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.

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.

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.

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.

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.

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