Hybrid discrete-continuous models, such as Jump Markov Linear Systems, are convenient tools for representing many real-world systems; in the case of fault detection, discrete jumps in the continuous dynamics are used to model system failures. Stochastic uncertainty in hybrid systems arises in both the continuous dynamics, in the form of uncertain state estimation, disturbances or uncertain mode...

We develop a preconditioned Bayesian regression method that enables sparse polynomial chaos representations of noisy outputs for stochastic chemical systems with uncertain reaction rates. The approach is based on the definition of an appropriate multiscale transformation of the state variables coupled with a Bayesian regression formalism. This enables efficient and robust recovery of both the t...

Abstract This paper considers the problem of robot motion planning in a workspace with obstacles for systems uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers adaptive control techniques to guarantee collision-free navigation predefined goal while compensating dynamic model uncertainties. We base our findings on sphere world-based configura...

In adaptive platooning strategies proposed in literature to handle uncertain and nonidentical vehicle dynamics (uncertain heterogeneous platoons) two aspects requiring proper design are neglected: bidirectional interaction among vehicles which might lead loss of string stability, engine saturation constraints cohesiveness. This work proposes a novel strategy handling these crucial aspects. Spec...