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.

Design of Control System for Detection of Sub-Nanometer Wood Flour Based on Neural Network

2011 ◽  
Vol 393-395 ◽  
pp. 44-48
Author(s):  
De Zhi Guo ◽  
Chun Mei Yang ◽  
Yan Ma
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Wood Flour ◽  
Network Control ◽  
Neural Network Control ◽  
Control Effect ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

In this paper, the detection of sub-nanometer wood flour based on neural network control, how to improved the quality of wood flour is proposed. In the analysis of the advantages of neural network controller, as the auxiliary controller for the PID controller, and improving the control effect of the system. With the contrast of the experimental results, illustrates the quality of the sub-nanometer wood flour has been improved by the neural network control.

A Neural Network Method for Miniature Unmanned Helicopter Heading Control

2012 ◽  
Vol 468-471 ◽  
pp. 93-96
Author(s):  
Meng Bai ◽  
Min Hua Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Network Control ◽  
Neural Network Control ◽  
Unmanned Helicopter ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Heading Control ◽  
Helicopter Aerodynamics

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

Study on the Fuzzy Neural Network Control Used in Wastewater Treatment

2011 ◽  
Vol 71-78 ◽  
pp. 3127-3132 ◽  
Author(s):  
Zhong Qi Wang ◽  
Cheng Zhao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Dissolved Oxygen ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Network Controller ◽  
Strong Robustness ◽  
Fuzzy Neural

In this paper, we introduce the study on fuzzy neural network control used in wastewater treatment. An effective fuzzy neural network controller is proposed. The simulation result shows that the system gives strong robustness and good dynamic characteristics. It is used to control dissolved oxygen and forecast water quality. The result indicates that the concentration of dissolved oxygen can reach expectation fleetly and effectively. The model has better precision of forecasting and faster speed of convergence.

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.

Teaching Neural Network Control System Design Using a Low-Cost Rapid Control Prototyping Platform

2013 ◽  
Author(s):  
Francisco Franquiz ◽  
Alecia Hurst ◽  
Yan Tang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Low Cost ◽  
Network Control ◽  
Control System Design ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Network Controller ◽  
Rapid Control ◽  
Rapid Control Prototyping

This paper presents the use of a low-cost rapid control prototyping platform, HILINK, in teaching a graduate course on neural network control system design for mechanical engineering students. The HILINK platform offers a seamless interface between physical plants and Simulink for implementation of hardware-in-the-loop real-time control systems. With HILINK, student can quickly build a neural network controller for applications using Neural Network Toolbox in Simulink. As a result, students can use one single environment for both computer simulation and hardware implementation to understand theories and tackle practical issues in a limited time frame. The paper presents the experimental setup and implementation process of the NARMA-L2 controller for DC motor speed control, and demonstrates the convenience and effectiveness of using HILINK in developing a neural network controller.

Varying Parameters of the Object Model on Fuzzy Neural Network Controller for Activated Sludge Method of the Sewage Treatment System

2011 ◽  
Vol 230-232 ◽  
pp. 339-345
Author(s):  
Zhao Hui Shi ◽  
Cheng Zhi Wang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Sewage Treatment ◽  
Network Control ◽  
Neural Network Control ◽  
Dissolved Oxygen Concentration ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

In this paper, we take characteristics of wastewater treatment and process technology, drawing on the effectiveness of thetraditional PID control and on the basis of its lack, with the key steps in the sewage treatment process - Aeration control of part of the process parameters, Fuzzy neural network control of dissolved oxygen concentration (DO) to achieve negative feedback control loop,design a model-based closed-loop cascade control system. Fuzzy systems, membership function, the structure of the network topology and algorithms are based on the actual issues identified in the fuzzy variables. Aiming at the four parts of the fuzzy control, adopting four fuzzy neural network based on the standard model - the input layer, Fuzzy layer,Inference layer,Clear layer are corresponding with it. Standing on two points: the dissolved oxygen concentration control and the rate of change from the error ,then design the Fuzzy neural network controller. Then the fuzzy neural network control technology could be used in wastewater treatment on the specific application of process control.

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.

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

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