Interval Prediction for Continuous-Time Systems with Parametric Uncertainties

Interval Prediction for Continuous-Time Systems with Parametric Uncertainties

Abstract

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

Publication
Proceedings of the 58th Conference on Decision and Control (CDC 2019)
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Edouard Leurent
PhD

My research interests include control, statistical learning and robotics.