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In some industries with heavy loads, chain conveyor belts are often used; their introduction has greatly improved the situation where manual labor was previously required to carry out such tasks, which is why chain conveyor belts have been widely adopted. However, in some situations, the length of chain conveyor systems is often limited, which requires segmented control; this in turn necessitates effective control of the connections between each segment. There are usually the following types of control methods, which I would like to share with you today: (1) Belt-type speed control. A chain plate conveyor is driven by an electric motor. In a modular chain plate conveyor, all moving parts such as rollers, idler rollers, and bearings generate resistance during operation. The operating resistance of combined chain conveyor systems can be divided into four categories: basic resistance (resistance in the intermediate section), inclination resistance, additional resistance, and special resistance. If the speed of a chain conveyor increases, the power required to overcome resistance also rises accordingly. By reducing the speed, the power consumption of the chain conveyor can be decreased, thereby achieving significant energy savings. However, the reduction in conveyor belt speed is conditional and is subject to the constraints imposed by the conveying volume, belt width, and belt strength. As the belt speed decreases, the linear density of the material will increase, and the tension required in the conveyor belt will also rise. To ensure the proper operation of the chain conveyor, the tension of the conveyor belt should be kept within the range permitted by the safety factor. Therefore, to reduce energy consumption, we should fully utilize the potential of bandwidth and data rate; while ensuring sufficient bandwidth and data rate, we should opt for a lower speed in order to save energy. (2) Apply a trance controller to save energy. To meet the requirements of smooth operation, speed, and energy efficiency for belt conveyors, selecting the right controller is crucial. The issue of energy conservation in chain conveyor systems is essentially the problem of achieving an appropriate balance between the conveyor belt speed and the transport capacity, which is in other words, the issue of speed control for chain conveyors. Conveyor belts for transporting viscoelastic materials that use variable-frequency speed control devices in combination with squirrel-cage motors exhibit severe delay characteristics, which become more pronounced as the conveying distance and volume increase. Since conveyor belts have many rotating components (idlers, drums), differences in manufacturing, installation, and maintenance, along with random variations in the conveying volume, all lead to significant random disturbances. Therefore, there is a great degree of uncertainty, that is, variability, regarding the relationship between parameters such as the belt speed of a combined chain conveyor and the input frequency of the inverter. The trance control system does not rely on an accurate mathematical model of the system, making it particularly suitable for complex systems ； It is suitable for controlling nonlinear, time-varying, and lagging systems; in short, it is an intelligent control system that combines intelligence and self-learning capabilities. The essence of trance control is to convert the control strategies of expert knowledge into automatic control strategies, based on the principle that trance entails conceptual and composite reasoning rules. Typically, it uses the deviation of the output variable of the controlled object and the rate of change of this deviation as its input variables, while setting the controlled quantity as the output variable of the fuzzy controller; this reflects the fuzzy quantitative relationship between the input and output linguistic variables and the linguistic control rules, as well as its algorithmic structure. In practical applications, the collected control information is subjected to fuzzy inference and fuzzy decision-making using language-based control rules to determine the fuzzy set of control values; thereafter, a precise value for output control is obtained through fuzzy judgment, which is then applied to the controlled object so that the controlled process achieves the desired control effect. Experience has shown that for some complex systems, especially those with uncertain, imprecise, and ambiguous information, trance control often yields better results than conventional control.