The Simulation of Robot Control Based on CMAC Neural Network PID Control Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 659-663
Author(s):  
Jian Li Yu ◽  
Ya Zhou Di ◽  
Lei Yin
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Robot Control ◽  
Control Algorithm ◽  
External Disturbance ◽  
System Model ◽  
Robot System ◽  
System Parameters ◽  
Cmac Neural Network ◽  
Neural Network Pid

According to the problem of nonlinear and uncertainty in robot control, this paper proposes a PID control algorithm based on CMAC neural network model, for the elimination of the influence of uncertainty caused by robot system parameters and external disturbance. The simulation results show that this algorithm can effectively overcome the uncertainties and external disturbance of robot system model, this algorithm has good robustness and stability, its performance is superior to the traditional PID control algorithm.

Fast Neural Network PID Control Algorithm Based on Multi-Step Predictive Index Function

1998 ◽  
Vol 31 (14) ◽  
pp. 167-168
Author(s):  
Jin Qibing ◽  
Wang Jianhui ◽  
Wang Yunhua ◽  
Gu Shusheng
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Predictive Index ◽  
Index Function ◽  
Neural Network Pid

scholarly journals Design of a New Catheter Operating System for the Surgical Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xu Ma ◽  
Jinpeng Zhou ◽  
Xu Zhang ◽  
Yang Qi ◽  
Xiaochen Huang
Keyword(s):  
Neural Network ◽  
Operating System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Radiation Injury ◽  
Control Parameters ◽  
Simulation Experiments ◽  
Neural Network Pid

In interventional surgery, the manual operation of the catheter is not accurate. It is necessary to operate the catheter skillfully and effectively to protect the surgeon from radiation injury. The purpose of this paper is to design a new robot catheter operating system, which can help surgeons to complete the operation with high mechanical precision. On the basis of the original mechanical structure—real catheter, the operation information of the main end operator is collected. After the information is collected, the control algorithm of the system is improved, and the BP neural network is combined with the traditional PID controller to adjust the PID control parameters more effectively and intelligently so that the motor can reflect the output of the controller better and faster. The feasibility and superiority of the BP neural network PID controller are verified by simulation experiments.

scholarly journals Study on Computer Monitoring System for Oilfield Joint Stations Based on the BP Neural Network PID Control Algorithm

2021 ◽  
Author(s):  
Jingtian Xu ◽  
yanli qiao
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
The Core ◽  
Computer Monitoring ◽  
Three Phase ◽  
Phase Separator ◽  
Neural Network Pid

Abstract: The Hanqu Joint Station of the Dingbian Oil Production Plant of Yanchang Oilfield Co., Ltd is located at the edge of the desert in northern China. the bad field conditions and strong sandstorm, the hardware of computer monitoring system of Joint Station is often damaged. At the same time, the core equipment of the joint station three-phase separator oil chamber liquid level is hard to achieve high precision constant value control, the general control algorithm is difficult to meet the control requirements.This paper proposed a design scheme of a oilfield joint station computer monitoring system based on the Siemens S7-300 PLC, the hardware of the monitoring system adopts the redundancy scheme of dual monitoring computers, dual programmable logic controllers (PLCs), and dual industrial Ethernet. The BP neural network PID control algorithm was used to realize constant value control of the oil chamber liquid level of the three-phase separator of the core equipment of the joint station，and realized high control precision. The monitoring system could well adapt to the harsh environment of the scene, and showed high reliability and efficiency.

Beer Fermentation Temperature Control Curve Optimization Based on the Fuzzy - Neural Network PID Control Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 325-328
Author(s):  
Xian Qiu Xu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Response Speed ◽  
Integral Control ◽  
Beer Fermentation ◽  
Fuzzy Neural ◽  
Neural Network Pid

An auto-control model is presented to the process of beer fermentation, which has the characteristics such as time-varying, inertia, time-delay and nonlinear. The traditional PID control is difficult to accurately control. This paper according to the beer fermentation of problems puts forward a new control algorithm: fuzzy-neural network PID control algorithm. The fuzzy logistic differential control and intelligent integral control were supplemented into the fuzzy set-point weight tuning, so that the insufficiency of original PID control method was effectively improved. The advantages of this control algorithm are not only constitute a simple, small static error, dynamic response speed but also the ability to learn, etc. Therefore it not only can strengthen robust and intelligence of the system, but also make design simple and easily be required.

scholarly journals Application of Fuzzy Neural Network PID In Laser-guided Mortar Projectile

2021 ◽  
Vol 2113 (1) ◽  
pp. 012078
Author(s):  
Yang Song ◽  
Fangxiu Jia ◽  
Xiaoming Wang ◽  
Dingming Meng ◽  
Lei Zhuang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Electromechanical Actuator ◽  
Fuzzy Neural ◽  
The Mathematical Model ◽  
Neural Network Pid

Abstract Based on the high control performance requirement of laser-guided mortar control system, the permanent magnet synchronous motor (PMSM) is adopted in this paper as the electromechanical actuator of the system, the mathematical model of the motor is analyzed, and the vector control technology is adopted to achieve precise control of position, speed and torque of the electromechanical actuator. Aiming at the characteristics of non-linearity, strong coupling and large parameter changes of the system in flight, an improved fuzzy neural network PID control method is proposed by combining the classical PID control algorithm with fuzzy control and neural network control algorithm to realize the real-time tuning and optimization of PID parameters. The mathematical model of the electromechanical actuator control system is established and simulated. The results show that the fuzzy neural network PID control has good tracking performance, small amplitude error, and strong adaptability to load changes.

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