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Interval Prediction for Continuous-Time Systems with Parametric\n Uncertainties

2019/04/09 by Edouard Leurent, Denis Efimov, Leurent, Edouard +5 · 16 citations
Computer Science · Engineering · Mathematics · #Artificial intelligence #Computer science #Control (management) #Control Systems and Identification #Control theory (sociology) #FOS: Electrical engineering #Formal Methods in Verification #Interval (graph theory) #Linear system #Lyapunov equation #Lyapunov function #Lyapunov optimization #Mathematical optimization #Mathematics #Nonlinear system #Parametric statistics #Scheduling (production processes) #Stability (learning theory) #Statistics #Systems and Control (eess.SY) #Vehicle Dynamics and Control Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1904.04727

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2019/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The problem of behaviour prediction for linear parameter-varying systems is\nconsidered in the interval framework. It is assumed that the system is subject\nto uncertain inputs and the vector of scheduling parameters is unmeasurable,\nbut all uncertainties take values in a given admissible set. Then an interval\npredictor is designed and its stability is guaranteed applying Lyapunov\nfunction with a novel structure. The conditions of stability are formulated in\nthe form of linear matrix inequalities. Efficiency of the theoretical results\nis demonstrated in the application to safe motion planning for autonomous\nvehicles.\n

Citations

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