Name
Abstract | ||
|---|---|---|
We propose a robust reachable-set-based model predictive control method for constrained linear systems. The systems are described by sampled-data models, where a continuous-time physical plant is controlled by a discrete-time digital controller. Thus, the state measurement and the control input are only updated at discrete sampling times, while the constraint satisfaction must be guaranteed not only at, but also between two consecutive time steps. By considering the computation time and using scalable reachability analysis and convex optimization tools, we compute real-time controllers that ensure constraint satisfaction for an infinite time horizon. We demonstrate the applicability of our proposed method using a vehicle platooning benchmark. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CDC40024.2019.9029873 | 2019 IEEE 58th Conference on Decision and Control (CDC) |
Keywords | DocType | ISSN |
scalable robust model predictive control,linear sampled-data systems,robust reachable-set-based model predictive control method,constrained linear systems,sampled-data models,continuous-time physical plant,discrete-time digital controller,control input,discrete sampling times,constraint satisfaction,consecutive time steps,computation time,scalable reachability analysis,real-time controllers,infinite time horizon | Conference | 0743-1546 |
ISBN | Citations | PageRank |
978-1-7281-1399-9 | 0 | 0.34 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felix Gruber | 1 | 0 | 0.34 |
| Matthias Althoff | 2 | 383 | 50.89 |