Nonlinear compensation of optimal proportional servo valve

As the core control component of hydraulic servo system, proportional servo valve is widely used in industrial automation, aerospace, construction machinery and other fields. Its control accuracy and response speed directly affect the overall performance of the system. However, in practical application, due to the influence of structural design, manufacturing errors and working environment, proportional servo valves often show certain nonlinear characteristics, such as dead zone, hysteresis and saturation, which will significantly reduce the dynamic response and control accuracy of the system. Therefore, how to effectively compensate the nonlinearity of proportional servo valve has become a key problem to improve the performance of the system.

Traditional proportional servo valve control mostly adopts linear PID control strategy, however, this method is often difficult to meet the requirements of high precision control when facing the system with strong nonlinearity. Therefore, in recent years, researchers have proposed a variety of nonlinear compensation methods, such as inverse model compensation, adaptive control, neural network compensation and fuzzy control. Among them, the optimal nonlinear compensation method based on inverse model has attracted wide attention because of its rigorous theory and remarkable compensation effect.

The core of nonlinear compensation of optimal proportional servo valve lies in establishing accurate nonlinear mathematical model and designing corresponding inverse model as feedforward compensator. The basic idea of this method is to obtain the nonlinear input-output relationship of the proportional servo valve through identification or modeling, construct its inverse model, and connect it in series between the controller and the actuator, so as to preprocess the control signal before input, so as to offset or weaken the influence of the original nonlinear characteristics. This compensation method can effectively improve the linearity and response speed of the system without changing the control structure of the original system.
　　he key to achieve the best nonlinear compensation lies in accurate modeling. At present, the commonly used modeling methods include analytical modeling based on physical principles, black-box modeling driven by experimental data and grey-box modeling combining their advantages. For the complex nonlinear proportional servo valve system, neural network modeling has become one of the modeling tools because of its strong nonlinear approximation ability. By training the input and output data of the neural network learning system, high-precision forward model and its inverse model can be obtained, so as to realize effective nonlinear compensation.
　　n addition, in order to enhance the robustness and adaptability of the system, some studies introduce adaptive mechanism into the compensation system, so that the compensator can adjust its parameters online to adapt to the changes of working conditions. This method shows good stability and control accuracy under complex working conditions such as dynamic load and temperature fluctuation.
　　To sum up, nonlinear compensation for proportional servo valve is an important way to improve the performance of hydraulic servo control system. With the development of modeling and control theory, especially the application of intelligent control and advanced identification technology, the nonlinear compensation method of optimal proportional servo valve will play an increasingly important role in engineering practice. Future research directions include the development of more efficient modeling algorithm, the realization of real-time online compensation system and the integration and optimization of various compensation strategies to meet the increasingly complex and high-precision control requirements.