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In the development of industrial control processes in the modern and contemporary era, many conveying equipment control processes cannot be fully automated. The difficulty lies in the fact that it is impossible to establish a process model for these complex systems of belt conveyors; or even if such a model can be created through certain simplifications, it remains too complex to be solved within reasonable time frames, making real-time control impossible. Although a belt conveyor system identification method can be used, the time required for numerous tests, the analysis involved, and changes in test conditions all make it impossible to establish an accurate model. A variable-speed hydraulic coupling is a nonlinear system, and it is quite difficult to establish an accurate mathematical model for belt conveyors; assumptions, approximations, omissions, and simplifications are made in the creation of mathematical models for various components of this system. Thus, the derived transfer function must differ from what is observed in practice, and this system is one that exhibits time variability, lag, and saturation. Therefore, the classical control theory approach is adopted to study this system. It can only serve as a reference and for comparison. For such belt conveyor systems, even by using computer simulation and modern control theory methods, the conclusions obtained cannot be used as guidelines due to the difficulty in accurately determining the parameters. It can only serve as a reference for further discussion, as this system has a small number of inputs and outputs; it can even be simplified to a single-input, single-output control system. There is no need to use modern control theory methods such as multivariable control or complex process control. Based on the experience of many field workers, it is also clear that, following theoretical approaches, numerous adjustments must be made in practical application, especially in terms of software programming, where repeated testing is required. Summarizing the process analyzed above, it can be seen that there is considerable ambiguity regarding the relationship between the movement of the shovel arm in the variable-speed hydraulic coupling of belt conveyors and the amount of fluid filled, as well as between the circulation flow rate and the output torque and speed. The entire process is characterized by non-linearity, time variability, significant delays, random disturbances, and potential difficulties in measurement. This makes it very difficult to establish an accurate mathematical model for the belt conveyor’s operation process. To this end, if we imagine using humans to replace automatic control, that is, studying it through vague control, we might achieve better results. Belt conveyor control establishes a control relationship between the control variable and the error and rate of change between the output and the set value. Based on human experience, control rules are formulated to regulate the belt conveyor system. The advantages of this control method are as follows: 1. Belt conveyor control technology does not require an accurate model of the process, and its structure is relatively simple. When designing controllers, only experience and operational data in this area are needed; control rules can be easily established based on qualitative understanding and experiments related to the industrial process. 2. The belt conveyor control system falls within the field of intelligent control; it is capable of replicating more closely the control behavior of an optimal operator, boasts high control stability, and is particularly suitable for controlling systems that are nonlinear, time-varying, and subject to frequent external disturbances as well as strong internal correlations. 3. Vagueness can help address the issue of the belt conveyor control system facing significant changes due to working conditions during underground coal mining operations (load), or experiencing frequent variations in transport capacity as a result of disturbances, thereby making the control process rather complex. 4. The control system can enable self-learning* and self-calibration to adjust the belt conveyor ； Together, other modern control methods, such as expert systems, can also be utilized to further optimize the calculations. 5. Numerous practical experiences have shown that a well-designed control system responds more rapidly, possesses good static and dynamic stability, and can achieve satisfactory control performance for belt conveyors.