):  
Kijoon Kim ◽  
Seungkeun Kim ◽  
Jinyoung Suk ◽  
Jongmin Ahn ◽  
Nakwan Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Feedback Linearization ◽  
Flight Test ◽  
Adaptive Controller ◽  
Neural Network Controller ◽  
Longitudinal Response ◽  
The Neural Network ◽  
Aerial Vehicle ◽  
Six Degree Of Freedom

This paper investigates experimental evaluation via flight tests for applying adaptive neural network controller to a flying-wing type unmanned aerial vehicle experiencing partial wing-loss. For this, six-degree-of-freedom numerical model is constructed taking into account damage-induced changes to the unmanned aerial vehicle in aerodynamic coefficients, mass, center of gravity, and moments of inertia. Numerical simulations are performed to investigate the flight dynamics change and to verify the performance of the neural network based controller. During the flight test, main wing-loss is artificially generated by 22% or 33% area moment. The flight test verifies that the damaged unmanned aerial vehicle shows drastic roll behavior with the unstable longitudinal response, and the neural network based adaptive controller combined with feedback linearization successfully compensates for the wing damage.

Adaptive Control for Steering Engine Fault of Ducted Fan UAV

2014 ◽  
Vol 602-605 ◽  
pp. 834-843
Author(s):  
An Huang ◽  
Zhong Xi Hou
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Adaptive Controller ◽  
Attitude Control System ◽  
Model Reference ◽  
Ducted Fan ◽  
Model Reference Adaptive ◽  
The Impact ◽  
The Relationship ◽  
Good Attitude

For the steering engine fault of ducted fan UAV that may arise during the hovering, designing adaptive controller for attitude control. First, concentrating on modeling of the hovering state of ducted fan UAV, and getting the relationship between steering engine and attitude control. Then analyzing the impact of steering engine fault on the attitude control system basing on the control model. Finally, designing model reference adaptive controller basing on the fault model, so that the ducted fan UAV can maintain good attitude control if steering engine fault occurs during the hovering. Simulation results show that when steering engine fault occurs, the model reference adaptive controller can effectively inhibit the adverse effects brought by steering engine fault, so the attitude control system has strong adaptability and robustness.

Control System Design of a Hex-Rotor Aircraft Based on the Neural Network Sliding Mode Method

2014 ◽  
Vol 971-973 ◽  
pp. 418-421 ◽  
Author(s):  
Chang Jun Zhao ◽  
Yue Bai ◽  
Xun Gong ◽  
Dong Fu Xu ◽  
Zhi Jun Xu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flight Control ◽  
Sliding Mode ◽  
Flight Performance ◽  
The Novel ◽  
Flight Control System ◽  
The Neural Network ◽  
Mode Method ◽  
Coupling Characteristics

For the existing Multi-rotor aircrafts, the under-actuation and strong coupling characteristics have a remarkable influence on their flight performance. In order to overcome this effect, a novel Hex-Rotor aircraft is proposed in this paper. Based on the unique configuration of its six driving rotors, the Hex-Rotor aircraft has the ability to achieve the real independent control on the space 6-DOF channels. An autonomous flight control system with neural network sliding mode is designed. The simulation proved that the novel Hex-Rotor aircraft has desired maneuvering capability,and thehe control system is able to guarantee the aircrafts tracking flight of the aircraft.

scholarly journals Hover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller

2017 ◽  
Vol 13 (1) ◽  
pp. 67-72
Author(s):  
Abdul-Basset Al- Hussein
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Lyapunov Theory ◽  
Laboratory Model ◽  
Model Reference ◽  
The Neural Network ◽  
Three Degree Of Freedom ◽  
Model Reference Adaptive

Unmanned aerial vehicles (UAV), have enormous important application in many fields. Quanser three degree of freedom (3-DOF) helicopter is a benchmark laboratory model for testing and validating the validity of various flight control algorithms. The elevation control of a 3-DOF helicopter is a complex task due to system nonlinearity, uncertainty and strong coupling dynamical model. In this paper, an RBF neural network model reference adaptive controller has been used, employing the grate approximation capability of the neural network to match the unknown and nonlinearity in order to build a strong MRAC adaptive control algorithm. The control law and stable neural network updating law are determined using Lyapunov theory.

Neural Network-Based Sensor Fault Accommodation in Flight Control System

2013 ◽  
Vol 22 (3) ◽  
pp. 317-333 ◽  
Author(s):  
Seema Singh ◽  
T. V. Rama Murthy
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flight Control ◽  
Generalized Regression Neural Network ◽  
Successful Implementation ◽  
Sensor Fault ◽  
Fault Accommodation ◽  
Aircraft Model ◽  
The Neural Network ◽  
Radial Basis

AbstractThis article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection (KBNNFD). Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (MBNN) is developed, which uses the radial basis function of the neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network (GRNN), which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/Simulink. Successful implementation and simulation results are shown and discussed using Simulink.

Optimizing a Modular Half-Speed Synchronous Generator and Implementing Its Voltage Control System Based on a Neural Network

2021 ◽  
Vol 7 (7) ◽  
pp. 61-70
Author(s):  
Andrey A. TATEVOSYAN ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnets ◽  
Magnetic System ◽  
Voltage Control ◽  
Synchronous Generator ◽  
Voltage Regulation ◽  
Design Parameters ◽  
Design Scheme ◽  
The Neural Network

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

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