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Dynamic Linearization and Extended State Observer-Based Data-Driven Adaptive Control
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AI
DOI：10.1109/TSMC.2023.3286575.png)
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Abstract
En 中文
This article aims at solving the problems of data-driven control design in the presence of strong uncertainties, hard nonlinearities, and model dependency by using a dynamic linearization (DL) method and an extended state observer (ESO). An unknown nonlinear nonaffine system is considered, whose input-output dynamics is then equivalently reformulated into a modified linear data model (mLDM) in which both a linear parametric increment description that is affine to the control input and the unmodeled uncertainties along with disturbances are included without omission or approximation. The uncertain parameter of the mLDM is estimated in real time by designing an adaptive mechanism, and the unmodeled uncertainties and disturbances are considered as a total extended state which is further estimated by developing a linear ESO. Subsequently, a modified DL-and-ESO-based data-driven adaptive control (mDLESO-DDAC) is proposed by using knowledge from previous control input to improve the control performance. The theoretical results are mathematically proved and then verified by simulations.
Keywords:
Data-driven adaptive control
dynamic linearization (DL)
extended state observer (ESO)
nonlinear nonaffine systems
strong uncertainties
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W