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Found 16 papers, 1 papers with code
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
Modeling the dynamics of flexible objects has become an emerging topic in the community as these objects become more present in many applications, e. g., soft robotics.
Data-driven Bayesian Control of Port-Hamiltonian Systems
Port-Hamiltonian theory is an established way to describe nonlinear physical systems widely used in various fields such as robotics, energy management, and mechanical engineering.
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data.
Physics-enhanced Gaussian Process Variational Autoencoder
Variational autoencoders allow to learn a lower-dimensional latent space based on high-dimensional input/output data.
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes.
Learning Rigidity-based Flocking Control with Gaussian Processes
We provide a decentralized control law that exponentially stabilizes the motion of the agents and captures Reynolds boids motion for swarms by using GPs as an online learning-based oracle for the prediction of the unknown dynamics.
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes
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 Impact of Data on the Stability of Learning-Based Control- Extended Version
In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance.
Deep Learning based Uncertainty Decomposition for Real-time Control
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration.
Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
To overcome this issue, we present a tracking control law for underactuated rigid-body dynamics using an online learning-based oracle for the prediction of the unknown dynamics.
Prediction with Approximated Gaussian Process Dynamical Models
The modeling and simulation of dynamical systems is a necessary step for many control approaches.
Smart Forgetting for Safe Online Learning with Gaussian Processes
The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles.
Localized active learning of Gaussian process state space models
We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy.
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
In this work, we present a framework to optimize the kernel and hyperparameters of a kernel-based model directly with respect to the closed-loop performance of the model.
Mean Square Prediction Error of Misspecified Gaussian Process Models
A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios.
Stable Gaussian Process based Tracking Control of Euler-Lagrange Systems
The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence.
