In this paper, we consider the problem of optimally designing the experimental conditions for
LPV system identification with the local approach. Such an LPV system identification …

Nonlinear model predictive control (NMPC) has become an important tool for optimization
based control of many (bio) chemical systems. A requirement for a well-performing NMPC …

This paper discusses the application of the virtual reference tuning (VRT) techniques to tune
neural controllers from batch input–output data, by particularising nonlinear VRT and …

In this paper, we present an overview of probabilistic techniques based on randomized
algorithms for solving “hard''problems arising in performance verification and control of …

The objective of this contribution is to quantify benefits of optimal input design compared to
the use of standard identification input signals, eg PRBS signals for some common, and …

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 …

In this contribution we consider sparse linear regression problems. It is well known that the
mutual coherence, ie the maximum correlation of the regressors, is important for the ability of …

The maximum absolute correlation between regressors, which is called mutual coherence,
plays an essential role in sparse estimation. A regressor matrix whose columns are highly …

An experiment design procedure for parameter estimation in nonlinear dynamical systems is
presented in this paper. The input to the system is designed in such a way that the …

In this article a new algorithm for the design of stationary input sequences for system
identification is presented. The stationary input signal is generated by optimizing an …