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Found 11 papers, 1 papers with code
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.
Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Our approach requires no prior model and corresponds, to the best of our knowledge, to the first algorithm that guarantees safety in settings with occasionally infeasible safety certificates without requiring a backup non-learning-based controller.
Online Constraint Tightening in Stochastic Model Predictive Control: A Regression Approach
Solving chance-constrained stochastic optimal control problems is a significant challenge in control.
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.
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Our approach computes a confidence region in the space of hyperparameters, which enables us to obtain a probabilistic upper bound for the model error of a Gaussian process with arbitrary hyperparameters.
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.
Anticipating the Long-Term Effect of Online Learning in Control
Control schemes that learn using measurement data collected online are increasingly promising for the control of complex and uncertain systems.
Parameter Optimization for Learning-based Control of Control-Affine Systems
By relaxing the problem through scenario optimization we derive a provably optimal method for control parameter tuning.
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.
How Training Data Impacts Performance in Learning-based Control
When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations.
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.
