[ Abstract ] This article reviews the main methods of motion control and, considering the characteristics of water supply systems and the problems existing in control, selects the optimal control strategy for water supply pumping stations. I. Introduction With the continuous improvement of automation, motion control systems can employ complex algorithms that were previously difficult to implement, and control performance has also greatly improved. The intelligence of controllers in motion control systems provides effective theories and methods for solving the control of complex systems that are difficult to solve using traditional methods. Mature motion control methods include: PID control algorithm, artificial neural network control, fuzzy control, expert control, and humanoid intelligent control. II. Main Methods of Motion Control 1. PID Control. PID control is one of the earliest developed control methods and is still widely used. It is a method based on the mathematical model of the object, especially suitable for deterministic control systems where accurate mathematical models can be established. However, for nonlinear, time-varying, and uncertain systems, it is difficult to achieve ideal control effects using conventional PID controllers. Moreover, in actual production, due to the complexity of parameter tuning methods, conventional PID parameters are often poorly tuned and have poor performance. 2. Artificial Neural Network Control. Artificial neural networks originated in the 1940s. While they reflect some basic characteristics of the human brain, they are not a true description of it, but rather an abstraction, simplification, and simulation. Information processing in the network is achieved through interactions between neurons. The key to neural network control is selecting a suitable neural network model and training and learning it until the desired structure and weights are achieved. However, neural network learning requires experimental samples, which must be obtained from known experience and prior experiments. Furthermore, the training and learning process of neural networks is sometimes complex, requiring thousands of iterations to obtain the optimal structure. Sometimes, a local optimum is obtained instead of a global optimum, and due to the limitations of the method, effective control of the object under discussion is difficult. 3. Fuzzy Control. In practical engineering, a highly skilled operator can achieve satisfactory control results by judging various phenomena on-site based on their rich practical experience. If the measures taken based on experience are transformed into corresponding control rules, and a controller is developed to replace these rules, control of complex industrial processes can also be achieved. Practice has proven that fuzzy controllers (FC), based on fuzzy control theory, can accomplish this task. Fuzzy control is a type of control based on fuzzy reasoning and imitating human thinking methods, implemented for objects that are difficult to model mathematically. It uses fuzzy sets from fuzzy mathematics to characterize these fuzzy languages ​​and employs production rules, i.e., "execute if the condition is true." The application of fuzzy control technology has achieved significant results in China. 4. Expert Control. Expert control is an important part of intelligent control. It organically combines the theory and technology of expert systems with the theory and methods of control theory, imitating the intelligence of experts in unknown environments to achieve effective control of the system. The core of expert control is the expert system, which has the ability to handle various unstructured problems, especially qualitative, heuristic, or uncertain knowledge information, achieving the system's control objectives through various reasoning processes. 5. Humanoid Intelligent Control. Humanoid Intelligent Control (HSIC) has, after 20 years of effort, formed a basic theoretical system and a relatively systematic design method, and has achieved success in numerous practical applications. Its main content is to summarize human control experience, imitate human control thinking and behavior, and use production rules to describe its heuristic and intuitive reasoning behavior in control. Because the fundamental characteristic of HSIC is to mimic the control behavior of control experts, its control algorithm is multimodal control, involving the alternating use of multiple modal controls. This algorithm can perfectly coordinate many conflicting control quality requirements in a control system, such as robustness and accuracy, speed and stability. III. Characteristics and Control Requirements of Water Supply Pumping Stations In the process of urban development, intelligent buildings have become a standard for people seeking good living conditions. Water supply pumping stations are an indispensable part of intelligent building complexes. Reasonably selecting the control method for water pumps can not only reduce project costs but also save energy. 1. Characteristics of Water Supply Systems. For specific users, the most prominent characteristic of water usage is randomness; which user uses water, how much water uses, and when water is used all have significant uncertainties. From a macroscopic perspective, the characteristics of a water supply system are mainly manifested in the following aspects: (1) the unknown, time-varying, random, and dispersed nature of system parameters; (2) the unknown and time-varying nature of system lag; (3) severe nonlinearity of the system; (4) the correlation between various variables in the system; (5) the unknown, diverse, and random nature of environmental disturbances. 2. Problems in control. The above characteristics belong to the control problems of complex objects (or processes) with uncertainty, which traditional control is powerless to solve. The main manifestations are: (1) uncertainty problems. Many control problems in water supply systems have uncertainty, which is difficult to model using traditional methods, and therefore cannot achieve effective control. (2) high nonlinearity. There are a large number of nonlinear problems in water supply systems. In traditional control theory, nonlinear theory is far less mature than linear theory, and is difficult to apply due to excessive complexity. (3) semi-structured and unstructured problems. Traditional control theory cannot solve the semi-structured and unstructured problems in water supply systems. (4) water supply system complexity problems. In complex systems, the relationships between subsystems are intricate, the elements are highly coupled and mutually restrictive, and the external environment is extremely complex. Traditional control lacks effective solutions. (5) Reliability issues. Conventional control problems based on mathematical models tend to be interdependent wholes, and the reliability issues of control for simple systems are not prominent. However, for water supply systems, if the above methods are used, the entire control system may collapse due to changes in conditions (continued on page 18) (continued from page 16). It is evident that traditional methods cannot effectively control such systems, and more effective control methods must be explored. 3. Control requirements. Regardless of the control methods used, the user's water demand must be met (i.e., maintaining a certain water pressure), the environment must be protected from noise pollution, and energy conservation must also be considered. Therefore, the control requirements can be defined as minimizing environmental pollution and saving energy while meeting the user's water supply requirements. IV. Selection of control strategies The selection of control strategies is closely related to the characteristics of the controlled object. Incorrect or inappropriate control strategies often lead to extremely poor control effects or even system loss of control. Currently, frequency converters are widely used in modern water supply pumping stations for energy conservation, creating favorable conditions for improving control quality. Frequency converters generally contain PID control modules, but PID algorithms are not appropriate for complex, uncertain water supply systems. Artificial neural networks, due to their limitations, also struggle to achieve effective control of the object under consideration. Expert control systems (ECS) are not necessarily a good choice due to the difficulty in acquiring and representing feature information and establishing a complete knowledge base. Fuzzy controllers (FC), based on fuzzy control theory, can achieve control of complex industrial processes. Their control quality and effectiveness are satisfactory, making them a viable strategy. Humanoid intelligent control: Experts have conducted simulation studies on uncertain and complex objects (or processes) using HISC and FC control strategies. Although both are based on errors and error change rates to calculate control quantities, due to the complexity of the system, the numerous uncertainties, and the strong correlation (strong coupling), field tests have shown that both HISC and FC can implement effective control, but HISC offers better control quality and robustness. Therefore, using HSIC to control uncertain water supply systems is a more rational choice. V. Conclusion Intelligent control has been widely applied in various fields such as industry, agriculture, and the military, solving many practical control application problems that traditional control methods cannot address. It demonstrates strong vitality and development prospects. With the expansion of basic theoretical research and practical applications, intelligent control will achieve a major leap forward in the field of control.