一种用于液压缸实时摩擦力估计的混合数据驱动算法
A Hybrid Data-Driven Algorithm for Real-Time Friction Force Estimation in Hydraulic Cylinders
AI总结：
本文提出一种结合LSTM和随机森林的混合算法，用于高精度实时估计液压缸摩擦力，克服传统模型的适应性与计算效率限制。
摘要：
液压系统因其高力输出、精确控制和在恶劣环境中的工作能力而在工业应用中被广泛使用。作为这些系统中的执行器，液压缸通过液压流体的位移来施加力和位置，但其操作受到摩擦力的显著影响。实现液压缸的精度需要在各种操作条件下准确的摩擦模型。现有的分析模型通常来自实验测试，需要识别或估计影响因素，但其适应性和计算效率有限。本研究介绍了一种基于长短期记忆（LSTM）网络和随机森林的混合数据驱动算法，用于非线性摩擦力估计。该算法通过从实验液压测试装置获取的训练数据，有效结合了特征检测和估计过程。它在多样化的操作条件和外部负载变化下实现了小于10%的一致且稳定的模型误差，确保了在复杂情况下的鲁棒性能。该算法的计算成本为每估计1.51毫秒，使其适用于实时应用。所提出的方法通过详细的分析和实验结果验证，包括与LuGre模型的直接比较。比较表明，尽管LuGre模型为摩擦建模提供了理论基础，但其性能受限于其无法动态调整液压缸的运行条件，进一步突显了所提出混合方法在实时应用中的优势。
英文摘要：
Hydraulic systems are widely utilized in industrial applications due to their high force generation, precise control, and ability to function in harsh environments. Hydraulic cylinders, as actuators in these systems, apply force and position through the displacement of hydraulic fluid, but their operation is significantly influenced by friction force. Achieving precision in hydraulic cylinders requires an accurate friction model under various operating conditions. Existing analytical models, often derived from experimental tests, necessitate the identification or estimation of influencing factors but are limited in adaptability and computational efficiency. This research introduces a data-driven, hybrid algorithm based on Long Short-Term Memory (LSTM) networks and Random Forests for nonlinear friction force estimation. The algorithm effectively combines feature detection and estimation processes using training data acquired from an experimental hydraulic test setup. It achieves a consistent and stable model error of less than 10% across diverse operating conditions and external load variations, ensuring robust performance in complex situations. The computational cost of the algorithm is 1.51 milliseconds per estimation, making it suitable for real-time applications. The proposed method addresses the limitations of analytical models by delivering high precision and computational efficiency. The algorithm's performance is validated through detailed analysis and experimental results, including direct comparisons with the LuGre model. The comparison highlights that while the LuGre model offers a theoretical foundation for friction modeling, its performance is limited by its inability to dynamically adjust to varying operational conditions of the hydraulic cylinder, further emphasizing the advantages of the proposed hybrid approach in real-time applications.
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