Abstract
This paper presents a hierarchical structure to directly design controllers for (possibly nonlinear) constrained systems. The proposed architecture combines the advantages of an inner data-driven switching controller designed to achieve a predefined closed-loop behavior and an outer model predictive controller, which is used as a reference governor. These design choices enable us to avoid the identification step typical of model-based approaches while exploiting the ability of model predictive controllers to handle constraints and optimize the closed-loop performance. As a proof of concept, a benchmark simulation example is used to demonstrate the effectiveness of the proposed strategy.
Original language | English |
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Title of host publication | 2021 American Control Conference, ACC 2021 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 2355-2360 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-6654-4197-1 |
DOIs | |
Publication status | Published - 28 Jul 2021 |
Externally published | Yes |
Event | 2021 American Control Conference, ACC 2021 - Virtual, Virtual, New Orleans, United States Duration: 25 May 2021 → 28 May 2021 http://acc2021.a2c2.org/ |
Conference
Conference | 2021 American Control Conference, ACC 2021 |
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Abbreviated title | ACC 2021 |
Country/Territory | United States |
City | Virtual, New Orleans |
Period | 25/05/21 → 28/05/21 |
Internet address |
Keywords
- Data-driven control
- Predictive models
- Switching systems
- Reference governor