Interval Prediction for Continuous-Time Systems with Parametric Uncertainties
Provides a theoretically guaranteed stable interval prediction method for safety-critical autonomous systems with parametric uncertainties.
The paper addresses interval prediction for linear parameter-varying systems with uncertain inputs and unmeasurable scheduling parameters, designing a stable interval predictor using a novel Lyapunov function structure. The method's effectiveness is demonstrated for safe motion planning in autonomous vehicles.
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.