Application of Neural Networks and PID in on Line of Real-Time Temperature Control System

2011 ◽  
Vol 110-116 ◽  
pp. 5009-5014
Author(s):  
Suleiman A. Elfandi ◽  
Mahmoud A. Osman ◽  
Nasser Ali ◽  
Nuri Agab
Keyword(s):  
Real Time ◽  
Pid Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Temperature Control System ◽  
Controlled System ◽  
Data Acquisition Card ◽  
On Line ◽  
Neural Network Algorithm ◽  
Velocity Form

The Heat Process Trainer PT326 (Feedback UK) model is obtained by using two different techniques one by the Ziegler-Nichols approximating method and the other with the system identification method. The Data acquisition card EC641 I/O PT326 is developed and tested. it can be used as an interface between PC and any similar controlled system. The “velocity” form of the PID controller algorithm is implemented in real time where this algorithm is an ideal of solving the bump less transfer mechanism between manual and automatic control operation and the implementation of anti-reset windup algorithm. The method of back propagation neural network algorithm online gives very good results with neither steady state error nor overshoots.

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen
Keyword(s):  
Surface Roughness ◽  
Real Time ◽  
Back Propagation ◽  
Cutting Parameters ◽  
Wavelet Network ◽  
Back Propagation Algorithm ◽  
The Real ◽  
Roughness Model ◽  
On Line ◽  
Artificial Network

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

A Novel Control Strategy for Pilot Controlled Proportional Flow Valve With Internal Displacement-Flow Feedback

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
He Wang ◽  
Xiaohu Wang ◽  
Jiahai Huang ◽  
Jun Wang ◽  
Long Quan
Keyword(s):  
Pressure Drop ◽  
Flow Rate ◽  
Control Strategy ◽  
Pid Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Internal Displacement ◽  
Valve Opening ◽  
Proportional Flow ◽  
Flow Valve

The present study is focused on the construction of a well-performing pilot controlled proportional flow valve with internal displacement-flow feedback. A novel control strategy for the valve is proposed in which the flow rate through the valve is directly controlled. The linear mathematical model for the valve is established and a fuzzy proportional–integral–derivative (PID) controller is designed for the flow control. In order to obtain the flow rate used as feedback rapidly and accurately in real-time, back propagation neural network (BPNN) is employed to predict the flow rate through the valve with the pressure drop through the main orifice and main valve opening, and the predicted value is used as the feedback. Both simulation and experimental results show that the predicted value obtained by BPNN is reliable and available for the feedback. The proposed control strategy is effective with which the flow rate through the valve remains almost constant when the pressure drop through the main orifice increases and the valve can be applied to the conditions where the independence of flow rate and load is required. For the valve with the proposed control strategy, the nonlinearity is less than 5.3%, the hysteresis is less than 4.2%, and the bandwidth is about 16 Hz. The static and dynamic characteristics are reasonable and acceptable.

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

scholarly journals Magnetorheological Elastomer Precision Platform Control Using OFFO-PID Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying-Qing Guo ◽  
Jie Zhang ◽  
Dong-Qing He ◽  
Jin-Bao Li
Keyword(s):  
Real Time ◽  
Fractional Order ◽  
Pid Controller ◽  
Vibration Isolation ◽  
Dynamic Responses ◽  
Magnetorheological Elastomer ◽  
Controlled System ◽  
Pid Algorithm ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

The magnetorheological elastomer (MRE) is a kind of smart material, which is often processed as vibration isolation and mitigation devices to realize the vibration control of the controlled system. The key to the effective isolation of vibration and shock absorption is how to accurately and in real time determine the magnitude of the applied magnetic field according to the motion state of the controlled system. In this paper, an optimal fuzzy fractional-order PID (OFFO-PID) algorithm is proposed to realize the vibration isolation and mitigation control of the precision platform with MRE devices. In the algorithm, the particle swarm optimization algorithm is used to optimize initial values of the fractional-order PID controller, and the fuzzy algorithm is used to update parameters of the fractional-order PID controller in real time, and the fractional-order PID controller is used to produce the control currents of the MRE devices. Numerical analysis for a platform with the MRE device is carried out to validate the effectiveness of the algorithm. Results show that the OFFO-PID algorithm can effectively reduce the dynamic responses of the precision platform system. Also, compared with the fuzzy fractional-order PID algorithm and the traditional PID algorithm, the OFFO-PID algorithm is better.

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