From the 1 of 6 linked papers with an AI index.
6 papers
Machines that Predict Trajectories from Templates
Claudio De Persis, Pietro Tesi
The paper develops a theory for predicting future system outputs using libraries of stored trajectory templates, characterizing exact and robust prediction for linear and certain n…
Output regulation via input-output data
Andrea Bisoffi, Wenjie Liu, Zhongjie Hu +1
From a multi-input-multi-output (MIMO) discrete-time linear system, we collect input-output data affected by noise in the form of an unknown exosignal and, from these data points (…
Event-triggered control of nonlinear systems from data
Hailong Chen, Claudio De Persis, Andrea Bisoffi +1
In a recent paper [8], we introduced a data-based approach to design event-triggered controllers for linear systems directly from data. Here, we extend the results in [8] to a clas…
Data-driven stabilization of nonlinear systems via descriptor embedding
Mohammad Alsalti, Claudio De Persis, Victor G. Lopez +1
We introduce the notion of descriptor embedding for nonlinear systems and use it for the data-driven design of stabilizing controllers. Specifically, we provide sufficient data-dep…
Neural network based control of unknown nonlinear systems via contraction analysis
Hao Yin, Claudio De Persis, Bayu Jayawardhana +1
This paper studies the design of neural network (NN)-based controllers for unknown nonlinear systems, using contraction analysis. A Neural Ordinary Differential Equation (NODE) sys…
Data-driven harmonic output regulation of a class of nonlinear systems
Zhongjie Hu, Claudio De Persis, John W. Simpson-Porco +1
The paper deals with the data-based design of state-feedback controllers that solve the output regulation problem for a class of nonlinear systems. Inspired by recent developments…