Kernel-Based State-Space Kriging for Predictive Control  

作　　者：A.Daniel Carnerero Daniel R.Ramirez Daniel Limon Teodoro Alamo 

机构地区：[1]the Department of System Engineering and Automation,University of Seville,Sevilla 41020,Spain [2]the School of Engineering,Tokyo Institute of Technology,Tokyo 152-8552,Japan [3]Department of System Engineering and Automation,University of Seville,Sevilla 41020,Spain

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第5期1263-1275,共13页自动化学报（英文版）

基　　金：supported by the Agencia Estatal de Investigación (AEI)-Spain (PID2019-106212RB-C41/AEI/10.13039/501100011033);Junta de Andalucía and FEDER funds (P20_00546)。

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.

关 键 词：Data-driven methods model identification Kernel methods predictive control 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]