Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Delay injection attacks on nonlinear control systems may trigger instability mechanisms like
finite escape time dynamics. The paper guards against such attacks by showing how a …

Particle filters (PFs) are algorithms that approximate the so-called filtering distributions in
complex state-space models. We present a unified view on PFs as importance sampling with …

Early detection of delay attacks on feedback control systems can be achieved by recursive
identification of delay and dynamics. The paper contributes with an analysis of the …

In this paper, a particle filter design scheme for a robust nonlinear control system of
uncertain heat exchange process against noise and communication time delay is presented …

Abstract System identification of nonlinear Markov jump systems (NMJSs) is crucial in
modeling complex systems that contain unknown continuous and discrete states. In this …

Simultaneous localization and map** (SLAM) is the task of building a map representation
of an unknown environment while at the same time using it for positioning. A probabilistic …

The convergence of a recursive prediction error method is analyzed. The algorithm identifies
a nonlinear continuous time state space model, parameterized by one right‐hand side …

The identification of dynamical systems from data is essential in control theory, enabling the
creation of mathematical models that accurately represent the behavior of complex systems …