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Found 10 papers, 2 papers with code
Nonparametric Control-Koopman Operator Learning: Flexible and Scalable Models for Prediction and Control
Linearity of Koopman operators and simplicity of their estimators coupled with model-reduction capabilities has lead to their great popularity in applications for learning dynamical systems.
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time.
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies.
Koopman Kernel Regression
Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e. g., the state of an agent or the reward of a policy.
Towards Data-driven LQR with Koopmanizing Flows
To utilize it for efficient LTI control design, we learn a finite representation of the Koopman operator that is linear in controls while concurrently learning meaningful lifting coordinates.
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators
When using Gaussian process regression to learn unknown systems, a commonly considered approach consists of learning the residual dynamics after applying some generic discretization technique, which might however disregard properties of the underlying physical system.
Diffeomorphically Learning Stable Koopman Operators
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling.
Learning the Koopman Eigendecomposition: A Diffeomorphic Approach
We present a novel data-driven approach for learning linear representations of a class of stable nonlinear systems using Koopman eigenfunctions.
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed.
Koopman Operator Dynamical Models: Learning, Analysis and Control
The Koopman operator allows for handling nonlinear systems through a (globally) linear representation.
