Software Implementation of the Neural Network Controller in Systems with Complementary Correction of the Control Action

2020 ◽  
Author(s):  
Alexandr Kobzev ◽  
Yuri Mishulin ◽  
Anastasia Lekareva ◽  
Oxana Sidorova
Keyword(s):  
Neural Network ◽  
Software Implementation ◽  
Control Action ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

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.

Design and Realization of Experimental Autonomous Driving System Based on Neural Network Control

2012 ◽  
Vol 241-244 ◽  
pp. 1953-1958
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jie Chang Wu ◽  
Jia Ming Wu
Keyword(s):  
Neural Network ◽  
Autonomous Driving ◽  
Network Control ◽  
Neural Network Control ◽  
Test Results ◽  
Driving System ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Autonomous Driving System

Intelligent vehicle is an attractive solution to the traffic problems caused by automobiles. An experimental autonomous driving system based on a slot car set is designed and realized in the paper. With the application of a wireless camera equipped on the slot car, the track information is acquired and sent to the controlling computer. A backpropogation (BP) neural network controller is built to imitate the way of player’s thinking. After being trained, the neural network controller can give the proper voltage instructions to the direct current (DC) motor of the slot car according to different track conditions. Test results prove that the development of the autonomous driving system is successful.

scholarly journals Application of Firefly Algorithm based Neural Network Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2659-2662
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Hidden Layer

The Firefly Algorithm is comparison of new optimize procedure based on PSO as tautness. The paper presents the competence and forcefulness of the Firefly algorithm as the optimize concept for a proportional–integral–derivative organizer under various loading conditions. The proposed PID controller is attempt to designed and implemented to frequency-control of a two area interconnected systems. The hidden layer formation is not personalized, as the interest lies only on the reckoning of the weights of the system. In sequence to obtain a practicable report, the weights of the neural network are computational or optimized by minimizing function cost or error. A Firefly Algorithm is an efficient but uncomplicated meta-heuristic optimization technique inspired by expected motion of fireflies towards more light, is used for the preparation of neural network. The simulation report view that the calculation competence of training progression using Firefly Optimization performance with Load frequency control. A study of the output report of the system PID controller and FA based neural network controllers are made for 1% change in load in area 1 and it is found that the proposed controllers ensures a better steady state response of the systems

scholarly journals A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3423 ◽  
Author(s):  
El Barhoumi ◽  
Ikram Ben Belgacem ◽  
Abla Khiareddine ◽  
Manaf Zghaibeh ◽  
Iskander Tlili
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Flow Rate ◽  
Fuel Flow Rate ◽  
Supply Pressure ◽  
Fuel Flow ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Interleaved Boost Converter

This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs.

Application of Multivariable Non-Linear Decoupling Control Based on Neural Network

2012 ◽  
Vol 214 ◽  
pp. 786-791
Author(s):  
Jian Bo Zhang ◽  
Dong Hai Fan ◽  
Ren Zhi Hu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Distributed Control ◽  
Decoupling Control ◽  
Distributed Control System ◽  
Neural Network Controller ◽  
Mimo Nonlinear System ◽  
The Neural Network ◽  
Network Controller ◽  
The Mathematical Model

Aimed at Neural Network can approach any nonlinear system with arbitrary accuracy, the frame of distributed NN decoupling system are proposed to decouple the MIMO nonlinear system. In this paper, we designed and finished the Distributed Control System based on ABB’s Freelance 800F, and collected experimental data to model the thermostatic heater, then we have carried out the mathematical model by means of MATLAB dynamic simulation. In sequence, we trained the neural network controller in MATLAB. When the decoupling is completed, we used controller to control the MIMO nonlinear system in DCS. Experiment result shows that it is conscientiously feasible and deserves to be widely applied in the process of controlling industry.

scholarly journals Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Mohit ◽  
Deepak Chhabra ◽  
Suresh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Active Vibration Control ◽  
Cantilever Plate ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Active Vibration ◽  
Artificial Neural

The active vibration control (AVC) of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

scholarly journals Neural Network Controller for Power Electronics Circuits

2017 ◽  
Vol 6 (2) ◽  
pp. 49
Author(s):  
K.J. Rathi ◽  
M. S. Ali
Keyword(s):  
Neural Network ◽  
Power Electronics ◽  
Intelligent Control ◽  
Output Voltage ◽  
Network Applications ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
The Neural Networks ◽  
Neural Network Applications

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK

Neural Network Control of a Flexible Link Manipulator in Contact With a Compliant Environment

2010 ◽  
Author(s):  
Reza Sabzehgar ◽  
Mehrdad Moallem
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
A Priori ◽  
Network Control ◽  
Model Parameters ◽  
Flexible Link ◽  
System A ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

A neural network controller for regulating the contact force of a flexible link manipulator in contact with a compliant environment is proposed in this paper. The dynamic model of a single-link flexible (SLF) manipulator is obtained using three rigid sub-links connected by two virtual springs. It is assumed that the length of each link is short enough to be considered as a rigid link. A neural network-based control strategy is then proposed to relax the a-priori knowledge of the model parameters of the flexible link manipulator. The weights of the neural network controller are adjusted to minimize the error between the actual contact force and desired force. To overcome the non-minimum phase characteristic of the system, a weighted term of input signal is added to controller’s cost function. Simulation results are presented to evaluate performance of the proposed controller.

scholarly journals Active Engine Mounting Control Algorithm Using Neural Network

2009 ◽  
Vol 16 (4) ◽  
pp. 417-437 ◽  
Author(s):  
Fadly Jashi Darsivan ◽  
Wahyudi Martono ◽  
Waleed F. Faris
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Pid Controllers ◽  
Engine Vibration ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Active Engine

This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

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