摘要
Advancements in dynamic modeling methods of robotic manipulator are critical to the effective implementation of model-based control.Traditional approaches rely on rigorous first-principles-based dynamic modeling and precise parameter identification,while this paper explores an altemative through data-driven model reconstruction.To tackle the curse of dimensionality in the model reconstruction of a serial robotic manipulator with multi-degree-of-freedom,a relative activation indicator is proposed.Based on this indicator,the k-means clustering algorithm is utilized to classify the data under different working conditions.Sub-sequently,we leverage the fundamental prior knowledge to find the dynamical characteristics of each cluster and reconstruct the dynamic model in a stepwise manner using the method of sparse identification of nonlinear dynamics(SINDy).For the library generation of SINDy,the strategy of double-feature-set for serial manipulators with common joint types is proposed.Simula-tion results show that the stepwise model reconstruction approach not only reduces the size of the library of candidate functions but also decreases the impact of data noise on the reconstruction results.Finally,controllers based on the reconstructed mod.els are deployed on the experimental platform and the experimental results demonstrate the improvement in trajectory tracking performance and the potential of the proposed method in engineering applications.
机械臂动力学建模方法的发展对于有效部署基于模型的控制至关重要.传统方法依赖于基于第一原理的动力学建模和精确的参数识别,而本文则通过数据驱动的模型重构探索了另一种方法.为解决多自由度串联机械臂模型重构中的维度灾难问题,本文提出了一种相对激活指标,并在此基础上使用k-means聚类算法对不同工况下机械臂的数据进行分类.随后,我们利用基础的先验知识找到每个聚类的动力学特征,并使用非线性动力学稀疏识别(SINDy)方法分步重构动力学模型.为生成SINDy候选函数库,本文提出了针对具有常见关节类型的串联机械臂的双特征集策略.仿真结果表明,分步模型重构方法不仅减少了候选函数库的规模,还降低了数据噪声对重构结果的影响.最后,我们在实验平台上部署了基于重构模型的控制器,实验结果证明了该方法在实验平台轨迹跟踪性能方面的提升以及在工程应用方面的潜力.
基金
supported by the National Natural Science Foundation of China(Grant Nos.12072237,12472022,12372022,12372065,and U2441202)
the Fundamental Research Funds for the Central Universities(Grant No.22120220590)。
