摘要
随着光电传感器应用范围的不断拓展，光电传感器跟踪系统的控制策略亟待改进。本研究采用模糊逻辑控制算法和神经网络算法，并结合多种控制策略，以提高跟踪系统的控制性能。模糊逻辑控制算法利用模糊集和模糊推理来处理具有不确定性的复杂输入，从而优化系统的响应速度。神经网络算法通过模型训练和数据学习，增强了控制系统的性能和适应性。此外，本文还展望了未来的发展趋势，包括智能技术的应用、新型传感器的开发以及系统集成与优化。这些改进和趋势将有助于跟踪系统控制策略的突破和发展，从而促进光电传感器技术的应用和进步。
关键词： 光电传感器；跟踪系统；控制策略
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.
Key words： Optoelectronic sensor; Tracking system; Control strategy
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