Frank Allgöwer — accepted AI-conference papers

2024

Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations

Patricia Pauli, Aaron J Havens, Alexandre Araujo, Siddharth Garg, Farshad Khorrami, Frank Allgöwer +1

ICLR 2024poster

Recently, semidefinite programming (SDP) techniques have shown great promise in providing accurate Lipschitz bounds for neural networks. Specifically, the LipSDP approach (Fazlyab et al., 2019) has received much attention and provides the least conservative Lipschitz upper bounds that can be compute…

Cited by 6 Source ↗PDF ↗Scholar ↗

2020

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Julian Nubert, Johannes Köhler, Vincent Berenz, Frank Allgöwer, Sebastian Trimpe

RA-L 2020

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear

Cited by 149 Source ↗Scholar ↗

2015

A robust nonlinear controller for nontrivial quadrotor maneuvers: Approach and verification

Yuyi Liu, Jan Maximilian Montenbruck, Paolo Stegagno, Frank Allgöwer, Andreas Zell

IROS 2015poster

This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bound…

Cited by 21 Source ↗Scholar ↗