34 citations · 97 across the 23 of their papers we have counts for
7 papers · 1 filter
Learning from Simulation, Racing in Reality
Eugenio Chisari, Alexander Liniger, Alisa Rupenyan +2
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation using a relativ…
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
Christopher König, Mohammad Khosravi, Markus Maier +3
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is c…
Performance-Driven Cascade Controller Tuning with Bayesian Optimization
Mohammad Khosravi, Varsha Behrunani, Piotr Myszkorowski +3
We propose a performance-based autotuning method for cascade control systems, where the parameters of a linear axis drive motion controller from two control loops are tuned jointly…
Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization
Markus Maier, Alisa Rupenyan, Christian Bobst +1
In this study, self-optimization of a grinding machine is demonstrated with respect to production costs, while fulfilling quality and safety constraints. The quality requirements o…
Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization
Mohammad Khosravi, Varsha Behrunani, Roy S. Smith +2
Cascaded controller tuning is a multi-step iterative procedure that needs to be performed routinely upon maintenance and modification of mechanical systems. An automated data-drive…
Optimization-Based Hierarchical Motion Planning for Autonomous Racing
José L. Vázquez, Marius Brühlmeier, Alexander Liniger +2
In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-le…