System identification is the art and science of building mathematical models of dynamic
systems from observed input–output data. It can be seen as the interface between the real …

Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter
estimation in data-driven process identification. EM is an algorithm for maximum likelihood …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

Data-driven transformations that reformulate nonlinear systems in a linear framework have
the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

Gain scheduling for nonlinear controller design is described in terms of general features of
the approach and in terms of early examples of applications in flight control and automotive …

Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has
become a promising system theoretical approach to handle the control of mildly nonlinear …

This paper describes a model identification procedure for identifying an electro-thermal
model of lithium ion batteries used in automotive applications. The dynamic model structure …

Ball screw feed-drive system (BSFDS) is the precision transmission mechanism widely used
in micron-scale positioning or motion trajectory control. Its desired specifications including …

In this paper we present a novel algorithm to identify LPV systems with affine parameter
dependence operating under open-and closed-loop conditions. A factorization is introduced …