scholarly journals Research on pH Value Control System of Haematococcus Pluvialis Algae Based on Fuzzy PID Algorithm

2020 ◽  
Vol 1631 ◽  
pp. 012051
Author(s):  
Shigang Cui ◽  
Xingzong Cai ◽  
Yongli Zhang ◽  
Xingli Wu ◽  
Lin He
2012 ◽  
Vol 542-543 ◽  
pp. 217-222
Author(s):  
Ren Jiang Li ◽  
Tie Wang ◽  
Cheng Xun Sun

The Sampling flow will be decreased because the permeability weakness of filterable membrane after the dust sampler working for a period of time. It seriously affects the accuracy of measurement, so we should keep the dust sampler working under the constant gas flow. The stable, rapidness and accuracy constant sampling can be achieved based on the application of fuzzy-PID algorithm and the adoption of PWM technique. It has carried on design to the dust sampler control system from selecting the hardware and designing the software to data processing and errors analysis, and discussing the preferable performance of the dust sample control system.


2012 ◽  
Vol 562-564 ◽  
pp. 1594-1597
Author(s):  
Chun Qia Liu ◽  
Shi Feng Yang

The fluidized bed is a complex system with a big lag, time-varying and non-linear. The conventional-PID methods are simple, practical, and high reliability. However, choosing and adjusting PID parameters rely on manual way. It is difficult to choose appropriate values when temperature requirement is higher. Inappropriate values may cause large overshoot and low control precision. Thus, in order to obtain more accurate and rapid PID control parameters and to avoid errors caused by human factors, the fuzzy control and PID algorithm were applied to the fluidized bed furnace temperature control system. The Fuzzy-PID controller was designed and the three PID parameters' self-tuning was realized. Simultaneously, the upper computer and the lower computer were designed. The lower computer mainly completed temperature measurement and adjustment functions. The collected temperature was transferred back to the upper computer at regular intervals. The upper computer was designed by virtual instrument technology. Practical operation shows that the temperature variation is below 0.3 when heating oven is in stable state and is close to the ideal PID response curve, which meets the average requirements of the fluidized bed heating oven. As an advanced reactor, fluidized bed was widely used in industrial process such as combustion, gasification and catalytic cracking[1].As the temperature affect the gas product composition of the fluidized bed, so improving the furnace temperature utilizing the automatic control system is one of the important issues furnace. The fluidized bed heating oven is heated by resistance wire heating and cooled by natural cooling. The temperature control after the adjustment is slow. It is a complex system with a big lag, time-varying and non-linear. Currently, the conventional-PID methods were taken to control the fluidized bed heating oven's temperature. This method is simple, practical, and high reliability. However, choosing and adjusting PID parameter rely on manual way, it is difficult to choose an appropriate values .Inappropriate values may cause large overshoot and low control precision. Thus, in order to obtain more accurate and rapid PID control parameters and to avoid errors caused by human factors, the fuzzy control and PID algorithm are applied to the fluidized bed furnace temperature control system. The self-tuning fuzzy PID controller is designed. Compared with the outdated control methods, PC control is more flexible and even more long-range.


Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1506
Author(s):  
Yongchao Shan ◽  
Lixin Zhang ◽  
Xiao Ma ◽  
Xue Hu ◽  
Zhizheng Hu ◽  
...  

An appropriate pH value of liquid fertilizer can enable crops to better absorb nutrients from fertilizers. However, the mixed liquid fertilizer with high concentration of liquid fertilizer and irrigation water has a high pH value, which affects the absorption of nutrients by crops. Therefore, the precise regulation of liquid fertilizer pH value is an important link to realize the integration of water and fertilizer in modern agriculture. Due to pipeline transportation and diffusion of the regulating liquid and liquid fertilizer, the pH value control system has the characteristics of time-varying, non-linear and time-delayed models, and it is difficult for ordinary controllers to accurately control the pH value of liquid fertilizer. Therefore, modern agriculture urgently needs a controller that can adapt to non-linear and uncertain systems. According to the characteristics of the pH regulation process of liquid fertilizer, this study proposes and designs a modified fuzzy-PID-Smith predictive compensation algorithm, which adds the fuzzy-PID algorithm to the predictor of the conventional Smith algorithm to compensate for the error between the actual and theoretical models in order to reduce the decline of control quality caused by the model mismatch to the control system. To verify the practicability and robustness of the algorithm in practical applications, a liquid fertilizer pH value control system with STM32F103ZET6 as the control core was developed. The pH control system with fuzzy-PID and Smith algorithm as controller was used as the control group. The model was simulated and tested under two conditions of exact matching and imprecise matching, and performance tests were carried out under different output flow rates. The results showed that the maximum overshoot of the modified fuzzy-PID-Smith predictive compensation algorithm was significantly less than that of the other two algorithms at different output flow rates, with an average of 0.23%. The average steady-state time of adjusting the pH value of liquid fertilizer from 7.3 to 6.8 was 72 s, which was superior to the 145 s and 3.2% of fuzzy-PID and 130 s and 1.4% of the Smith controller.


2013 ◽  
Vol 336-338 ◽  
pp. 637-640
Author(s):  
Dong Hui Li ◽  
Yi Hui Xu

Mathematical model of pipeline pressure is built according to mass conservation. Aiming at solving the poor adaptability of routine PID algorithm and the disturbance of inlet steam, adaptive fuzzy-PID algorithm and Feed-forward controller are proposed in the control system. System simulation is conducted by MATLAB, and parameter self-tuning rules determined by different errors are studied. Simulation results show that the pressure control system has better static and dynamic performance such as quicker response, smaller overshoot and better capacity of resisting disturbance.


Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 118
Author(s):  
Hongqiao Yin ◽  
Wenjun Yi ◽  
Jintao Wu ◽  
Kangjian Wang ◽  
Jun Guan

Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.


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