1 citations · 3 across the 10 of their papers we have counts for
12 papers
Computationally Efficient Data Generation for Learning Nonlinear MPC Policies
Pietro Gori, Anton Pozharskiy, Franco Angelini +3
Learning MPC requires large datasets generated by repeatedly solving MPC problems. For Nonlinear MPC (NMPC), dataset generation is computationally expensive because feasible initia…
CCOpt: an Open-Source Solver for Large-Scale Mathematical Programs with Complementarity Constraints
Anton Pozharskiy, François Pacaud, Moritz Diehl +1
This paper presents the Julia package CCOpt, built on top of the interior-point solver MadNLP. CCOpt implements a suite of algorithms for Mathematical Programs with Complementarity…
Real-Time Algorithms for Model Predictive Control of Hybrid Dynamical Systems
Armin Nurkanović, Anton Pozharskiy, Moritz Diehl
Model predictive control (MPC) of hybrid dynamical systems is challenging because the associated optimization problem is nonsmooth and the resulting feedback law is discontinuous.…
Real-Time Non-Smooth MPC for Switching Systems: Application to a Three-Tank Process
Hendrik Alsmeier, Felix Häusser, Andreas Knödler +4
Real-time model predictive control of non-smooth switching systems remains challenging due to discontinuities and the presence of discrete modes, which complicate numerical integra…
An Augmented Lagrangian Method on GPU for Security-Constrained AC Optimal Power Flow
François Pacaud, Armin Nurkanović, Anton Pozharskiy +2
We present a new algorithm for solving large-scale security-constrained optimal power flow in polar form (AC-SCOPF). The method builds on Nonlinearly Constrained augmented Lagrangi…
Towards Solutions of Manipulation Tasks via Optimal Control of Projected Dynamical Systems
Anton Pozharskiy, Armin Nurkanović, Moritz Diehl
We introduce a modeling framework for manipulation planning based on the formulation of the dynamics as a projected dynamical system. This method uses implicit signed distance func…
First-Order Sweeping Processes and Extended Projected Dynamical Systems: Equivalence, Time-Discretization and Numerical Optimal Control
Anton Pozharskiy, Armin Nurkanović, Moritz Diehl
Constrained dynamical systems are systems such that, by some means, the state stays within a given set. Two such systems are the (perturbed) Moreau sweeping process and the recentl…
Finite Elements with Switch Detection for Numerical Optimal Control of Projected Dynamical Systems
Anton Pozharskiy, Armin Nurkanović, Moritz Diehl
The Finite Elements with Switch Detection (FESD) method is a highly accurate direct transcription method for optimal control of several classes of nonsmooth dynamical systems. This…
Solving mathematical programs with complementarity constraints arising in nonsmooth optimal control
Armin Nurkanović, Anton Pozharskiy, Moritz Diehl
This paper examines solution methods for mathematical programs with complementarity constraints (MPCC) obtained from the time-discretization of optimal control problems (OCPs) subj…
Finite Elements with Switch Detection for Numerical Optimal Control of Nonsmooth Dynamical Systems with Set-Valued Heaviside Step Functions
Armin Nurkanović, Anton Pozharskiy, Jonathan Frey +1
This paper develops high-accuracy methods for numerically solving optimal control problems subject to nonsmooth differential equations with set-valued step functions. A notable sub…
FESD-J: Finite Elements with Switch Detection for Numerical Optimal Control of Rigid Bodies with Impacts and Coulomb Friction
Armin Nurkanović, Jonathan Frey, Anton Pozharskiy +1
The Finite Elements with Switch Detection (FESD) is a high-accuracy method for the numerical simulation and solution of optimal control problems subject to discontinuous ODEs. In t…
Finite Elements with Switch Detection for Direct Optimal Control of Nonsmooth Systems with Set-Valued Step Functions
Armin Nurkanović, Jonathan Frey, Anton Pozharskiy +1
This paper extends the Finite Elements with Switch Detection (FESD) method [Nurkanović et al., 2022] to optimal control problems with nonsmooth systems involving set-valued step fu…