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

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Detalles Bibliográficos
Publicado en:	Mathematics vol. 13, no. 23 (2025), p. 3854-3876
Autor principal:	Zhang Yiteng
Otros Autores:	Lin, Xiangyun, Zhang, Rui
Publicado:	MDPI AG
Materias:	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
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	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
Fuente:	Engineering Database