arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.04550eess.SYcs.SY
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使用数据驱动模型对机械系统进行反馈线性化

Feedback linearisation of mechanical systems using data-driven models

Merijn Floren, Koen Classens, Tom Oomen, Jean-Philippe Noël

AI总结:

针对非线性机械系统反馈线性化依赖难以获得的第一性原理模型的问题,提出利用数据驱动模型线性化输入-输出响应的基于模型的参考跟踪控制架构,实验证明其具有高性能及对不完美建模和外推的鲁棒性。

摘要:

非线性机械系统动力学线性化是一个重要且开放的研究领域。一种常见方法是反馈线性化,这是一种将非线性系统的输入-输出响应转化为等效线性响应的非线性控制方法。反馈线性化的主要问题在于它需要系统精确的第一性原理模型,而这通常难以获得。在本文中,我们设计了一种替代控制方法,利用数据驱动模型来线性化非线性机械系统的输入-输出响应。具体而言,针对具有输出非线性的非线性反馈系统,开发了一种基于模型的参考跟踪架构。该方法整体表现出高性能,并对不完美建模和外推具有显著的鲁棒性。这些发现通过在非对称 Duffing 振荡器上进行的大量合成实验,以及使用高精度运动系统的实验原型得到了验证。

英文摘要:

Linearising the dynamics of nonlinear mechanical systems is an important and open research area. A common approach is feedback linearisation, which is a nonlinear control method that transforms the input-output response of a nonlinear system into an equivalent linear one. The main problem with feedback linearisation is that it requires an accurate first-principles model of the system, which are typically hard to obtain. In this paper, we design an alternative control approach that exploits data-driven models to linearise the input-output response of nonlinear mechanical systems. Specifically, a model-based reference tracking architecture is developed for nonlinear feedback systems with output nonlinearities. The overall methodology shows a high degree of performance combined with significant robustness against imperfect modelling and extrapolation. These findings are demonstrated using large set of synthetic experiments conducted on a asymmetric Duffing oscillator and using an experimental prototype of a high-precision motion system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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