Participating in electricity markets through demand response causes new requirements for
optimizing process control of chemical plants. The last ten years have brought great …

In this paper, we propose an efficient zero-order algorithm that can be used to compute an
approximate solution to robust optimal control problems (OCP) and robustified nonconvex …

In recent years, efficient optimization algorithms for nonlinear model predictive control
(NMPC) have been proposed, that significantly reduce the online computational time. In …

Tractable safety-ensuring algorithms for cyber-physical systems are important in critical
applications. Approaches based on Control Barrier Functions assume continuous …

This thesis embodies a foundation for practically applicable implementations of predictive
methods for motion planning through corridors in the workspace of robotic systems. The …

This paper introduces an extension to the well-known Real Time Iteration (RTI) for Nonlinear
Model Predictive Control (NMPC). We combine algorithmic ideas of the RTI, Advanced Step …

In this paper, we derive a general contraction estimate for a class of algorithms that can be
used in the context of nonlinear model predictive control (NMPC) to compute approximate …

In recent years several fast Nonlinear Model Predictive Control (NMPC) strategies have
been proposed, aiming at reducing computational burden and widening the scope of NMPC …

This paper discusses systems theoretic and computational aspects of a feasible, but
suboptimal, nonlinear model predictive control scheme based on fixed sensitivities of the …

Moving Horizon Estimation (MHE) is an optimization-based approach to nonlinear state
estimation. The computational burden associated with the online solution of the …