TR2016-045
Stability and feasibility of MPC for switched linear systems with dwell-time constraints
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- "Stability and Feasibility of MPC for Switched Linear Systems with Dwell-time Constraints", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525323, July 2016, pp. 2681-2686.BibTeX TR2016-045 PDF
- @inproceedings{Bridgeman2016jul,
- author = {Bridgeman, Leila and Danielson, Claus and Di Cairano, Stefano},
- title = {Stability and Feasibility of MPC for Switched Linear Systems with Dwell-time Constraints},
- booktitle = {American Control Conference (ACC)},
- year = 2016,
- pages = {2681--2686},
- month = jul,
- doi = {10.1109/ACC.2016.7525323},
- url = {https://www.merl.com/publications/TR2016-045}
- }
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- "Stability and Feasibility of MPC for Switched Linear Systems with Dwell-time Constraints", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525323, July 2016, pp. 2681-2686.
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Abstract:
This paper considers the control of discretetime switched linear systems using model predictive control.A model predictive controller is designed with terminal cost and constraints depending on the terminal mode of the switched linear system. Conditions on the terminal cost and constraints are presented to ensure persistent feasibility and stability of the closed-loop system given sufficiently long dwell-time. A procedure is proposed to numerically compute admissible terminal costs and constraint sets.
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NEWS MERL makes a strong showing at the American Control Conference Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine LearningBrief- The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.