A neural network Proportional-Integral-Differential (PID) control based on a genetic algorithm for a coupled-tank system

2015 ◽  
pp. 45-50 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Pid Control ◽  
Proportional Integral ◽  
Tank System

scholarly journals Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3069-3077
Author(s):  
Feilong Zheng ◽  
Yundan Lu ◽  
Shuguang Fu
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Temperature Control ◽  
Intelligent Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Furnace Temperature ◽  
Proportional Integral ◽  
Intelligent Pid

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

Research on PID Neural Network Control System of Temperature for Agricutural Film Unit

2013 ◽  
Vol 341-342 ◽  
pp. 694-699
Author(s):  
Yue Feng ◽  
Mei Xia Qiao ◽  
Shuai Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Melting Process ◽  
Network Control ◽  
Heating Process ◽  
Neural Network Control ◽  
Proportional Integral ◽  
Pid Neural Network

The temperature of agricultural film unit affects the plastic film directly. Since unit heating process has the characters of time delay, nonlinear, time-varying and strong coupling. It is difficult to create a mathematical model structure of plastic melting process. Thus, temperature control is very difficult. This paper presents decoupling control strategy and corresponding control algorithm based on PID (proportional-Integral-differential) neural network. Proportional, integral, differential neurons form a three-layer neural network. This design gives full play to respective advantages of PID control and neural network, and takes advantage of BP neural network to establish the dynamic model of system.

PIDNN Based Intelligent Control of Ignition Oven

2011 ◽  
Vol 396-398 ◽  
pp. 493-497
Author(s):  
Yu Qian Ying ◽  
Jian Gang Lu ◽  
Jin Shui Chen ◽  
You Xian Sun
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Pid Control ◽  
Proportional Integral Derivative ◽  
Labor Costs ◽  
Neural Structure ◽  
Fuel Gas ◽  
Significant Saving ◽  
Proportional Integral

In a steel plant, fuel gas caloricity of ignition oven always changes rapidly and largely. Consequently, the temperature of ignition oven can’t keep steady. To overcome this problem we employ intelligent control of ignition oven based on PIDNN (Proportional-Integral-Derivative Neural Network). As we know, ignition oven is a nonlinear, large delay and slow time-varying process, so traditional PID control usually doesn’t work well. Artificial neural networks can perform adaptive control by learning, so we adopt Proportional-Integral-Derivative neural network to tackle the problem taking the advantages of both PID control and neural structure. In order to satisfy the restrictions of industrial instruments, we combine PIDNN control algorithm with expert system mechanism to fulfill the final intelligent control strategy. At a sintering plant in Hangzhou, we deploy the intelligent control strategy turning out a satisfactory result that the ignition oven temperature can be controlled steadily within a much smaller range with significant saving of labor costs and improving of energy efficiency.

Application of Optimized BPNN Based on QGA in PID Control of Coal Mine Detection Robots

2012 ◽  
Vol 605-607 ◽  
pp. 1605-1608
Author(s):  
Yang Yang He ◽  
Zhi Gang Niu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Pid Control ◽  
Motor Speed ◽  
Mine Detection ◽  
Function Model ◽  
Quantum Genetic Algorithm ◽  
On Line

This thesis regards TUT-CMDR type coal mine detection robots as the research object and put forward an application of optimized BP neural network based on Quantum Genetic Algorithm in PID Control of motor speed. Transfer function model of speed control system of TUT-CMDR motor was established. Firstly, initial weights and thresholds of BP neural network were optimized by Quantum Genetic Algorithm, and then BP neural network was designed to adjust the parameters of PID on line. Finally, the results show that the algorithm is feasible and superiority.

Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1245-1252
Author(s):  
Ying Ying Feng ◽  
Nan Mu Hui ◽  
Zong An Luo ◽  
Dian Hua Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Hydraulic System ◽  
Learning Ability ◽  
Hydraulic Control ◽  
Control Effect ◽  
Hydraulic Control System

For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

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