An Improved Single Neuron Adaptive PID Control Algorithm

2009 ◽  
Author(s):  
Xu Bao-chang ◽  
Wu Jian-zhang ◽  
Chen Yong-kun
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Adaptive Pid Control

Advancing Precision of Grating Tiling with Modified Single Neuron Adaptive PID Control Algorithm

2012 ◽  
Vol 39 (5) ◽  
pp. 0502014
Author(s):  
王聪 Wang Cong ◽  
杜丽 Du Li ◽  
张军伟 Zhang Junwei ◽  
周海 Zhou Hai ◽  
王逍 Wang Xiao ◽  
...  
Keyword(s):  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Adaptive Pid Control

Research and Design of DC Blushless Motor Speed Control System Based on Single Neuron PID Control

2013 ◽  
Vol 347-350 ◽  
pp. 322-326
Author(s):  
Qiao Hong Li ◽  
Fang Hou
Keyword(s):  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Speed Control ◽  
Motor Speed ◽  
Brushless Dc ◽  
Brushless Motor ◽  
Speed Control System ◽  
Adaptive Pid Control

The speed regulating system of DC blushless motor was mostly studied. This paper is based on a simplified mathematical model of Brushless DC motor which was consisted of the traditional PID and single neurons. In the Simulink environment, it is established by the control algorithm of single neuron adaptive PID brushless DC motor speed control system closed loop simulation model. From simulation results, the single neuron adaptive PID control system of DC brushless motor has excellent dynamic and static performance. Based on the analysis of DC blushless motor speed control system and simulation results of the neural network control algorithm, hardware of the digital control system for DC brushless motor is designed with control center of high performance microcontroller 80C196KC,which is of single neuron adaptive PID control algorithm.

Research on Single Neuron Adaptive PID Control

2012 ◽  
Vol 150 ◽  
pp. 174-177 ◽  
Author(s):  
Yan Hong Zhang ◽  
De An Zhao ◽  
Jian Sheng Zhang
Keyword(s):  
Neural Networks ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Single Neuron ◽  
Control Parameters ◽  
Control Effect ◽  
The Common ◽  
Single Neuron Pid ◽  
Adaptive Pid Control

As a branch of the intelligent control, neural networks is applied in control more and more widely, the single neuron adaptive PID control algorithm is studied in this paper, and the program is written by MATLAB, the common object of single neuron adaptive PID is simulated, and the effect of single neuron adaptive PID control parameters on control effect is analyzed, experimental results show that the single neuron PID control has more obvious advantages than general PID control.

Single Neuron Adaptive PID Control of the Silicon-Based Integrated Micro Nano-Positioning XY-Stage

2009 ◽  
Vol 60-61 ◽  
pp. 207-212
Author(s):  
Jia Chou Wang ◽  
Wei Bin Rong ◽  
Li Ning Sun ◽  
Xin Xin Li
Keyword(s):  
Pid Control ◽  
Single Neuron ◽  
Displacement Sensor ◽  
Robust Controller ◽  
Dynamical Characteristics ◽  
Repeatability Error ◽  
Control Scheme ◽  
Silicon Based ◽  
Adaptive Pid Control ◽  
Better Than

An integrated micro xy-stage is designed and fabricated for application in nanometer-scale operation and nanometric positioning precision. This device integrates the functions of both actuating and sensing in the same silicon ship and is mainly composed of a silicon-based xy-stage, electrostatics comb actuator and a displacement sensor. In this paper a robust control strategy based on single neuron adaptive PID control theory is developed for silicon-based xy-stage, considering electrical, mechanical, and stiffness models. Single neuron adaptive PID control enables compact realization of a robust controller tolerant of device characteristics variation, types of inherent instabilities, and improving dynamical characteristics. The experimental results verified that the controller is more suitable for the silicon integrated micro xy-stage, under which the settling time is less than 2.5ms and the repeatability error is better than ±24.9nm. In addition, the presented control scheme is simple to implement in practical application.

Design and Implementation of Intelligent Control System for Infrared Shrink Film Machine

2014 ◽  
Vol 532 ◽  
pp. 204-207
Author(s):  
Jiang Zhao ◽  
Wei Ke Fei ◽  
Chong Qing Hao ◽  
Ying Zhang
Keyword(s):  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Physical Simulation ◽  
Simulation System ◽  
Infrared Heating ◽  
Control Effect ◽  
Intelligent Control System ◽  
Technical Requirements ◽  
Adaptive Pid Control

In order to study the control problem of infrared heating shrinkage machine, i.e. Large delay,nonlinearity of the control system, one type of the basic organization and simulation principle of the hardw-in-the-loop simulation system are presented in the given paper, and the simulation model of infrared heating shrinkage machine is discussed. Aiming to solve the problem of traditional PID control algorithm is difficult to get ideal control effect, an adaptive PID control algorithm based on BP neural network is proposed. The object can be better online controlled and adjusted after the algorithm has been applied, meanwhile the requirement of accuracy and reliability will be improved, and quite a lot debugging time will be saved.The results show that the system basically satisfies the technical requirements and provides a good experimental platform.The study is provided with great significance for the realization of the semi-physical simulation system.

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.

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