Conference item
Optimization-based trajectory generation and receding horizon control for systems with convex dynamics
- Abstract:
- In this paper we propose an optimization-based control scheme, which can be used for trajectory generation or receding horizon control for system with nonlinear, but convex dynamics, and both explicit and implicit discrete time models. The scheme uses both the nonlinear model and its linearization to construct a tube containing all possible future system trajectories, and uses this tube to predict performance and ensure constraint satisfaction. The controls sequence and tube cross-sections are optimized online in a sequence of convex programs without the need of pre-computed error bounds. We prove feasibility, stability and non-conservativeness of the approach, with the series of convex programs converging to a point which is a local optimum for the original nonlinear optimal control problem. We further present how a structure preserving model can be implemented within the approach and used to reduce the number of constraints and guarantee a structure-preserving discrete trajectory solution.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 545.1KB, Terms of use)
-
- Publisher copy:
- 10.1109/CDC49753.2023.10383697
Authors
- Publisher:
- IEEE
- Host title:
- Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)
- Pages:
- 3262-3269
- Publication date:
- 2024-01-19
- Acceptance date:
- 2023-09-15
- Event title:
- 62nd IEEE Conference on Decision and Control (CDC 2023)
- Event location:
- Marina Bay Sands, Singapore
- Event website:
- https://cdc2023.ieeecss.org/
- Event start date:
- 2023-12-13
- Event end date:
- 2023-12-15
- DOI:
- EISSN:
-
2576-2370
- ISSN:
-
0743-1546
- EISBN:
- 979-8-3503-0124-3
- ISBN:
- 979-8-3503-0125-0
- Language:
-
English
- Keywords:
- Pubs id:
-
1595532
- Local pid:
-
pubs:1595532
- Deposit date:
-
2024-01-06
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2024
- Rights statement:
- ©️ IEEE 2024
- Notes:
- This paper was presented at the 62nd IEEE Conference on Decision and Control (CDC 2023), Marina Bay, Singapore, 13th-15th December 2023. This is the accepted manuscript version of the article. The final version is available online from IEEE at https://dx.doi.org/10.1109/CDC49753.2023.10383697
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