As the core control component in hydraulic system, servo valve plays a vital role in aerospace, industrial automation, robotics and other fields. In order to control the servo system accurately, it is necessary to identify its key parameters accurately. Parameter identification of servo valve refers to determining the dynamic and static characteristic parameters of servo valve through experimental data and mathematical modeling, thus providing basis for system modeling, controller design and fault diagnosis.
First, the basic structure and working principle of servo valve
Servo valve usually consists of torque motor (or electromagnetic coil), nozzle baffle mechanism, main valve core and feedback mechanism. Its working principle is to adjust the flow and direction of hydraulic oil according to the change of input electrical signal, so as to control the movement of actuator. The dynamic response characteristics, flow gain, pressure gain, bandwidth, dead zone and other parameters of the servo valve are the key factors affecting the performance of the whole hydraulic system.
Second, the significance of parameter identification
In the design and control of servo system, it is often difficult to meet the demand of high precision control if only relying on theoretical calculation or parameter values provided by manufacturers. Due to the influence of manufacturing tolerance, material aging, temperature change and other factors, the actual parameters of servo valve will deviate from the theoretical value. Through parameter identification technology, we can obtain more realistic model parameters, thus improving the control accuracy and stability of the system.
Third, the method of servo valve parameter identification
Parameter identification of servo valve is usually divided into static identification and dynamic identification:
# 1. Static parameter identification
Static identification is mainly aimed at the flow-displacement characteristics and pressure-displacement characteristics of servo valves. By inputting different control currents to the servo valve, the corresponding output flow or pressure is measured, the static characteristic curve is drawn, and the parameters such as flow gain, dead zone and nonlinearity are calculated accordingly. Static identification is generally completed under low frequency or steady state conditions, which is suitable for analyzing the open-loop characteristics of servo valves.
# 2. Dynamic parameter identification
Dynamic identification focuses on the behavior of servo valve in the process of transient response, such as step response and frequency response. Commonly used dynamic identification methods include:
Frequency domain method: By applying sinusoidal signals with different frequencies to the servo valve, the output response is measured, the Bode diagram is drawn and the transfer function of the system is fitted, and then the parameters such as bandwidth, damping ratio and natural frequency are identified.
-Time domain method: using step or pulse excitation signal, the output response curve of the system is recorded, and the parameters are estimated by combining the least square method, recursive least square method (RLS) or neural network.
In addition, with the development of modern control theory, system identification software tools (such as MATLAB system identification toolbox) and artificial intelligence algorithms (such as genetic algorithm and particle swarm optimization algorithm) are also widely used in servo valve parameter identification, which improves the accuracy and efficiency of identification.
IV. Matters needing attention in the identification process
During parameter identification of servo valve, the following points should be noted:
1. The experimental conditions should be as close as possible to the actual working environment to reduce the influence of environmental factors on the identification results;
2. The input excitation signal should have sufficient bandwidth and amplitude to excite the dynamic characteristics of the system;
3. The data acquisition system should have high accuracy and sampling frequency;
4. The choice of model structure should be reasonable to avoid over-fitting or under-fitting.
V. Conclusion
To sum up, parameter identification of servo valve is the key link to realize high-precision hydraulic servo control. Through scientific experimental design and advanced identification methods, the modeling accuracy and control performance of the servo system can be effectively improved. With the development of intelligent control and big data analysis technology, servo valve parameter identification will develop in the direction of higher precision, stronger adaptability and more intelligence, which will provide strong support for the optimal design and real-time control of complex hydraulic system.