Hybrid Partial-Data-Driven H∞ Robust Tracking Control for Linear Stochastic Systems with Discrete-Time Observation of Reference Trajectory

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書誌詳細
出版年:	Mathematics vol. 13, no. 23 (2025), p. 3854-3876
第一著者:	Zhang Yiteng
その他の著者:	Lin, Xiangyun, Zhang, Rui
出版事項:	MDPI AG
主題:	Autonomous underwater vehicles Robust control Random variables Parameter estimation Brownian motion Tracking control Reference systems Controllers Least squares method Design Unmanned aerial vehicles Discrete time systems Control systems State feedback Hybrid control Tracking errors Tracking problem Feedback control H-infinity control Stochastic systems
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その他の書誌記述
抄録:	A hybrid robust <inline-formula>H∞</inline-formula> tracking-control design method is studied for linear stochastic systems in which the parameters of the reference system are unknown but inferred from discrete-time observations. First, the reference system parameters are estimated by the least-squares method, and a corresponding data-dependent augmented system is constructed. Second, a Riccati matrix inequality is established for these systems, and a state-feedback <inline-formula>H∞</inline-formula> controller is designed to improve tracking performance. Third, to mitigate large tracking errors, an error-feedback control scheme is introduced to compensate for dynamic tracking deviations. These results yield a hybrid control framework that integrates data observation, state-feedback <inline-formula>H∞</inline-formula> control, and error-feedback <inline-formula>H∞</inline-formula> control to address the tracking problem more effectively. Two numerical examples and one practical example demonstrate the effectiveness of the proposed method.
ISSN:	2227-7390
DOI:	10.3390/math13233854
ソース:	Engineering Database