摘要
随着工业4.0的推进和智能制造技术的发展，智能控制算法在复杂制造系统中的应用日益重要，其对提升生产效率、优化资源配置及实现智能化决策具有关键作用本研究以深度学习为核心技术手段，针对智能制造系统中多变量、非线性、不确定性的控制难题展开深入探讨旨在设计一种高效、自适应的智能控制算法，以满足复杂制造场景下的动态需求研究中提出了一种基于深度神经网络与强化学习相结合的混合控制框架，通过构建多层次特征提取模型和策略优化机制，实现了对制造过程中复杂工况的精准建模与实时调控实验结果表明，该算法在面对不确定性干扰时表现出显著的鲁棒性和适应能力，相较于传统控制方法，其控制精度提升了25%，响应时间缩短了30%此外，本研究还创新性地引入了迁移学习思想，使得算法能够快速适配不同类型的制造任务，从而大幅降低了调试成本和时间总体而言，本研究不仅为智能制造系统的智能控制提供了新的理论支持和技术路径，还为未来相关领域的研究奠定了坚实基础
关键词：智能控制算法；深度学习；强化学习；智能制造系统；迁移学习
Abstract
With the advancement of Industry 4.0 and the development of smart manufacturing technologies, the application of intelligent control algorithms in complex manufacturing systems has become increasingly significant, playing a crucial role in enhancing production efficiency, optimizing resource allocation, and enabling intelligent decision-making. This study focuses on deep learning as the core technical approach to address the challenging issues of multi-variable, nonlinear, and uncertain control in smart manufacturing systems, aiming to design an efficient and adaptive intelligent control algorithm that satisfies the dynamic requirements of complex manufacturing scenarios. A hybrid control fr amework combining deep neural networks and reinforcement learning is proposed, which constructs multi-level feature extraction models and strategy optimization mechanisms to achieve precise modeling and real-time regulation of complex conditions during the manufacturing process. Experimental results demonstrate that the algorithm exhibits remarkable robustness and adaptability when facing uncertain disturbances, with a 25% improvement in control accuracy and a 30% reduction in response time compared to traditional control methods. Furthermore, this study innovatively incorporates transfer learning concepts, enabling the algorithm to rapidly adapt to different types of manufacturing tasks, thereby significantly reducing debugging costs and time. Overall, this research not only provides new theoretical support and technical pathways for intelligent control in smart manufacturing systems but also lays a solid foundation for future studies in related fields.
Keywords：Intelligent Control Algorithm; Deep Learning; Reinforcement Learning; Intelligent Manufacturing System; Transfer Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法概述 2
二、深度学习在智能制造中的应用基础 2
(一) 深度学习技术原理概述 2
(二) 智能制造系统的关键需求 3
(三) 深度学习与智能制造的结合点 3
三、智能控制算法的设计与优化 4
(一) 控制算法的基本框架构建 4
(二) 基于深度学习的算法改进策略 4
(三) 控制算法性能评估指标体系 5
四、实验验证与结果分析 6
(一) 实验环境与数据集设计 6
(二) 控制算法的实际应用案例 6
(三) 实验结果对比与分析 7
结 论 8
参考文献 9
