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. 2024 Jul 3;14(1):15332.
doi: 10.1038/s41598-024-64262-7.

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

Jianying Li  1   2 , Weidong Li  3 , Xiaoyan Du  3
Affiliations

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

Jianying Li et al. Sci Rep. .

Abstract

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

Keywords: Electro-hydraulic position servo system; Fuzzy rules; Load stiffness; PID parameter learning rate; Phase lag; RBF neural network.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Four-way valve control hydraulic cylinder schematic diagram.
Figure 2
Figure 2
Block diagram of displacement output of valve-controlled hydraulic cylinder.
Figure 3
Figure 3
Open loop Bode plot: (a) System open-loop Bode diagram; (b) PID control of open loop Bode diagram.
Figure 4
Figure 4
Response curve of load state.
Figure 5
Figure 5
The response curve of different load stiffness: (a) 1.5 × 109 N/m; (b) 3 × 109 N/m; (c) 6 × 109 N/m; (d) 9 × 109 N/m.
Figure 6
Figure 6
Response curve of different frequencies: (a) 2 HZ; (b) 4 HZ; (c) 6 HZ; (d) 8 HZ; (e) 10 HZ.
Figure 7
Figure 7
Structure diagram of RBF neural network.
Figure 8
Figure 8
FUZZY-RBF-PID controller structure diagram.
Figure 9
Figure 9
Membership function of αP.
Figure 10
Figure 10
Simulink control diagram.
Figure 11
Figure 11
System no-load response curve.
Figure 12
Figure 12
System load response curve.
Figure 13
Figure 13
Response curve with load stiffness of 9 × 1010 N/m.
Figure 14
Figure 14
system response curve with 1HZ and 10HZ.
See this image and copyright information in PMC

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