Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly
used in safety-critical applications. One measure of robustness to such perturbations in the …

This paper presents a robust learning-based predictive control strategy for nonlinear
systems subject to both input and output constraints, under the assumption that the model …

In this paper, some data-based control design options that can be used to accommodate for
the presence of uncertainties in continuous-state engineering systems are recalled and …

The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …

A comprehensive approach addressing identification and control for learning-based Model
Predictive Control (MPC) for linear systems is presented. The design technique yields a data …

In this study, the authors propose a stabilising data‐based model predictive controller for
systems subject to constraints in which the prediction model is inferred from experimental …

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea
mining system are the critical issues for operating stability. To solve this bottleneck problem …

In this article, an event-triggered online learning problem for Lipschitz continuous systems
with nonlinear model mismatch is considered, with the aim of building a data-efficient …

Techniques known as Nonlinear Set Membership prediction, Kinky Inference or Lipschitz
Interpolation are fast and numerically robust approaches to nonparametric machine learning …

Aiming at solving the model-free fault-tolerant spacecraft attitude control problem, a data-
driven adaptive control scheme is proposed to the spacecraft in the presence of actuator …