Dynamic Linearization and Extended State Observer-Based Data-Driven Adaptive Control
针对强不确定性、硬非线性和模型依赖问题，提出一种结合动态线性化和扩展状态观测器的数据驱动自适应控制方法，将未知非线性非仿射系统等价转化为修正线性数据模型，并实时估计不确定参数和总扩展状态，利用历史控制输入提升性能。
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.