Develops a framework for state-space estimation when the parameters of the underlying
linear model are subject to uncertainties. Compared with existing robust filters, the proposed …

Given the recent advances in computer vision, image processing and control systems, self-
driving vehicles has been one of the most promising and challenging research topics …

Path following and lateral stability are crucial issues for autonomous vehicles. Moreover,
these problems increase in complexity when handling articulated heavy-duty vehicles due to …

State estimation plays a fundamental role in control systems that rely on the knowledge of
the underlying system state, especially when it is not readily available. The Kalman filter is …

In this technical note, a robust recursive regulator for linear discrete-time systems, which are
subject to parametric uncertainties, is proposed. The main feature of the optimal regulator …

This paper formulates and solves a robust criterion for least-squares designs in the
presence of uncertain data. Compared with earlier studies, the proposed criterion …

In this paper, we propose a robust hybrid state estimation (RHSE) algorithm to redress the
presence of bounded data uncertainties (BDU) using SCADA measurements and phasor …

In this paper we deal with an optimal filtering problem for uncertain discrete-time systems.
Parametric uncertainties of the underlying model are assumed to be norm bounded. We …

The augmented state robust regularized least-squares filter (ASRRLSF) and two-stage
robust regularized least-squares filter (TSRRLSF) are proposed for discrete time-varying …

In this paper, a new robust Kalman filter is proposed for discrete-time time-varying linear
stochastic systems. The system under consideration is subject to stochastic and norm …