摘 要
随着现代工业自动化水平的不断提高，机械电子系统在各个领域得到广泛应用，其复杂性和不确定性也日益增加，传统控制算法难以满足高性能、高精度的要求，自适应控制算法凭借其能够实时调整控制参数以应对系统参数变化和外部扰动的优势成为研究热点。本文旨在深入研究机械电子系统中的自适应控制算法，通过理论分析与实验验证相结合的方法，构建适用于不同类型机械电子系统的自适应控制模型。基于Lyapunov稳定性理论设计了自适应控制器，采用神经网络对系统未知动态特性进行逼近补偿，利用遗传算法优化控制器参数。结果表明，所提出的自适应控制算法使机械电子系统具有更好的动态响应性能，在跟踪精度方面较传统PID控制提高了约30%，抗干扰能力显著增强，能有效抑制10% - 20%幅度的随机扰动。
关键词：自适应控制算法 机械电子系统 神经网络
Abstract
With the continuous improvement of modern industrial automation level, mechanical and electronic system is widely used in various fields, its complexity and uncertainty is increasing, the traditional control algorithm is difficult to meet the requirements of high performance, high precision, adaptive control algorithm with its real-time adjustment control parameters to cope with the system parameter changes and the advantages of external disturbance become a research hotspot. This paper aims to thoroughly study adaptive control algorithms in mechanoelectronic systems and construct adaptive control models suitable for different types of mechanoelectron systems by combining theoretical analysis and experimental verification. The adaptive controller is designed based on the Lyapunov stability theory, using the neural network to compensate the unknown dynamic characteristics of the system, and optimizing the controller parameters using the genetic algorithm. The results show that the proposed adaptive control algorithm makes the mechanoelectronic system with better dynamic response performance, about 30% more tracking accuracy than the traditional PID control, significantly enhanced anti-interference ability, and can effectively suppress random disturbances of 10% -20% amplitude.
Keyword:Adaptive Control Algorithm Mechatronic System Neural Network
目 录
1绪论 1
1.1机械电子系统自适应控制的研究背景 1
1.2国内外研究现状综述 1
1.3研究方法与技术路线 2
2自适应控制算法理论基础 2
2.1自适应控制基本概念 2
2.2常见自适应控制算法 3
2.3算法性能评价指标 3
3机械电子系统建模与分析 4
3.1系统数学模型建立 4
3.2动态特性分析 5
3.3模型验证与误差分析 5
4自适应控制算法在机械电子系统中的应用 6
4.1控制算法设计与实现 6
4.2实验平台搭建与测试 7
4.3应用效果评估与优化 7
结论 8
参考文献 9
致谢 10
