This paper deals with the robustness of Model Predictive Controllers for constrained
uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input …

A novel optimization method is proposed to minimize a convex function subject to bilinear
matrix inequality (BMI) constraints. The key idea is to decompose the bilinear mapping as a …

This paper proposes a set-membership state estimator and a zonotopic Kalman observer for
discrete-time descriptor systems. Both approaches are developed in a set-based context …

Model Predictive Control (MPC) is an area in rapid development with respect to both
theoretical and application aspects. The former petrochemical applications of MPC were …

This paper presents new methods for set-valued state estimation of nonlinear discrete-time
systems with unknown-but-bounded uncertainties. A single time step involves propagating …

This title focuses on two significant problems in the field of automatic control, in particular
state estimation and robust Model Predictive Control under input and state constraints …

This paper presents new methods for set-valued state estimation of discrete-time nonlinear
systems whose trajectories are known to satisfy nonlinear equality constraints, called …

In this paper we provide a method to compute robust control invariant sets for nonlinear
discrete-time systems. A simple criterion to evaluate if a convex set in state space is a robust …

This paper proposes a new set-membership technique to implement polytopic set
computation for nonlinear discrete-time systems indirectly. The proposed set-membership …

We propose two distributed set-based observers using strip-based and set-propagation
approaches for linear discrete-time dynamical systems with bounded modeling and …