Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Zhang, Mingli; Zhang, Yijie; He, Xiaolong; Gao, Zhengjie

doi:10.3390/pr9081475

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…The BP neural network optimized by particle swarm optimization (PSO) can also effectively avoid the result falling into local optimization. Using the optimized BP neural network can improve the performance of the PID controller [24,25].…”

Section: Methods Of Simulation Testmentioning

confidence: 99%

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Fertilization Control System Research in Orchard Based on the PSO-BP-PID Control Algorithm

et al. 2022

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In order to improve the precision of the variable-rate fertilization system in orchards, this paper conducted a simulation by MATLAB and experimental research based on a variable-rate fertilization experiment platform. The variable-rate fertilization experimental platform was mainly composed of a power supply, DC motors, a PPC-15A1 on-board computer that contains a PCI8932 PC-DAQ, speed sensors, fertilizer dischargers, and a NAV60 module that can receive Beidou Navigation Satellite System positioning data. According to the fertilizer application mechanism of an external grooved wheel fertilizer applicator, the control system model of the variable-rate fertilization driven by the DC motor for orchards was established. A BP neural network adaptive PID controller based on particle swarm optimization (PSO) was proposed to improve the control precision of the system. The step response simulation results by MATLAB show that the overshoot of the BP-PID controller optimized by the PSO algorithm (PSO-BP-PID) is 12.7%, and the adjustment time is 0.557 s. The variable-rate fertilization experiments were conducted, in which the control system was tested by using the PSO-BP-PID controller. The variable fertilizer seeder control system of the Chinese national standard was adopted to evaluate the performance indexes of the system, such as the range of fertilizer amount adjustment, the response time of fertilizer amount adjustment, and the control precision of fertilizer amount. In the variable rate fertilization experiments, the average fertilization errors, respectively, are 1.16% and 1.07%, under the conditions of changing the target fertilization amount and the vehicle speed. The test results are consistent with the simulation results, and the variable-rate fertilization performance parameters are improved.

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Section: Methods Of Simulation Testmentioning

confidence: 99%

“…where δ is the control error of fertilizer discharge, %; Q 1 is the target fertilization, g/m; Q 2 is the actual amount of fertilization, g/m. The coefficient of variation is calculated according to Equation (24).…”

Section: Methods Of the Variable-rate Fertilization Testmentioning

confidence: 99%

Fertilization Control System Research in Orchard Based on the PSO-BP-PID Control Algorithm

et al. 2022

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“…The number of implicit layers in the BP neural network needs to be considered in the data prediction problem of quality in modeling process. In recent years, four-layer BP neural network is conventional in small sample prediction models due to its advantages of fast operation and high accuracy [13].…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

Wind Power Prediction Based on Difference Method

Zhang¹,

Zhang²,

Cui³

2022

Journal of Compurters

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<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the neural network and LSTM and feeding the original data and the difference-processed data into the neural network model respective-ly. Finally, the data are added for validation, and the raw data used include wind power data in Belgium from November 1, 2019 to November 30, 2019.The experimental results show that the LSTM prediction accuracy is improved by 178.67%, and is effective in predicting long-term wind power data with 216.06% accuracy improvement, the neural network prediction accuracy is improved by 154.07%, and the short-term wind power prediction accuracy is improved by 228%.</p> <p> </p>

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“…The principal reason is that it only needs three adjustable parameters, and does not require the exact information of the system [2]. Because of this, the majority of research in the field of process control has concentrated on PID control [3][4][5][6][7].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Tuning of PID/PIDD2 Controllers for Second-Order Oscillatory Systems with Time Delays

2023

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PID control is the longest history and the most vital basic control mode that has been widely applied in the production process. The oscillatory dynamics of the process have various features, and parameter tuning is complicated. To reduce the complexity of the parameter tuning process and improve the performance of the system, in this research, we propose a new tuning method for the PID/PIDD2 controllers for second-order oscillatory systems with time delays under the constraint of certain robustness. In comparison to existing PID for second-order oscillatory systems with time delays, simulation findings demonstrate that the tuning method of the proposed PID/PIDD2 controllers trades off robustness and disturbance rejection performance.

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Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Cited by 8 publications

References 23 publications

Fertilization Control System Research in Orchard Based on the PSO-BP-PID Control Algorithm

Fertilization Control System Research in Orchard Based on the PSO-BP-PID Control Algorithm

Wind Power Prediction Based on Difference Method

Tuning of PID/PIDD2 Controllers for Second-Order Oscillatory Systems with Time Delays

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