.png){kind=icon}
返回
Data-driven learning and control of nonlinear system dynamics
.png){kind=icon}
OA
AI
DOI：10.1007/s11071-024-10149-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work falls into the field of discovering the dynamic equations of stabilizable nonlinear systems, via a learning-and-control algorithm to process the data sets of trajectories previously obtained. To this end, an interlaced method to learn and control nonlinear system dynamics from a set of demonstrations is proposed, under a constrained optimization framework for the unsupervised learning process. The nonlinear system is modeled as a mixture of Gaussians and Sontag's formula together with its associated Control Lyapunov Function is proposed for learning and control. Lyapunov stability and robustness in noisy data environments are guaranteed, as a result of the inclusion of control in the learning-optimization problem. The performances are validated through a well-known dataset of demonstrations with handwriting complex trajectories, succeeding in all of them and outperforming previous methods under bounded disturbances, possibly coming from inaccuracies, imperfect demonstrations, or noisy datasets. As a result, the proposed interlaced solution yields a good performance trade-off between reproductions and robustness. Therefore, this work sheds some more light on the automatic discovery of nonlinear dynamics from noisy raw data.
Keyword:
Data-driven dynamics
Control Lyapunov functions
Unsupervised machine learning
期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
机构
引用论文
.png){kind=icon}
Learning and Reproduction of Gestures by Imitation An Approach Based on Hidden Markov Model and Gaussian Mixture Regression基于隐马尔可夫模型和高斯混合回归的手势模仿学习与再现方法OAAI
OAAI分享收藏分享收藏分享收藏分享收藏
查看更多内容