MANZANO CRESPO, JOSÉ MARÍA, Munoz de la Pena, David , Limon, Daniel
No
Int. J. Robust Nonlinear Control
Article
Científica
3.9
1.403
13/12/2022
000898061600001
2-s2.0-85144044501
In this article, the authors propose a novel continuous projected kinky inference algorithm, which inherits the good properties of projected kinky inference in terms of prediction error bound and computational time while ensuring Lipschitz continuity. Based on this, a learning based MPC is presented which is demonstrated to be input-to-state stable by design. Illustrative examples are shown in a learning-based MPC framework.
inference algorithms; machine learning; predictive control; input-to-state stability; system identification