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摘要: 动力学与控制是研究系统运动规律力学机理及其调控方法的学科, 在现代工程与科学研究中具有重要作用. 来自工程结构、耦合构件间力传递和环境交互中的结构和几何非线性、接触力的非光滑性、环境干扰的不确定性和与环境多场耦合交互等因素的复杂性, 使得传统动力学建模、动力学响应预测和动力学控制的智能化变得异常困难. 数据驱动方法的快速发展为解决这些问题提供了全新思路和新的研究范式. 近年来的研究成果表明, 数据驱动方法不但可以解决或部分解决传统动力学方法无法解决的问题, 而且可以显著提升动力学行为预测和性能优化的能力, 为动力学与控制研究的智能化奠定必要的基础, 在复杂耦合系统的建模、分析与调控中展现出巨大的潜力与科学价值. 本文简要介绍了近年来数据驱动方法在机器人动力学建模与运动调控、跨声速气动弹性动力学建模、结构动力学设计、随机动力学、基于脑机接口技术和神经动力学模型的运动控制、机械设备故障诊断与剩余寿命预测等方面的应用研究进展, 并探讨了这些领域面临的挑战与发展趋势.Abstract: Dynamics and control is a discipline that studies the dynamic mechanisms of systems and their control strategies, and plays an important role in modern engineering and scientific research. The complexity caused by geometric nonlinearity, the non-smoothness of contact forces, and the uncertainty of environmental interferences and multi-physics problems poses significant challenges to dynamic modeling, prediction and intelligent control. The rapid development of data-driven methods has provided new ideas and new research paradigms for addressing these challenges. Recent researches have shown that data-driven methods can not only solve some problems that traditional dynamics methods cannot address but also significantly enhance the ability to predict dynamical behavior and design advanced structures. These methods lay the foundation for intelligent research in dynamics and control and demonstrate great potential and scientific value in the modeling, analysis, and regulation and control of complex systems. This paper briefly reviews the research progress of data-driven methods in areas such as robot motion control, transonic aeroelastic modeling and analysis, dynamics design, stochastic dynamics, neurodynamics, fault diagnosis and remaining useful life prediction of machinery. It also discusses the challenges and trends in these fields.
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图 2 多自由度串联机械臂分步模型重构方法示意图(Guo & Zhang 2023)
图 3 自适应−神经网络切换控制方法示意图(Jiang et al. 2018)
图 4 非定常流动的数据驱动建模及气动弹性应用流程图(Yao et al. 2022).
图 5 数据驱动的NACA0012翼型跨声速颤振预测. (a) 颤振发散时域响应; (b) 流场压强快照(Yao et al. 2022).
图 11 基于代理模型的结构多目标优化(He et al. 2021b)
图 12 (a) 基于迁移学习的拓扑优化(Xu et al. 2024); (b)-(c) 考虑非线性因素的叶片阻尼器振动特性优化(Denimal et al. 2021)
图 13 (a)-(b) 声学散射逆向设计(Wu et al. 2022); (c) GM-βVAE逆向设计框架(Wang et al. 2022d)
图 14 (a)-(b) 航空发动机叶片设计(Sun et al. 2023a); (c) TNN设计方法(Liu & Yu 2022b).
图 15 (a) PIML的涡激振动预测(Tang et al. 2022)(b) 基于物理知识增强的TMD参数设计(Yang et al. 2023)
图 17 小脑神经网络模型结构和组成. 皮层的期望运动信号和本体感觉信号由苔藓纤维输入至小脑网络, 指导信号由误差产生, 使颗粒细胞−浦肯野细胞产生突触可塑性, 小脑唯一输出核——小脑底核产生的力矩控制机械臂产生运动.
图 20 皮层−小脑模型在目标轨迹探索实验中的表现: (a)在期望轨迹为圆形的150试验过程中, 机械臂末端轨迹随试验次数的变化. (b)肩、肘关节角随试验次数的变化, 在对期望轨迹的探索中, 肩、肘关节角误差随试验次数不断下降
图 22 基于一维深度卷积神经网络的智能诊断模型图(Zhao et al. 2020)
图 23 智能诊断问题中的不同迁移学习场景(Ma et al. 2020)
图 24 基于分布度量的迁移诊断方法流程图(Yang et al. 2019b)
图 25 基于残差卷积长短期记忆网络的寿命预测方法模型结构图(Wang et al. 2020a)
图 26 基于分布度量的迁移预测方法流程图(Chen et al. 2023a)
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