Name
Abstract | ||
|---|---|---|
This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Process (GP) and takes residual model uncertainty into account through a chance constrained formulation. We present a sparse GP approximation with dynamically adjusting inducing inputs, enabling a real-time implementable controller. The formulation is demonstrated in simulations, which show significant improvement with respect to both lap time and constraint satisfaction compared to an NMPC without model learning. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/ECC.2018.8550162 | 2018 EUROPEAN CONTROL CONFERENCE (ECC) |
Field | DocType | ISSN |
Performance control,Constraint satisfaction,Data modeling,Residual,Control theory,Computer science,Control theory,Model predictive control,Gaussian process,Model learning | Conference | 2018 European Control Conference (ECC), Limassol, 2018, pp.
1341-1348 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Hewing | 1 | 25 | 4.27 |
| Alexander Liniger | 2 | 31 | 8.79 |
| Melanie Nicole Zeilinger | 3 | 298 | 30.91 |