Data-Driven Dynamic Internal Model Control

doi:10.1109/TCYB.2024.3387409

Data-Driven Dynamic Internal Model Control

2024-09-01

1

PRE

AI

DOI：10.1109/TCYB.2024.3387409

Original

Original request for help

Save

Abstract

En 中文

A data-driven dynamic internal model control((DIMC)-I-3) scheme is proposed for unknown nonlinear non affine systems bypassing modeling steps. Different from the traditional internal model constructed by either a first-principle or an identified model, a dynamic internal model (DIM) is developed in this work using I/O data where a compact form dynamic linearization approach is introduced for addressing the nonlinearity and nonaffine structure. Then, the (DIMC)-I-3 is proposed with both a nominal control algorithm and an uncertainty compensation control algorithm. The former can quickly respond to the feedback errors and the latter can compensate the model-plant mismatch and external disturbances. Meanwhile, the adaptive parameter updating law in the proposed (DIMC)-I-3 method inherits the robustness against uncertainties. A nominal (DIMC)-I-3 is also designed without including the compensator when there is no exogenous disturbance since the adaptive mechanism can handle system uncertainty. Further, the results are extended and a full-form dynamic linearization-based (DIMC)-I-3 is developed to address control of nonlinear systems with more complex dynamics. All the proposed (DIMC)-I-3 methods are data-driven without need of an explicit model, and thus they are significant extensions from the traditional model-based IMC. Simulation study verifies the results.

Keywords:

Data-driven method

dynamic linearization

internal model control (IMC)

nonlinear nonaffine systems

Data-Driven Dynamic Internal Model Control

Abstract

Journal

Organization