Filling Machine Control System Based on RBF Neural Network

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With the development of mechanical automation control technology, the intelligent control ability of mechanical equipment is constantly improved. In the design of the liquid filling machine, it is necessary to carry out high-precision quantitative control of the liquid filling machine. Combining the method of high-precision filling control parameter analysis and fuzzy identification, a quantitative control model of a liquid filling machine is established. By optimizing parameter simulation and spatial parameter information fusion configuration, the output stability and quantitative filling control ability of liquid filling machines are improved. The research on the quantitative control methods of high-precision filling of related liquid filling machines has attracted great attention.
 
Therefore, related scholars have studied the quantitative control method of high-precision filling of liquid filling machines, and made some progress. Yang Zhenyu, etc. designed a multi-control point high-speed brick filling machine control system. Aiming at the complex structure of the liquid filling machine, the pressure, liquid level, flow rate and temperature were controlled by multiple control points to realize the full-cycle control of the whole production process. Taking PLC as the control core, the filling machine was controlled by the control mode of upper and lower computers. Li Wenyu et al. [4] designed a full-automatic liquid filling machine control system, which controlled the liquid filling machine fully by PLC technology, controlled the accuracy of weighing instruments by networking, and combined with the AC impedance spectrum feature extraction method, distributed and controlled the dynamic reliability liquid level of the liquid filling machine. The traditional method of high-precision filling quantitative control of liquid filling machines has poor output stability and weak adaptive control ability.
 
Aiming at the above problems, this paper puts forward a high-precision quantitative control method for liquid filling machines based on the RBF neural network. Constructed the constraint parameter model of the liquid filling machine's high-precision filling control. Through the fuzzy parameter constraint method, the high-precision filling of the liquid filling machine was carried out. Combined with the spatial disturbance fusion method, the high-precision filling disturbance and feature analysis of the liquid filling machine was carried out. Through the parameter adaptive identification method, the quantitative analysis of the liquid filling machine's high-precision filling was carried out. The fitting control of the liquid filling machine's high-precision filling was carried out by using the B-spline curve fitting method. Through the adaptive parameter adjustment, the RBF neural network model was constructed, and the quantitative control law of the liquid filling machine's high-precision filling was carried out. Finally, the simulation test analysis shows the superior performance of the proposed method in improving the quantitative control ability of the high-precision filling of liquid filling machines.
 
Parametric model and feature analysis

1. Construction of transfer function of a servo-driven metering cylinder of liquid filling machine
In order to realize the quantitative control of high-precision filling of liquid filling machine based on RBF neural network, the medium attribute of high-precision filling control of the liquid filling machine is analyzed by the method of liquid level characteristic analysis, and the structure model of parameter acquisition system of high-precision filling control of the liquid filling machine is constructed. See Figure 1.
 

Fig.1 Parameter collection system for high-precision filling control of the liquid filling machine
 
According to the collection model shown in Figure 1, analyze the specific pressure value of the bottle mouth of the filling machine in the filling process [7]. The kinematic viscosity coefficient V, solution density and parameter number N of filling fluid are acquired by the acquisition system, and the kinematic viscosity coefficient V', fixed-point coordinates x, y and parameter number N' of filling fluid are output after passing through the system. By analyzing the steady-state situation of pressure sensing parameters of liquid filling machine, the steady-state parameter identification model of filling machine pressure is obtained as follows:
 

Where: s is the control area; V is the control volume; V is the kinematic viscosity coefficient of the fluid; T is time; Is the solution density; X,  Y are fixed-point coordinates of fluid; N is the number of parameters. According to the identification parameter model of high-precision filling pressure control of the liquid filling machine, i, j t is used to represent the residual vector of structural strength distribution information of the liquid filling machine, and the expression of the time interval model for calculating high-precision filling of liquid filling machine is as follows:
 
 

i, j U t represent the parameter identification model of high-precision filling quantitative control of the liquid filling machine, which is described as:
 

 
Where: R is the radius of the quantitative cylinder; H is the distance the piston moves. The distance of piston movement can be obtained by formula (4):
 

Where: f2 is the position instruction of the liquid filling machine; P is the pitch of the liquid filling machine; It is the reduction ratio of the servo motor of the liquid filling machine. According to the number of pulses sent by the liquid filling machine to the servo driver, it can be known that the filling capacity load parameter model is:

Where: f1 is the number of pulses; NM is the electronic gear ratio to the servo motor. According to the structure of the servo-driven metering cylinder, the mathematical expression of the rotation angle of the permanent magnet synchronous motor and the output rotation angle of the ball screw is obtained as follows:

Where: Ks is the torsional stiffness of the ball screw; Js is the moment of inertia of the ball screw; Fs is the viscous damping coefficient of the ball screw; S is the inductance of the stator shaft. The transfer function of a servo-driven metering cylinder of a liquid filling machine is obtained from the above-mentioned processes:
 

Where: is the rotor angular velocity of the servo-driven metering cylinder; X is the displacement of the lifting beam of the liquid filling machine. The transfer function construction of the servo-driven metering cylinder of the liquid filling machine is completed.
 
2. Feature analysis of fuzzy parameters
By analyzing the matching between the emission frequency and the resonance frequency in the resonance stage of the liquid filling machine in the high-precision filling process, and considering the uncertain factors of the mechanical property transmission of the liquid filling machine, it is obtained that under the control of the excitation pulse, the RBF neural network model is adopted, and the weighted iteration function of the corrosion dynamic characteristic distribution in the liquid filling machine is obtained as follows:

Where: *( ) j j N E t, 0≤i≤k 1，0≤ (t)≤ 1 represents the high-precision filling speed parameter of the liquid filling machine. By using multivariate state parameter analysis, the fuzzy constraint term is {Wfinal}, and the characteristic parameters controlled by the liquid filling machine are:

The characteristic distribution of high-precision filling of liquid filling machine is as follows:

Where: is the molecular diffusion volume of groups A and B; Under the same amplitude, the impedance characteristic analysis method is adopted to obtain the liquid level diffusion characteristic matrix R of high-precision filling of liquid filling machine, which is defined as:

The disturbance corrosion behavior of a liquid filling machine driven continuously is characterized by (k), and liquid filling is obtained under the constraint of the viscosity coefficient of the medium. Machine sensitivity enhancement feature distribution:

The collected quantitative control parameters of high-precision filling of the liquid filling machine are characterized, and the steady-state characteristic quantity of quantitative control of the liquid filling machine is obtained. High-precision filling disturbance and feature analysis of liquid filling machine based on spatial disturbance fusion method.
 
Control model optimization
 
1. Optimization and adjustment of control parameters of liquid filling machine
Combining the spatial disturbance fusion method to analyze the disturbance and characteristics of high-precision filling of liquid filling machine, the quantitative analysis of high-precision filling of liquid filling machine is carried out by the method of parameter adaptive identification, and the fitting control of high-precision filling of liquid filling machine is carried out by the method of B-spline curve fitting. By adjusting the adaptive parameters, the amplitude function relation is as follows:

According to the variation law of spatial disturbance fusion coefficient, the fractional adaptive extended Kalman filter method is introduced, and the output linear fractional control parameters are as follows:

Considering the disturbance of the system, the fuzzy state constraint model of adaptive control parameter identification of liquid filling machine is obtained:

Through the method of ultrasonic excitation pulse detection, the steady-state error component of high-precision filling of liquid filling machine is obtained as follows:

When carrying out high-precision filling control and optimization design of the liquid filling machine. The compound prediction model is used to identify the parameters and optimize the control of high-precision filling of liquid filling machines.
 
2. Design of high-precision filling control law for liquid filling machine
Using the method of B-spline curve fitting, the fitting control of high-precision filling of liquid filling machine is carried out, and the connecting rod parameter set of liquid filling machine is obtained. A dynamic parameter constraint model of high-precision filling of liquid filling machine is constructed between coordinate systems i 和 i−1, and the spatial dynamic parameter constraint distribution matrix can be expressed as follows:


 
In the 4×4 homogeneous coordinate system, the equilibrium point constraint characteristic quantity of high-precision filling of liquid filling machine is obtained , and the tracking impulse of a high-precision filling drive of liquid filling machine is σ 7. Considering introducing lumped interference into the liquid level control system, the fuzzy control parameters are as follows:

According to the orderly distribution of the filter, the liquid level sensing result of the high-precision filling of the liquid filling machine is obtained. Build an RBF neural network model, as shown in Figure 2. According to the RBF neural network model shown in Figure 2, the optimal design of quantitative control law for high-precision filling of liquid filling machine is carried out. Under the rigid constraint, the output of high-precision filling drive control of the liquid filling machine is obtained as follows:
 


 

Fig.2 RBF neural network model
 
Equation (19) is equivalent to the dynamic analytical model of the high-precision filling operation of the liquid filling machine. To sum up, the optimal design of quantitative control law for high-precision filling of liquid filling machines is carried out by means of parameter optimization identification and analysis.
 
Simulation analysis
 
1. Quantitative control time delay comparison
 
In order to verify the application performance of the proposed method in high-precision filling quantitative control of the liquid filling machine, a simulation test was carried out. In order to ensure the effectiveness of the experiment, the motors and related parameters of the three methods are completely consistent. The rated power of the motors is 1 kW, the rated torque is 3.14Nꞏm, the maximum torque is 9.55nm, the rated speed is 3000 r/min, and the maximum speed is 4000 r/min.
It is given that the number of nodes in the input layer of the RBF neural network is 14, the number of hidden nodes is 8, the number of nodes in the output layer is 4, and the time delay parameter of the liquid filling machine is 1.56 ms, and the fluctuation amplitude is 15 dB. According to the above parameters, the high-precision control of the liquid filling machine is carried out. Check the control time delay of the multi-control point control method, full-automatic control method and the proposed method. The results are shown in Figure 3. According to the analysis of Figure 3, with the increase in iteration times, the total control delay of each method also increases. When the number of iterations is 200, the sum of quantitative control delay of the multi-control point control method is 168 ms, that of the automatic control method is 256 ms, and that of the proposed method is 6 ms When the number of iterations is 900, the sum of quantitative control delay of multi-control point control method is 335 ms, that of automatic control method is 365 ms, and that of the proposed method is only 28 ms The sum of the quantitative control time delay of the proposed method is obviously lower than that of the other two methods, which indicates that the control efficiency of the method in this paper is higher.
 
 

Fig.3 Comparison of quantitative control delay
 
 
2. Control stability of filling machine
 
In order to further determine the control stability of the filling machine, the multi-control point control method, automatic control method and the proposed method are used to test the filling control stability of the liquid filling machine. The results are shown in Table 1. According to the analysis of Table 1, when the number of iterations is 600, the filling control stability of the multi-control point control method is 84.32%, that of the automatic control method is 82.21%, and that of the proposed method is 96.45%. When the iteration number is 1000, the filling control stability of the multi-control point control method is 81.32%, that of the automatic control method is 75.43%, and that of the proposed method is 98.82%. Compared with other methods, the filling control stability of the proposed method is higher, which indicates that the proposed method effectively improves the liquid level.
Quantitative control ability.
 
3. Filling volume control effect of liquid filling machine
In order to verify the liquid quantitative control ability, check the multi-control point control method, the full-automatic control method and the liquid volume control accuracy of the proposed method, the results will be output by sigmaplot software as shown in Figure 4. The filling capacity of the liquid filling machine is set to 100 mL. Compare the filling accuracy of filling machines under different control methods. According to the analysis of Figure 4, when the number of experiments is 10, the filling capacity of the liquid filling machine with the multi-control point control method is 93.5 mL, which is 6.5 mL different from the set capacity of 100 mL.
 
Tab.1 Stability of filling control  

Fig.4 Filling volume of the liquid filling machine under different methods
1. Multi-control point control method 2. Fully automatic control method 3. The control method in this paper
 
The filling capacity of the liquid filling machine with the automatic control method is 92.7 mL, which is 7.3 mL different from the set capacity. The filling capacity of the proposed liquid filling machine is 100 mL, and the set capacity difference is 0. When the number of experiments is 50, the filling capacity of the liquid filling machine with the multi-control point control method is 96.3 mL, which is 3.7 mL different from the set capacity of 100 mL. The filling capacity of the liquid filling machine with the automatic control method is 95.2 mL, and the difference from the set capacity is 4.8 mL. The filling capacity of the proposed liquid filling machine is 100 mL, and the set capacity difference is 0. The filling accuracy of the proposed method is much higher than that of other methods, which shows that the quantitative control effect of the proposed method is better.
 
Conclusion
 
The quantitative control model of high-precision filling of liquid filling machine is established by combining the analysis of high-precision filling control parameters and fuzzy identification method. The quantitative control method of high-precision filling of liquid filling machine based on RBF neural network is proposed in this paper. The analysis of pressure sensing characteristics of the liquid filling machine is realized by liquid level parameter identification, and the optimization of high-precision filling quantitative control of the liquid filling machine is completed by parameter optimization identification and analysis method. Through the experiment, the following conclusions are drawn.
 
1. The sum of the quantitative control delay of the proposed method is low. When the number of iterations is 900, the sum of the quantitative control delay of the proposed method is only 28 ms
 
2. The filling control stability of the proposed method is high. When the iteration number is 1000, the filling control stability of the proposed method is as high as 98.82%.
 
3. The filling accuracy of the filling machine of the proposed method is much higher than that of other methods, and the filling quantitative control effect of the filling machine is better. When the number of experiments is 50, the filling capacity of the liquid filling machine of the proposed method is 100 mL, and the set capacity difference is 0.