no code implementations • 4 Feb 2024 • Baha Zarrouki, Marios Spanakakis, Johannes Betz
However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime.
no code implementations • 10 Nov 2023 • Baha Zarrouki, João Nunes, Johannes Betz
In this paper, we present a novel Reduced Robustified NMPC (R$^2$NMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets.
no code implementations • 7 Nov 2023 • Baha Zarrouki, Chenyang Wang, Johannes Betz
In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance.
no code implementations • 28 Oct 2023 • Baha Zarrouki, Chenyang Wang, Johannes Betz
Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints.