TR2016-044
Further Results and Properties of Indirect Adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty
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- "Further Results and Properties of Indirect Adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525367, July 2016, pp. 2948-2953.BibTeX TR2016-044 PDF
- @inproceedings{Donglei2016jul,
- author = {Donglei, Fan and Di Cairano, Stefano},
- title = {Further Results and Properties of Indirect Adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty},
- booktitle = {American Control Conference (ACC)},
- year = 2016,
- pages = {2948--2953},
- month = jul,
- doi = {10.1109/ACC.2016.7525367},
- url = {https://www.merl.com/publications/TR2016-044}
- }
,
- "Further Results and Properties of Indirect Adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525367, July 2016, pp. 2948-2953.
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Abstract:
We extend a recently developed design for indirect adaptive model predictive control (IAMPC) and presents additional results on its stability properties. IAMPC guarantees constraints satisfaction including during the learning transient, is input-to-state stable (ISS) with respect to the parameter estimation error, and has computational burden comparable to that of non-adaptive MPC. In this paper we extend IAMPC to the case of uncertain input-to-state matrix, we provide a new method to design robust constraints, and we show additional stability results, in particular that asymptotic stability does not require the parameter estimation error to be zero, which also allow us to derive a tighter ISS Lyapunov function.
Related News & Events
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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.