Dynamic Tube MPC for Nonlinear Systems
Modeling error or external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over feedback policies but at the expense of increased computational complexity. Tube MPC is an approximat...
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130262 |
| _version_ | 1826199793573036032 |
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| author | Lopez, Brett Thomas Slotine, Jean-Jacques E How, Jonathan P |
| author2 | Massachusetts Institute of Technology. Aerospace Controls Laboratory |
| author_facet | Massachusetts Institute of Technology. Aerospace Controls Laboratory Lopez, Brett Thomas Slotine, Jean-Jacques E How, Jonathan P |
| author_sort | Lopez, Brett Thomas |
| collection | MIT |
| description | Modeling error or external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over feedback policies but at the expense of increased computational complexity. Tube MPC is an approximate solution strategy in which a robust controller, designed offline, keeps the system in an invariant tube around a desired nominal trajectory, generated online. Naturally, this decomposition is suboptimal, especially for systems with changing objectives or operating conditions. In addition, many tube MPC approaches are unable to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for nonlinear systems where both the tube geometry and open-loop trajectory are optimized simultaneously. By using boundary layer sliding control, the tube geometry can be expressed as a simple relation between control parameters and uncertainty bound; enabling the tube geometry dynamics to be added to the nominal MPC optimization with minimal increase in computational complexity. In addition, DTMPC is able to leverage state-dependent uncertainty to reduce conservativeness and improve optimization feasibility. DTMPC is demonstrated to robustly perform obstacle avoidance and modify the tube geometry in response to obstacle proximity. |
| first_indexed | 2024-09-23T11:25:53Z |
| format | Article |
| id | mit-1721.1/130262 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:25:53Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1302622022-10-01T03:36:30Z Dynamic Tube MPC for Nonlinear Systems Lopez, Brett Thomas Slotine, Jean-Jacques E How, Jonathan P Massachusetts Institute of Technology. Aerospace Controls Laboratory Massachusetts Institute of Technology. Nonlinear Systems Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Modeling error or external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over feedback policies but at the expense of increased computational complexity. Tube MPC is an approximate solution strategy in which a robust controller, designed offline, keeps the system in an invariant tube around a desired nominal trajectory, generated online. Naturally, this decomposition is suboptimal, especially for systems with changing objectives or operating conditions. In addition, many tube MPC approaches are unable to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for nonlinear systems where both the tube geometry and open-loop trajectory are optimized simultaneously. By using boundary layer sliding control, the tube geometry can be expressed as a simple relation between control parameters and uncertainty bound; enabling the tube geometry dynamics to be added to the nominal MPC optimization with minimal increase in computational complexity. In addition, DTMPC is able to leverage state-dependent uncertainty to reduce conservativeness and improve optimization feasibility. DTMPC is demonstrated to robustly perform obstacle avoidance and modify the tube geometry in response to obstacle proximity. National Science Foundation (Grant 1122374) ARL-DCIST (Contract W911NF-17-2-0181) 2021-03-29T19:53:05Z 2021-03-29T19:53:05Z 2019-08 2019-07 2020-08-07T15:41:18Z Article http://purl.org/eprint/type/ConferencePaper 9781538679265 2378-5861 https://hdl.handle.net/1721.1/130262 Lopez, Brett T. et al. "Dynamic Tube MPC for Nonlinear Systems." 2019 American Control Conference, July 2019, Philadelphia, Pennsylvania, Institute of Electrical and Electronics Engineers, August 2019. © 2019 American Automatic Control Council en http://dx.doi.org/10.23919/acc.2019.8814758 2019 American Control Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Lopez, Brett Thomas Slotine, Jean-Jacques E How, Jonathan P Dynamic Tube MPC for Nonlinear Systems |
| title | Dynamic Tube MPC for Nonlinear Systems |
| title_full | Dynamic Tube MPC for Nonlinear Systems |
| title_fullStr | Dynamic Tube MPC for Nonlinear Systems |
| title_full_unstemmed | Dynamic Tube MPC for Nonlinear Systems |
| title_short | Dynamic Tube MPC for Nonlinear Systems |
| title_sort | dynamic tube mpc for nonlinear systems |
| url | https://hdl.handle.net/1721.1/130262 |
| work_keys_str_mv | AT lopezbrettthomas dynamictubempcfornonlinearsystems AT slotinejeanjacquese dynamictubempcfornonlinearsystems AT howjonathanp dynamictubempcfornonlinearsystems |