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Gaussian process model based predictive control
Abstract
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.
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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
Cautious Model Predictive Control Using Gaussian Process Regression
Hewing, L., Kabzan, J., & Zeilinger, M. N. (2020) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2019.2949757 · Cited by 577
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018) · arXiv · doi:10.48550/arxiv.1805.12114 · Cited by 525
Model learning for robot control: A survey
Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503
Cautious Model Predictive Control Using Gaussian Process Regression
Hewing, L., Kabzan, J., & Zeilinger, M. N. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000389524 · Cited by 469
Metadata
- Authors
- Kocijan, J., Murray-Smith, R., Rasmussen, C. E., & Girard, A.
- Venue
- Proceedings of the 2004 American Control Conference
- Publisher
- IEEE
- Published
- 2004
- Volume
- 3
- Pages
- 2214-2219 vol.3
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W2162977544
- Cited by
- 267
- References
- 0
- FWCI
- 4.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kocijan, J., Murray-Smith, R., Rasmussen, C. E., & Girard, A. (2004). Gaussian process model based predictive control. Proceedings of the 2004 American Control Conference. https://doi.org/10.23919/acc.2004.1383790
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