Study on Fuzzy PID Control in Double Closed-Loop DC Speed Regulation System

Yang, Hongjuan; Dong, Decun; Ren, Youjun; Xu, Jing; Yang, Siyuan

doi:10.1109/icmtma.2011.687

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…The fuzzy language variables are NB, NM, NS, ZO, PS, PM, and PB, respectively. It means negative large, negative medium, negative small, zero, positive small, positive medium, and positive large [36,37] . The fuzzy PID control block diagram is shown in Figure 4.…”

Section: Fertilizer Drive Unitmentioning

confidence: 99%

Development of the peanut precision fertilization control system based on threshold velocity algorithm

Yang

Shang

et al. 2022

International Journal of Agricultural and Biological Engineering

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In view of the fact that the current ground wheel velocimetry of the peanut precision fertilizer control system cannot solve the phenomenon of ground wheel slippage, and signal interference and delay loss cannot be excluded by BeiDou positioning velocimetry, a set of peanut precision fertilizer control system was designed based on the threshold speed algorithm. The system used STM32F103ZET6 microcontroller as the main controller, and touch screen for setting the operating parameters such as operating width, fertilizer type, and fertilizer application amount. The threshold speed algorithm combined with BeiDou and ground wheel velocimetry was adopted to obtain the forward speed of the tractor and adjust the speed of the DC drive motor of the fertilizer applicator in real time to achieve precise fertilizer application. First, through the threshold speed algorithm test, the optimal value of the length N of the ground wheel speed measurement queue was determined as 3, and the threshold of the speed variation coefficient was set to 4.6%. Then, the response performance of the threshold speed algorithm was verified by comparative test with different fertilization amounts (40 kg/hm 2 , 50 kg/hm 2 , 60 kg/hm 2 , 70 kg/hm 2 ) under two speed acquisition methods of ground wheel speed measurement and threshold speed algorithm (combination of Beidou single-point speed and ground wheel speed measurement) in different operation speeds (3 km/h, 4 km/h, 5 km/h). The response performance test results showed that the average value of the velocimetry delay distance of the BeiDou single-point positioning velocimetry method was 0.58 m, while the average value of that with the threshold velocity algorithm was 0.27 m, which decreased by 0.31 m and indicated more accurate with the threshold velocity algorithm. The field comparison test for fertilizer application performance turned out an over 96.08% accuracy rate of fertilizer discharge by applied with the threshold speed algorithm, which effectively avoided the inaccurate fertilizer application caused by wheel slippage and raised the accuracy of fertilizer discharge by at least 1.2% compared with that of using the ground wheel velocimetry alone. The results showed that the threshold speed algorithm can meet the requirements of precise fertilizer application.

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Section: Fertilizer Drive Unitmentioning

confidence: 99%

Development of the peanut precision fertilization control system based on threshold velocity algorithm

Yang

Shang

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…PID control is a classic robust control method in process control, which realizes closed-loop stable control through proportional, integral and derivative calculations of system input and output deviations [15]. Its Laplace transform mathematical expression is (3) In the formula, Kp, Ki (Ti), Kd (Td) respectively represent the proportional, integral, and differential coefficients. In order to quickly and effectively determine the three parameters, the "PIDTuner" tool that comes with Matlab is used to check the dynamic and static indicators of the system in real time through the step response, and determine the optimal Kp, Ki, and Kd parameters online.…”

Section: Pid Control Algorithm Parametersmentioning

confidence: 99%

“…Because the incremental PID control algorithm is only related to this deviation, it is not as easy to produce cumulative error as the positioned PID algorithm, and the amount of calculation is less, so the closed-loop feedback of the Buck circuit mainly adopts the incremental PID control algorithm [3][4].…”

Section: Introductionmentioning

confidence: 99%

Adjustable Voltage-stabilized Power Supply System based on Single Chip Microcomputer

Yan¹,

Hu²

2020

2020 4th International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCIA 2020)

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“…Fuzzification is an accurate numerical classification, which sets a fuzzy set corresponding to each file. The subsets of fuzzy languages are selected as negative big, negative middle, negative small, zero, positive small, positive middle, and positive big and also expressed as NB, NM, NS, ZE, PS, PM, PB , respectively [33]. The input universes are taken as −6, −5, −4, −3, −2, −1, 0, 1, 2, 3, 4, 5, 6 .…”

Section: Fuzzy/pid Compound Controller Based On Variablementioning

confidence: 99%

An Improved Fuzzy Neural Network Compound Control Scheme for Inertially Stabilized Platform for Aerial Remote Sensing Applications

Zhou

Yuan

et al. 2018

International Journal of Aerospace Engineering

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An improved fuzzy neural network (FNN)/proportion integration differentiation (PID) compound control scheme based on variable universe and back-propagation (BP) algorithms is proposed to improve the ability of disturbance rejection of a three-axis inertially stabilized platform (ISP) for aerial remote sensing applications. In the design of improved FNN/PID compound controller, the variable universe method is firstly used for the design of the fuzzy/PID compound controller; then, the BP algorithm is utilized to finely tune the controller parameters online. In this way, the desired performances with good ability of disturbance rejection and high stabilization accuracy are obtained for the aerial ISP. The simulations and experiments are, respectively, carried out to validate the improved FNN/PID compound control method. The results show that the improved FNN/PID compound control scheme has the excellent capability in disturbance rejection, by which the ISP’s stabilization accuracy under dynamic disturbance is improved significantly.

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Study on Fuzzy PID Control in Double Closed-Loop DC Speed Regulation System

Cited by 14 publications

References 7 publications

Development of the peanut precision fertilization control system based on threshold velocity algorithm

Development of the peanut precision fertilization control system based on threshold velocity algorithm

Adjustable Voltage-stabilized Power Supply System based on Single Chip Microcomputer

An Improved Fuzzy Neural Network Compound Control Scheme for Inertially Stabilized Platform for Aerial Remote Sensing Applications

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