A data-driven CLF controller based on a kriged predictor

A.D. Carnerero, D.R. Ramirez, T. Hatanaka, T. Alamo

In this paper, we present a data-driven methodology to predict and control the behaviour of nonlinear and non-autonomous systems based on kernel functions. The technique computes the forecasting by means of a linear combination of past data. The weights used to compute the prediction are obtained by solving a convex optimization problem that stems from a novel kriging formulation. A Control Lyapunov Function (CLF) based controller using the presented predictor is also built. Finally, numerical examples of both prediction and control are presented, showing the efficacy of the proposed approach.