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Data-driven adaptive linear quadratic tracker for completely unknown LTI systems
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AI
DOI：10.1080/00207721.2026.2673417.png)
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Abstract
En 中文
In this article, online data-driven on-policy optimum control architecture for linear time-invariant (LTI) continuous-time (CT) systems with entirely unknown dynamics is presented. To construct an adaptive optimum controller, an online control gain estimator and system identifier utilises both previously stored data and present data in conjunction with conventional gradient descent update algorithms. This method guarantees parameter convergence without necessitating persistence of excitation (PE). The data-driven technique diverges from the traditional method by not imposing the stringent persistence of excitation (PE) criterion on the regressor. Rather, it enables online validation and creates parameter convergence by concurrently utilising the present data and the information-rich past stored data. The controller with state feedback is formed using a gain parameter, which has been proven to converge toward the optimum LQT gain. Lyapunov-based analysis is employed to ascertain the semi-global uniformly ultimately bounded (UUB) stability of the entire system. The proposed approach is subsequently corroborated by simulation outcomes.
Keywords:
Linear time invariant system
system identification
data-driven
adaptive optimal control
optimal control
linear quadratic tracker
on-policy method
Journal
I
IF:
4.6
Papers:
1.1K
Citations:
7.3K
