The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Cautious Model Predictive Control Using Gaussian Process Regression
Open accessAbstract
Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the context of control, it is seeing increasing use for modeling of nonlinear dynamical systems from data, as it allows the direct assessment of residual model uncertainty. We present a model predictive control (MPC) approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP. We describe a principled way of formulating the chance-constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control. Using additional approximations for efficient computation, we finally demonstrate the approach in a simulation example, as well as in a hardware implementation for autonomous racing of remote-controlled race cars with fast sampling times of 20 ms, highlighting improvements with regard to both performance and safety over a nominal controller.
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Cites
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Learning-Based Model Predictive Control for Safe Exploration
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Cited by
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 835
Self-citationData-Driven Model Predictive Control With Stability and Robustness Guarantees
Berberich, J., Kohler, J., Muller, M. A., & Allgower, F. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3000182 · Cited by 803
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-042920-020211 · Cited by 769
Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator
Wang, Y., Leibold, M., Lee, J., Ye, W., Xie, J., & Buss, M. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3142629 · Cited by 52
Kohler, J., Soloperto, R., Muller, M. A., & Allgower, F. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.2982585 · Cited by 19
Metadata
- Authors
- Hewing, L., Kabzan, J., & Zeilinger, M. N.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2020-11
- Volume
- 28
- Issue
- 6
- Pages
- 2736-2743
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- 2374-0159
- OpenAlex
- W2619551236
- Cited by
- 577
- References
- 16
- FWCI
- 21.37
- Type
- journal article
- Language
- en
- Open access
- green
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