This paper gives an overview of recent developments in set-theoretic methods for nonlinear
systems, with a particular focus on the activities in our own research group. Central to these …

This chapter aims to give a concise overview of numerical methods and algorithms for
implementing robust model predictive control (MPC). We introduce the mathematical …

A wide variety of approaches for set-valued simulation, parameter identification, state
estimation as well as reachability, observability and stability analysis for nonlinear discrete …

A new method is presented for enclosing the reachable sets of nonlinear ordinary differential
equations subject to a range of inputs. Reachable set enclosures are used for uncertainty …

For the performance of global optimization algorithms, the rate of convergence of convex
relaxations to the objective and constraint functions is critical. We extend results from …

This study is concerned with the rigorous solution of worst‐case robust optimal control
problems having bounded time‐varying uncertainty and nonlinear dynamics with affine …

A reliable quantification of the worst-case influence of model uncertainty and external
disturbances is crucial for the localization of vessels in marine applications. This is …

We propose a complete-search algorithm for solving a class of non-convex, possibly infinite-
dimensional, optimization problems to global optimality. We assume that the optimization …

In many automated motion planning systems, vehicles are tasked with tracking a reference
path or trajectory that is safe by design. However, due to various uncertainties, real vehicles …

The graph-based invariant set (GIS) algorithm is a promising set-based technique for
computing the largest (with respect to inclusion) control invariant set of general discrete-time …