MBD of grey prediction fuzzy-PID irrigation control technology

Zhang, Yubin; Wei, Zhengying; Lin, Qiyin; Zhang, Lei; Xu, Jing

doi:10.5004/dwt.2018.22336

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Conventional PID control and fuzzy PID control can hardly avoid the overshoot and ensure the stability of the system. In our research a grey prediction model is adopted, which replaces the input of the PID method with the prediction value instead of the feedback value to reduce the overshoot and improve the stability of the system [31,32].…”

Section: Fuzzy Pid Temperature Control Based On Grey Prediction Modelmentioning

confidence: 99%

An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer

Peng

Yang

et al. 2023

Applied Sciences

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The reconstruction of deep in situ environment up to 95 °C and 70 MPa using water is critical for the fidelity testing of deep Earth rocks. The temperature and pressure of the water have strong coupling in such an environment, which makes the control of temperature and pressure very difficult. The paper firstly presents the design of the system of deep in situ environment reconstruction and core transfer (SERCT); secondly, for the problem of high temperature and pressure control, a pressure-temperature (P-T) interpolation control algorithm based on the iso–density P-T curves of water is proposed. A P-T coupling control path is decomposed into two independent interpolation paths: an iso–thermal pressure control and an iso–mass temperature control, which realizes the decoupling control of temperature and pressure. Then, a fuzzy-PID dual mode method is adopted for the pressure control after decoupling, which reduces the overshoot and the dynamic response time of the system. For temperature control, a segmented and grouped electric heating mode is designed to improve the uniformity of the temperature field. A fuzzy PID temperature control algorithm based on grey prediction is proposed to achieve high precision temperature control with small overshoot. Finally, the effectiveness of the proposed methods is verified by experiments.

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Section: Fuzzy Pid Temperature Control Based On Grey Prediction Modelmentioning

confidence: 99%

An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer

Peng

Yang

et al. 2023

Applied Sciences

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“…For complex PID parameter tuning problems, many scholars use swarm intelligence algorithms to optimize PID controller parameters. Zhang et al 25 designed a control model combining PID control, fuzzy control, and gray predictive control for water-fertilizer ratio adjustment and irrigation control accuracy in agricultural water-fertilizer irrigation. Hekimoglu et al 26 proposed the atomic Search Optimization (ASO) algorithm and its modified version to determine the control parameters of the PID controller for motor speed.…”

Section: Related Workmentioning

confidence: 99%

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Huang

Tian

Liu

et al. 2022

Sci Rep

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Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

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“…China introduced water-saving irrigation technology from developed countries decades ago, yet the technology is used infrequently in the total irrigated area in China. The literature [18] proposed a water diffusion controller using fuzzy control logic, and the experimental results showed that the controller designed by it has a remarkable effect in this regard. This paper is divided into three parts to discuss the intelligent agricultural irrigation control platform based on PLC technology.…”

Section: Introductionmentioning

confidence: 99%

Construction of an intelligent agricultural irrigation control platform based on PLC technology

Li,

Zhang,

Zhang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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This paper firstly constructs a PLC-based intelligent irrigation control system, analyzes the requirements of its system functions, and determines three major modules of the system: data acquisition module, intelligent irrigation module, and Web service module. Then the system fuzzy control process is determined. According to the moisture value collected by the soil moisture sensor, the average is taken, and the deviation and deviation change rate is obtained by calculating the difference with the set value, and the control value is obtained by querying the previously deposited fuzzy control table through a series of transformations, and the command is executed according to the program setting. The automatic irrigation requirement is achieved by controlling the solenoid valve on and off. Finally, the analysis of system functions in terms of sensor accuracy, system function testing, and temperature and air humidity prediction accuracy was carried out. Regarding sensor accuracy, the error of relative soil moisture content is within 3%, and the relative error of soil temperature is within 0.5. In the system control function test, the solenoid valve will open for irrigation when the soil relative water content is lower than 40% or higher than 70% of the warning range, and the lighting will be turned on when the light intensity is lower than 200 Lux, and the system control is in line with the expectation. The intelligent irrigation system is of great significance to agricultural development.

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MBD of grey prediction fuzzy-PID irrigation control technology

Cited by 8 publications

References 12 publications

An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer

An Innovative System of Deep In Situ Environment Reconstruction and Core Transfer

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Construction of an intelligent agricultural irrigation control platform based on PLC technology

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