152 citations · 285 across the 20 of their papers we have counts for
15 papers · 1 filter
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Thomas Beckers, Tom Z. Jiahao, George J. Pappas
Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dy…
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Thomas Beckers, Jacob Seidman, Paris Perdikaris +1
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which…
Learning Rigidity-based Flocking Control with Gaussian Processes
Manuela Gamonal, Thomas Beckers, George J. Pappas +1
Flocking control of multi-agents system is challenging for agents with partially unknown dynamics. This paper proposes an online learning-based controller to stabilize flocking mot…
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
Thomas Beckers, Sandra Hirche, Leonardo Colombo
Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms. The design of…
An Introduction to Gaussian Process Models
Thomas Beckers
Within the past two decades, Gaussian process regression has been increasingly used for modeling dynamical systems due to some beneficial properties such as the bias variance trade…
The Impact of Data on the Stability of Learning-Based Control- Extended Version
Armin Lederer, Alexandre Capone, Thomas Beckers +2
Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, w…
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
Thomas Beckers, Leonardo Colombo, Sandra Hirche +1
Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. Trajectory tracking…
Prediction with Approximated Gaussian Process Dynamical Models
Thomas Beckers, Sandra Hirche
The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g.,…
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
Thomas Beckers, Somil Bansal, Claire J. Tomlin +1
Kernel-based nonparametric models have become very attractive for model-based control approaches for nonlinear systems. However, the selection of the kernel and its hyperparameters…
Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control
Thomas Beckers, Sandra Hirche
Tracking control for soft robots is challenging due to uncertainties in the system model and environment. Using high feedback gains to overcome this issue results in an increasing…
Stable Gaussian Process based Tracking Control of Lagrangian Systems
Thomas Beckers, Jonas Umlauft, Dana Kulić +1
High performance tracking control can only be achieved if a good model of the dynamics is available. However, such a model is often difficult to obtain from first order physics onl…
Stable Model-based Control with Gaussian Process Regression for Robot Manipulators
Thomas Beckers, Jonas Umlauft, Sandra Hirche
Computed-torque control requires a very precise dynamical model of the robot for compensating the manipulator dynamics. This allows reduction of the controller's feedback gains res…
Equilibrium Distributions and Stability Analysis of Gaussian Process State Space Models
Thomas Beckers, Sandra Hirche
Gaussian Process State Space Models (GP-SSM) are a data-driven stochastic model class suitable to represent nonlinear dynamics. They have become increasingly popular in non-paramet…
Gaussian Process based Passivation of a Class of Nonlinear Systems with Unknown Dynamics
Thomas Beckers, Sandra Hirche
The paper addresses the problem of passivation of a class of nonlinear systems where the dynamics are unknown. For this purpose, we use the highly flexible, data-driven Gaussian pr…
Stability of Gaussian Process State Space Models
Thomas Beckers, Sandra Hirche
Gaussian Process State Space Models (GP-SSMs) are a non-parametric model class suitable to represent nonlinear dynamics. They become increasingly popular in data-driven modeling ap…