As a key actuator in hydraulic system, servo valve is widely used in high-precision control occasions, such as aerospace, robotics, CNC machine tools and other fields. With the development of artificial intelligence technology, neural network control method shows good adaptability and robustness in servo valve control, especially in dealing with nonlinear, time-varying and uncertain systems.
The core of neural network control lies in its training process. For servo valve control system, the training of neural network is usually divided into two stages: offline training and online training. Firstly, in the off-line stage, the neural network is preliminarily trained by using the existing servo valve system data (such as input voltage, output flow, pressure change, load change, etc.). These data can be obtained through experiments or simulations. In the training process, the supervised learning method is adopted, and the error between the expected output and the actual output is used as feedback. The back propagation algorithm (BP algorithm) is used to continuously adjust the weights and bias values of the network to minimize the error function.
However, the servo valve system is dynamic and time-varying, and it is often difficult to meet the actual control requirements only by off-line training. Therefore, online training has become the key link of neural network control. On-line training refers to collecting input and output data in real time during the system operation and continuously optimizing the neural network model. This process is usually combined with reinforcement learning or adaptive learning strategy, so that the controller can automatically adjust the control strategy according to environmental changes and system state. For example, the combination of model-based predictive control (MPC) and neural network can dynamically adjust the control signal on the basis of predicting the future state of the system, thus improving the control accuracy and response speed.
In addition, in order to improve the training efficiency and control performance, various optimization methods can be introduced. For example:
1. Data preprocessing: the original data is normalized to eliminate dimensional differences;
2. Optimization of network structure: Convolutional neural network (CNN), recurrent neural network (RNN) or short-term memory network (LSTM) are adopted to adapt to the dynamic characteristics of the servo valve;
3. Hybrid control strategy: combine neural network with traditional methods such as PID control and fuzzy control to give full play to their respective advantages;
4. Transfer learning: use the training results of the existing model to accelerate the training process of the new system.
To sum up, the effective training of servo valve neural network control depends on high-quality data, reasonable network structure, flexible learning strategy and feedback mechanism of the actual system. By continuously optimizing the training process, the dynamic response performance and robustness of the servo valve control system can be significantly improved, which provides strong technical support for modern industrial automation.