## 7 Conclusions  
To enhance energy efficiency and production output, this study proposes a multi-motor power distribution strategy under light-load conditions. Compared to existing research, the main contribution of this study is the introduction of a multi-motor power distribution strategy based on fuzzy control and Radial Basis Function (RBF) neural networks. The method proposed in reference [26] focuses on optimizing single-motor drive systems for green operation of belt conveyors through speed control. In [27], the authors explore control strategies for distributed permanent magnet direct drive systems but do not delve deeply into power distribution among multiple motors. In contrast, the proposed strategy in this study not only considers multi-motor synchronous speed control but also optimizes powerdistribution through fuzzy control and neural networks, achieving higher energy efficiency and stability. The main conclusions are as follows:  
1. Simulation Validation: MATLAB/Simulink simulations validated the accurate control of motor startup and operation through neural networks, enabling automatic adjustment of motor positions for varying load conditions. While ensuring sufficient power for the conveyor belt, energy savings are achieved by deactivating some motors. The specific power curve adjustments help reduce operational impacts on the conveyor system, maintaining normal motor operation. 2. Load Matching and Cost Savings: The conveyor belt's output power is matched to the load by adjusting the number of motors according to different transportation distances, thereby reducing operational costs. For light-load operation, further exploration of motor and roller interchangeability could enhance motor utilization and simplify maintenance. 3. Large-Scale Industrialization Prospects: Integrating distributed permanent magnet direct drive technology into belt conveyors improves the intelligence of the system while reducing energy consumption, offering economic benefits. Preliminary cost-benefit analysis and market demand surveys indicate that the investment payback period for this technology is within five years, making it attractive. However, transitioning from a laboratory environment to actual industrial applications requires addressing challenges such as equipment standardization, system integration, and long-term reliability testing. Specific needs for different industrial applications may necessitate further customization and optimization of control algorithms to ensure optimal performance. Despite the positive results in simulations and experiments, the study's multi-motor power distribution strategy has the following limitations:  
1. Experimental Conditions: The experiments were conducted in relatively ideal and controlled environments, whereas real-world applications may encounter more complex working conditions and external factors (e.g., temperature, humidity, dust) that could affect system performance and stability. 2. Algorithm Complexity: The use of fuzzy control and RBF neural networks showed good results in small-scale systems, but further optimization may be necessary for large-scale applications to ensure real-time performance and stability. The computational load and response speed of these algorithms could become bottlenecks, especially when handling large volumes of data and complex scenarios. 3. System Integration: Implementing the distributed permanent magnet direct drive belt conveyor system in real-world applications requires integration with existing industrial equipment and control systems. This may involve issues related to hardware compatibility, system stability, and maintenance costs, necessitating further research and testing. In summary, despite facing a series of challenges, the industrial prospects of this technology remain promising, especially in modern manufacturing and processing industries that seek high efficiency and low operational costs.