Hydraulic press systems generally have high nonlinear and uncertain dynamic characteristics. Simple linear or nonlinear differential equations cannot fully express the corresponding actual systems, so the control algorithms based on accurate models are not suitable. The control algorithms commonly used in hydraulic press systems are as follows:

1. The hydraulic press system adaptive control algorithm, which is suitable for systems with certain uncertainties, can automatically adjust the model parameters according to the data characteristics in the process of analysis and processing, and finally approach the target in the best state.

2. The hydraulic press system seeks the optimal control algorithm by itself. It is suitable for the controlled system without a precise mathematical model but with nonlinear characteristics. During the control process, the real-time optimal state can be obtained through continuous measurement, understanding, calculation, and judgment.

3. The neural network control algorithm of the hydraulic press system does not require a thorough understanding of the system. Through learning and training with a certain amount of input and output samples, it can approximate any complex nonlinear mapping with extremely high precision and has a strong self-learning ability. , adaptive capability, and nonlinear mapping capability.

4. The fuzzy control algorithm of the hydraulic press systems is suitable for complex nonlinear systems with inaccurate or uncertain known information. It summarizes control rules based on theoretical and empirical analysis and then controls the system through reasonable reasoning based on the rules. It has strong robustness.