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

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Publié dans:Mathematics vol. 13, no. 23 (2025), p. 3854-3876
Auteur principal: Zhang Yiteng
Autres auteurs: Lin, Xiangyun, Zhang, Rui
Publié:
MDPI AG
Sujets:
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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Résumé: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
Source:Engineering Database