Abstract

With the continuous expansion of the application range of optoelectronic sensors, there is a need for improvement in the control strategies for optoelectronic sensor tracking systems. In this study, fuzzy logic control algorithms and neural network algorithms are employed, along with the integration of various control strategies, to improve the control performance of the tracking systems. The fuzzy logic control algorithm utilizes fuzzy sets and fuzzy inference to handle complex inputs with uncertainty, thereby optimizing the system's responsiveness. On the other hand, the neural network algorithm enhances the performance and adaptability of the control system by training models and learning from data. Additionally, this paper provides insights into future development trends, including the application of intelligent technologies, the development of new sensors, and system integration and optimization. These improvements and trends will contribute to breakthroughs and advancements in control strategies for tracking systems, thereby promoting the application and progress of optoelectronic sensor technology.