TR2019-061
Modular Design for Constrained Control of Actuator-Plant Cascades
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- "Modular Design for Constrained Control of Actuator-Plant Cascades", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8814744, July 2019, pp. 1755-1760.BibTeX TR2019-061 PDF
- @inproceedings{DiCairano2019jul,
- author = {Di Cairano, Stefano},
- title = {Modular Design for Constrained Control of Actuator-Plant Cascades},
- booktitle = {American Control Conference (ACC)},
- year = 2019,
- pages = {1755--1760},
- month = jul,
- publisher = {IEEE},
- doi = {10.23919/ACC.2019.8814744},
- url = {https://www.merl.com/publications/TR2019-061}
- }
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- "Modular Design for Constrained Control of Actuator-Plant Cascades", American Control Conference (ACC), DOI: 10.23919/ACC.2019.8814744, July 2019, pp. 1755-1760.
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Abstract:
We consider layered control architectures where a constraint-enforcing upper layer is cascaded with a lower layer controlled actuator. As we aim for a design where each layer requires as little knowledge as possible of the other, the upper layer is based on a model that neglects the lower layer dynamics, and includes instead additive uncertainty. The uncertainty set is constructed and “declared” by the lower layer based only on constraints on the command “declared” by the upper layer. This results in a contract between upper layer and lower layer guaranteeing a bound on the prediction error if the command satisfies the declared constraints. The command and plant constraints are robustly enforced by model predictive control with a robust control invariant set. The stability properties are analyzed, and a case study of vehicle steering control is shown.
Related News & Events
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NEWS MERL researchers presented 8 papers at American Control Conference Date: July 10, 2019 - July 12, 2019
Where: Philadelphia
MERL Contacts: Mouhacine Benosman; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference, MERL presented 8 papers on subjects including model predictive control applications, estimation and motion planning for vehicles, modular control architectures, and adaptation and learning.