Fuzzy Self-Tuning PID Temperature Control Modeling and Simulation System

2015 ◽  
Vol 713-715 ◽  
pp. 854-858 ◽  
Author(s):  
Zheng Qiang Guan ◽  
Xiao Ming Luo ◽  
Le Peng Song
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Pid Control ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Temperature Control System ◽  
Temperature Hysteresis ◽  
Fuzzy Pid Control ◽  
Time Problem ◽  
Self Tuning

For the current general heating control system tracks the set temperature hysteresis heating device to automatically adjust the real-time problem of poor design a fuzzy self-tuning proportional-integral-derivative (PID) controller parameters. Scoop out using PID control and fuzzy control algorithm combined method; fuzzy PID control parameters are adjusted. Use Matlab to model in simulink, the aryl the controller simulation analysis. The results show that the fuzzy self-tuning PID controller overshoot σ≈l%, steady-state error Island es=0. This method can improve the performance of the temperature control system..

Application of a New Smith Fuzzy PID in Electric Boiler Temperature Control System

2015 ◽  
Vol 727-728 ◽  
pp. 633-636 ◽  
Author(s):  
Qiang Gu ◽  
Feng Long Zheng ◽  
Bin Bin Liu ◽  
Wen Yan Yang
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Pid Control ◽  
Pid Controller ◽  
Temperature Control System ◽  
Model Parameters ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller ◽  
Electric Boiler

Since the object model is time-varying and time-delay in the electric boiler temperature control system, Smith Fuzzy PID controller is applied in it. Simulation results in the paper show that the overshoot is reduced by 31.3% compared with the system under Smith PID control and the adjusting time reached a 30% reduction compared with those under Fuzzy PID control with T=380 and τ=110. In order to eliminate the oscillation problem in system when model parameters change, a new Smith Fuzzy PID controller with two degrees of freedom is applied, whose control performance is much better than that of Smith Fuzzy PID.

Simulation Analysis of Electro-Pneumatic Proportional Fuzzy-PID Control System Based on AMESim/Simulink

2014 ◽  
Vol 945-949 ◽  
pp. 2568-2572
Author(s):  
Si Yuan Wang ◽  
Guang Sheng Ren ◽  
Pan Nie
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Proportional Control ◽  
Fuzzy Pid Controller ◽  
Design Requirements ◽  
Set Up

The test rig for hydro-pneumatic converter used in straddle type monorail vehicles was researched, and its electro-pneumatic proportional control system was set up and simulated based on AMESim/Simulink. Compared fuzzy-PID (Proportion Integral Derivative) controller with PID controller through fuzzy logic tool box in Simulink, the results indicate that, this electro-pneumatic proportional control system can meet design requirements better, and fuzzy-PID controller has higher accuracy and stability than PID controller.

Research on Fuzzy Pid Control System of Temperatuer for Tertiary Air

2013 ◽  
Vol 341-342 ◽  
pp. 892-895
Author(s):  
Jun Chao Zhang ◽  
Shao Hong Jing
Keyword(s):  
Control System ◽  
Control Theory ◽  
Pid Control ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Effective Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller ◽  
Self Tuning

The introduction of the AQC boiler has complex effects on the temperature of Tertiary air, traditional PID is difficult to achieve the effective control. Combined the method of the conventional PID with the fuzzy control theory, a fuzzy self-tuning PID controller is designed. Compared with traditional PID, results of simulation show that the fuzzy PID controller improves not only the adaptability and robustness of the system, but also the system's static and dynamic performance.

The Control System of Test Bench for Thermal Insulating Properties of Building Doors and Windows Based on Fuzzy-PID Control

2013 ◽  
Vol 385-386 ◽  
pp. 968-972
Author(s):  
Zhi Guang Gong ◽  
Chun Hui Du ◽  
Ya Jie Li ◽  
Jiang Li Yu
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Pid Control ◽  
Test Bench ◽  
Temperature Control System ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Temperature Measuring ◽  
Thermal Insulating ◽  
Insulating Properties

According to many temperature measuring points, the large delay, nonlinear and time-varying characteristics of the temperature control system of test bench for thermal insulating property, By combining fuzzy-PID control technology with Zigbee WSN technology, a design scheme of the automatic control system of test bench for thermal insulating properties of building doors and windows is put forward, which is low-cost and high precision. The microprocessor of sensor node adopts CC2530 wireless MCU. The system adopts Zigbee wireless network and RS-485 bus to transmit digital temperature and humidity signals, and is no wiring, intermediate links are lessened, transmission error is reduced and temperature measuring accuracy is improved. By adopting Fuzzy-PID control, the self-adaptability of controller and the precision of temperature control is improved.

Multiple Models Fuzzy PID Controller Design in Air-Condition Temperature Control System

2013 ◽  
Vol 860-863 ◽  
pp. 1616-1619
Author(s):  
Hang Jiang ◽  
Xin Wang ◽  
Yi Hui Zheng ◽  
Li Xue Li ◽  
Yan Liu
Keyword(s):  
Temperature Control ◽  
Pid Control ◽  
Pid Controller ◽  
Controller Design ◽  
Temperature Control System ◽  
Multiple Models ◽  
Multiple Model ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller

This paper deals with the study of air-condition temperature control, which is a complex system of time varying, nonlinear, imprecise model and uncertain work environment. In this paper, a multiple models fuzzy PID controller is designed. Considering the air-condition temperature adjustment system to combat aircraft and the frequently changing external environment the multiple fuzzy PID controllers are estimated according to the temperature changing area. Each time, based on temperature tested, only one fuzzy PID Controller is chosen to improve the control precision. At the end, simulation results show that multiple model fuzzy PID control is superior to the single fuzzy PID control, which effectively improve the transient response of the system, the steady state accuracy and robustness, having good prospects for engineering applications.

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