| Autor: |
Muhammed Bahadır Saltık, Leyla Özkan, Siep Weiland |
| Přispěvatelé: |
Smart Process Operations and Control Lab, Cyber-Physical Systems Center Eindhoven, Control Systems, EAISI High Tech Systems, EIRES Chem. for Sustainable Energy Systems, EIRES Eng. for Sustainable Energy Systems, EAISI Foundational, Control of high-precision mechatronic systems, Spatial-Temporal Systems for Control, Discrete Technology and Production Automation |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
International Journal of Robust and Nonlinear Control, 32(15), 8252-8279. Wiley |
| ISSN: |
1049-8923 |
| Popis: |
In this article, we present a novel linear model predictive control (MPC) strategy for controlling uncertain dynamical systems. The moment based MPC strategy is based on the (centralized) moments of the stochastic cost and constraint functions. We show that moment based MPC formulations yield computationally tractable predictive control problems, due to the explicit analytic expressions of the moments and linearity in the dynamics. This article considers (i) the output regulation MPC configuration, where the output is steered towards a reference and state vector is not correctly accessible to the controller at the decision instants; (ii) the moment based MPC formulation with uncertainties that are non-Gaussian random variables, and (iii) bound and polytopic constraints in the uncertain MPC problem subject to either Gaussian disturbances or uncertainties with symmetric distributions. We demonstrate the effectiveness of the proposed MPC technique on simulation examples such as a double integrator system and a quadruple tank system. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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