摘 要
随着智能制造技术的快速发展，机械臂作为智能装备中的核心组件，在工业生产、医疗辅助和服务机器人等领域发挥着重要作用。然而，传统机械臂在结构设计与控制策略方面存在诸多局限性，如负载能力不足、运动精度较低以及能耗较高等问题，难以满足复杂任务需求。为此，本研究以提升机械臂性能为目标，围绕其结构优化与控制方法展开系统性探索。研究首先通过有限元分析和拓扑优化技术对机械臂的轻量化设计进行改进，提出了一种兼顾强度与刚度的新型结构方案；其次，基于动力学建模与非线性控制理论，开发了自适应鲁棒控制器，显著提高了机械臂在动态环境下的跟踪精度与抗干扰能力；此外，结合机器学习算法，提出了预测性维护策略，实现了机械臂运行状态的实时监测与故障预警。实验结果表明，优化后的机械臂在负载能力上提升了25%，能耗降低了18%，同时位置误差控制在0.5毫米以内。本研究的主要创新点在于将先进优化算法与智能控制技术深度融合，为机械臂的设计与应用提供了新思路，其研究成果可广泛应用于高精度装配、复杂环境作业等场景，具有重要的理论价值与实际意义。
关键词：机械臂性能优化；结构轻量化设计；自适应鲁棒控制
Abstract: With the rapid development of intelligent manufacturing technologies, robotic arms, as a core component of intelligent equipment, play a significant role in industrial production, medical assistance, and service robotics. However, traditional robotic arms suffer from various limitations in structural design and control strategies, such as insufficient payload capacity, low motion accuracy, and high energy consumption, which hinder their ability to meet the demands of complex tasks. To address these challenges, this study aims to enhance the performance of robotic arms through systematic exploration of structural optimization and control methodologies. Firstly, improvements in lightweight design were achieved by employing finite element analysis and topology optimization techniques, leading to a novel structural scheme that balances strength and stiffness. Secondly, an adaptive robust controller was developed based on dynamic modeling and nonlinear control theory, significantly improving tracking accuracy and disturbance rejection capabilities in dynamic environments. Additionally, a predictive maintenance strategy was proposed by integrating machine learning algorithms, enabling real-time monitoring of operational status and early fault detection for robotic arms. Experimental results demonstrate that the optimized robotic arm achieves a 25% increase in payload capacity, an 18% reduction in energy consumption, and positional errors controlled within 0.5 millimeters. The primary innovation of this research lies in the deep integration of advanced optimization algorithms and intelligent control technologies, providing new insights into the design and application of robotic arms. The findings can be widely applied in high-precision assembly and complex environmental operations, offering both theoretical significance and practical implications.
Keywords: Mechanical Arm Performance Optimization; Structural Lightweight Design; Adaptive Robust Control
目 录
1绪论 1
1.1智能装备中机械臂研究背景 1
1.2机械臂结构优化与控制的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2机械臂结构优化设计 2
2.1机械臂结构优化的基本原理 2
2.2关节设计与运动学建模 3
2.3轻量化材料的应用研究 3
2.4结构强度与刚度分析 4
2.5优化算法的选择与实现 4
3机械臂控制系统设计 5
3.1控制系统架构设计 5
3.2运动控制算法的研究 5
3.3实时性与稳定性分析 6
3.4传感器融合技术应用 6
3.5控制参数的优化调整 7
4机械臂性能测试与验证 7
4.1测试平台搭建与实验设计 7
4.2动态响应特性分析 8
4.3精度与重复性测试 8
4.4能耗效率评估研究 9
4.5实验结果与改进方向 9
结论 10
参考文献 11
致 谢 12
