The application of intelligent control algorithm in sanitation monitoring system

2017 ◽  
Author(s):  
Mei Chen ◽  
Linghan Zhu
Keyword(s):  
Monitoring System ◽  
Intelligent Control ◽  
Control Algorithm

RBF Neural Network Used on Universal Intelligent Monitoring System in Heavy Machinery

2014 ◽  
Vol 556-562 ◽  
pp. 3242-3245 ◽  
Author(s):  
Si Yuan Lu ◽  
Qing Hui Zhou ◽  
Qing Bo Li
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Intelligent Monitoring ◽  
Universal System ◽  
Heavy Machinery ◽  
Intelligent Control System ◽  
Micro Controller Unit

In the intelligent monitoring system of heavy machinery, the key technology is control algorithm because it is difficult to formulate exact mathematical model to the nonlinear and time-varying characteristic in heavy machinery. The monitoring system is firstly designed and developed for the universal system in heavy machinery. And then, RBF Neural Network is applied in the control strategy by OD2131 MCU(micro controller unit). The simulated curve is displayed by Matlab. Their dada communication between OD2131 MCU and Matlab is in CANopen protocol. Finally, the simulation is made for an electric motor. The simulation results show that adding artificial neural network compensation can enhance the accuracy in the intelligent control system.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

scholarly journals Application Research of Intelligent Control Algorithm in Air Conditioning System

2021 ◽  
Vol 687 (1) ◽  
pp. 012174
Author(s):  
Xin Yang ◽  
Yuqing Zhao ◽  
Jiayu Deng ◽  
Shengshi Tang ◽  
Hexuan Su ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Air Conditioning ◽  
Control Algorithm ◽  
Application Research ◽  
Air Conditioning System

Robust Control Method Based on Machine Learning for Boiler Combustion System

2014 ◽  
Vol 685 ◽  
pp. 368-372 ◽  
Author(s):  
Hao Zhang ◽  
Ya Jie Zhang ◽  
Yan Gu Zhang
Keyword(s):  
Robust Control ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Control Method ◽  
Support Vector ◽  
Control Performance ◽  
Fuzzy Pid Control ◽  
On Line ◽  
Control Output

In this study, we presented a boiler combustion robust control method under load changes based on the least squares support vector machine, PID parameters are on-line adjusted and identified by LSSVM, optimum control output is obtained. The simulation result shows control performance of the intelligent control algorithm is superior to traditional control algorithm and fuzzy PID control algorithm, the study provides a new control method for strong non-linear boiler combustion control system.

A semi-custom ASIC implementation of an intelligent control algorithm for a reconfigurable data acquisition system

2005 ◽  
Author(s):  
Hai Phuong Le ◽  
Ronny Veljanovski ◽  
Aladin Zayegh ◽  
Jugdutt Singh ◽  
Aleksandar Stojcevski
Keyword(s):  
Data Acquisition ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Data Acquisition System ◽  
Acquisition System

scholarly journals Wireless Real-Time Monitoring System for the Implementation of Intelligent Control in Subways

10.5772/62679 ◽  
2016 ◽  
Author(s):  
Alessandro Carbonari ◽  
Massimo Vaccarini ◽  
Mikko Valta ◽  
Maddalena Nurchis
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Intelligent Control ◽  
Real Time Monitoring

scholarly journals Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

2020 ◽  
Vol 8 (3) ◽  
pp. 210 ◽  
Author(s):  
Renqiang Wang ◽  
Donglou Li ◽  
Keyin Miao
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Intelligent Control ◽  
Basis Function ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Unmanned Surface Vehicles ◽  
Radial Basis

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

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