Finite-sample system identification algorithms can be used to build guaranteed confidence
regions for unknown model parameters under mild statistical assumptions. It has been …

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 …

Estimating a function from noisy measurements is a crucial problem in statistics and
engineering, with an impact on machine learning predictions and identification of dynamical …

Here the problem of designing two degrees of freedom controllers for an unknown plant
based on input–output measurements is discussed. Virtual reference feedback tuning aims …

Abstract Sign-Perturbed Sums (SPS) is a finite sample system identification method that
constructs exact, non-asymptotic confidence regions for the unknown parameters of linear …

This paper considers the problem of the ground target positioning and tracking algorithm for
multi UAVs formation cooperative detection, and a real time and fast algorithm is proposed …

A key challenge in science and engineering is to provide a quantitative description of the
systems under investigation, leveraging the noisy data collected. Such a description may be …

Complementing data-driven models of dynamic systems with certificates of reliability and
safety is of critical importance in many applications, such as in the design of robust control …

Results of mechanical tests, especially in dynamics, obtained under similar input conditions
always have a statistical scatter. Currently, there is no generally accepted engineering …

The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system
identification method for the identification of closed-loop observable stochastic linear …