From the 1 of 9 linked papers with an AI index.
6 papers · 1 filter
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…
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…
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…
Feedback linearization through the lens of data
C. De Persis, D. Gadginmath, F. Pasqualetti +1
Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear c…
Controller synthesis for input-state data with measurement errors
Andrea Bisoffi, Lidong Li, Claudio De Persis +1
We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data. We focus on input-state data corrupted by measurement er…