Kernel-Based State-Space Kriging for Predictive Control
文献类型：期刊论文
| 作者 | A. Daniel Carnerero; Daniel R. Ramirez; Daniel Limon; Teodoro Alamo |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:5页码:1263-1275 |
| 关键词 | Data-driven methods model identification Kernel methods predictive control |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2023.123459 |
| 英文摘要 | In this paper, we extend the state-space kriging (SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging (K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control (NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51560]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | A. Daniel Carnerero,Daniel R. Ramirez,Daniel Limon,et al. Kernel-Based State-Space Kriging for Predictive Control[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(5):1263-1275. |
| APA | A. Daniel Carnerero,Daniel R. Ramirez,Daniel Limon,&Teodoro Alamo.(2023).Kernel-Based State-Space Kriging for Predictive Control.IEEE/CAA Journal of Automatica Sinica,10(5),1263-1275. |
| MLA | A. Daniel Carnerero,et al."Kernel-Based State-Space Kriging for Predictive Control".IEEE/CAA Journal of Automatica Sinica 10.5(2023):1263-1275. |
入库方式： OAI收割
来源：自动化研究所
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