Lukas Hewing

TL;DR: This work describes a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control and presents a model predictive control approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP.

TL;DR: A learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior.

TL;DR: A model-based control approach, which makes use of data gathered during operation to improve the model of the robotic arm and thereby the tracking performance, and shows how offset-free tracking can be achieved by augmenting a nominal model with both a Gaussian process and an additive disturbance model suitable for efficient online estimation of the residual disturbance via an extended Kalman filter.

TL;DR: An adaptive high performance control method for autonomous miniature race cars that makes use of a Gaussian Process and takes residual model uncertainty into account through a chance constrained formulation, enabling a real-time implementable controller.