98 citations · 154 across the 15 of their papers we have counts for
19 papers
Robust Nonlinear Optimal Control via System Level Synthesis
Antoine P. Leeman, Johannes Köhler, Andrea Zanelli +2
This paper addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncert…
Inexact GMRES Policy Iteration for Large-Scale Markov Decision Processes
Matilde Gargiani, Dominic Liao-McPherson, Andrea Zanelli +1
Policy iteration enjoys a local quadratic rate of contraction, but its iterations are computationally expensive for Markov decision processes (MDPs) with a large number of states.…
Zero-Order Optimization for Gaussian Process-based Model Predictive Control
Amon Lahr, Andrea Zanelli, Andrea Carron +1
By enabling constraint-aware online model adaptation, model predictive control using Gaussian process (GP) regression has exhibited impressive performance in real-world application…
Asynchronous Computation of Tube-based Model Predictive Control
Jerome Sieber, Andrea Zanelli, Antoine P. Leeman +2
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that…
A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems
David Kiessling, Andrea Zanelli, Armin Nurkanović +5
In this paper, we propose a Feasible Sequential Linear Programming (FSLP) algorithm applied to time-optimal control problems (TOCP) obtained through direct multiple shooting discre…
Dynamic Programming Through the Lens of Semismooth Newton-Type Methods (Extended Version)
Matilde Gargiani, Andrea Zanelli, Dominic Liao-McPherson +2
Policy iteration and value iteration are at the core of many (approximate) dynamic programming methods. For Markov Decision Processes with finite state and action spaces, we show t…
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
Matilde Gargiani, Andrea Zanelli, Andrea Martinelli +2
Despite their success, policy gradient methods suffer from high variance of the gradient estimate, which can result in unsatisfactory sample complexity. Recently, numerous variance…
Introducing the quadratically-constrained quadratic programming framework in HPIPM
Gianluca Frison, Jonathan Frey, Florian Messerer +2
This paper introduces the quadratically-constrained quadratic programming (QCQP) framework recently added in HPIPM alongside the original quadratic-programming (QP) framework. The…
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Jerome Sieber, Andrea Zanelli, Samir Bennani +1
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. This…
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh +2
First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as…
An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay
Barbara Barros Carlos, Tommaso Sartor, Andrea Zanelli +4
The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors. These systems are char…
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
Matilde Gargiani, Andrea Zanelli, Moritz Diehl +1
Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training DNNs. SGN is a second-order optimization me…
A Lyapunov Function for the Combined System-Optimizer Dynamics in Inexact Model Predictive Control
Andrea Zanelli, Quoc Tran Dinh, Moritz Diehl
In this paper, an asymptotic stability proof for a class of methods for inexact nonlinear model predictive control is presented. General Q-linearly convergent online optimization m…
CIAO: MPC-based Safe Motion Planning in Predictable Dynamic Environments
Tobias Schoels, Per Rutquist, Luigi Palmieri +3
Robots have been operating in dynamic environments and shared workspaces for decades. Most optimization based motion planning methods, however, do not consider the movement of othe…
Stability Analysis of Real-Time Methods for Equality Constrained NMPC
Andrea Zanelli, Quoc Tran-Dinh, Moritz Diehl
In this paper, a proof of asymptotic stability for the combined system-optimizer dynamics associated with a class of real-time methods for equality constrained nonlinear model pred…
acados: a modular open-source framework for fast embedded optimal control
Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis +7
This paper presents the acados software package, a collection of solvers for fast embedded optimization intended for fast embedded applications. Its interfaces to higher-level lang…
Continuous Control Set Nonlinear Model Predictive Control of Reluctance Synchronous Machines
Andrea Zanelli, Julian Kullick, Hisham Eldeeb +3
In this paper we describe the design and implementation of a current controller for a reluctance synchronous machine based on continuous set nonlinear model predictive control. A c…
The BLAS API of BLASFEO: optimizing performance for small matrices
Gianluca Frison, Tommaso Sartor, Andrea Zanelli +1
BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization and other applications targe…
BLASFEO: basic linear algebra subroutines for embedded optimization
Gianluca Frison, Dimitris Kouzoupis, Tommaso Sartor +2
BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization. A key difference with respe…