Considering discrete-time linear time-varying systems with unknown dynamics, controllers guaranteeing bounded closed-loop trajectories, optimal performance and robustness to process measurement noise are designed via convex feasibility optimisation problems involving purely data-dependent matrix inequalities. For the special case of periodically systems, infinite-horizon guarantees achieved bas...

Machine learning can be effectively applied in control loops to make optimal decisions robustly. There is increasing interest using spiking neural networks (SNNs) as the apparatus for machine engineering because SNNs potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware being rapidly developed. A defining characteristic of problems that environmental reactions del...

This paper presents a data-driven control for network of autonomous vehicles. The general scheme cruise is implemented set vehicles defined by nonlinear equation. Koopman operator allows representing the system in space (lifted space), suitable to get linear observer that used distributed instead model. proposed simplified design and also use Model Predictive Control (MPC) form.

A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) linear time-invariant (LTI) with unknown dynamics purely data. While most data-driven literature focuses on robustness respect measurement noise, only a few works consider exploiting probabilistic information of disturbances performance-oriented in...