Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910734021033984 |
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| author | Krupa, Pablo Köhler, Johannes Ferramosca, Antonio Alvarado, Ignacio Zeilinger, Melanie N. Alamo, Teodoro Limon, Daniel |
| author_facet | Krupa, Pablo Köhler, Johannes Ferramosca, Antonio Alvarado, Ignacio Zeilinger, Melanie N. Alamo, Teodoro Limon, Daniel |
| contents | This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_06157 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation Krupa, Pablo Köhler, Johannes Ferramosca, Antonio Alvarado, Ignacio Zeilinger, Melanie N. Alamo, Teodoro Limon, Daniel Systems and Control This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. |
| title | Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2406.06157 |