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Kernel-Based State-Space Kriging for Predictive Control
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DOI：10.1109/JAS.2023.123459.png)
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Abstract
En 中文
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.
Keywords:
Computational modeling
Predictive models
Time measurement
Real-time systems
Temperature control
Nonlinear dynamical systems
Noise measurement
Data-driven methods
model identification
Kernel methods
predictive control
Journal
I
IF:
19.2
Papers:
1.4K
Citations:
1.1W