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1篇 您的检索式：作者名="Daniel Limon"

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		题名	作者	年代	出处	被引量
	1	Kernel-Based State-Space Kriging for Predictive Control显示文摘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.	A.Daniel Carnerero Daniel R.Ramirez Daniel Limon Teodoro Alamo	2023	IEEE/CAA Journal of Automatica Sinica2023，10，5：	0

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