Single Neuron PID Control of Aircraft Deicing Fluids Rapid Heating System

2013 ◽  
Vol 8 (2) ◽  
Author(s):  
Bin Chen ◽  
Hong-zhen Yang ◽  
Li-wen Wang
Keyword(s):  
Pid Control ◽  
Single Neuron ◽  
Rapid Heating ◽  
Heating System ◽  
Single Neuron Pid ◽  
Aircraft Deicing Fluids

Design and Research of Energy Saving Control System for Heating Network

2012 ◽  
Vol 591-593 ◽  
pp. 1405-1409
Author(s):  
Hong Xing Sun ◽  
Chuang Gao ◽  
Xin Yan
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Heating System ◽  
Loop Model ◽  
Control Effect ◽  
Central Heating ◽  
Single Neuron Pid ◽  
Energy Saving Control ◽  
Controlled Object

In the central heating system, because the controlled object has the characteristics of time-varying, nonlinear, strong coupling and big lag. The traditional single loop model and the conventional PID control algorithm are difficult to react the mechanism and meet the control requirements. Therefore a model of multiplex heating network is established. By comparing the single neuron PID control algorithm with the traditional PID control algorithm, the results show that the single neuron PID control algorithm has a better control effect.

Stability Analysis of Bridge Crane Hoists

2014 ◽  
Vol 543-547 ◽  
pp. 111-114
Author(s):  
Yun Yan Song
Keyword(s):  
Control Strategy ◽  
Pid Control ◽  
Single Neuron ◽  
Bridge Crane ◽  
Matlab Simulation ◽  
Factors Affecting ◽  
Overhead Cranes ◽  
Depth Study ◽  
Main Factors ◽  
Single Neuron Pid

Hoist bridge crane swinging phenomenon often occurs during the operation, making the work more difficult, there is a certain danger. To solve this problem, anti-sway system for overhead cranes depth study to identify the main factors affecting heavy lifting swinging; proposed single neuron PID control strategy, while using MATLAB simulation used to verify the reliability of the method.

Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation

2010 ◽  
Vol 139-141 ◽  
pp. 1945-1949
Author(s):  
Tian Pei Zhou ◽  
Wen Fang Huang
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Control Method ◽  
Optimization Method ◽  
Separation Process ◽  
Control Effect ◽  
Environment Change ◽  
Good Convergence ◽  
Single Neuron Pid

In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.

scholarly journals Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3929 ◽  
Author(s):  
Cristian Napole ◽  
Oscar Barambones ◽  
Isidro Calvo ◽  
Javier Velasco
Keyword(s):  
Pid Control ◽  
Single Neuron ◽  
Absolute Error ◽  
Linear Effect ◽  
Learning Rules ◽  
Artificial Neural ◽  
Single Neuron Pid ◽  
Artificial Neural Network Ann ◽  
And Control ◽  
Device Operation

This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.

Based on Single Neuron PID Control of Vehicle Active Suspension System

2013 ◽  
Vol 380-384 ◽  
pp. 528-531 ◽  
Author(s):  
Xiao Feng Liu ◽  
Xin Hua Xie
Keyword(s):  
Control Strategy ◽  
Pid Control ◽  
Single Neuron ◽  
Ride Comfort ◽  
Control Strategies ◽  
Active Suspension ◽  
Vital Role ◽  
Suspension System ◽  
Suspension Control ◽  
Single Neuron Pid

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

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