Data-Driven Modeling, Filtering and Control

Methods and applications

AvCarlo, Novara,Carlo Novara

Inbunden, Engelska, 2019

Del i serien Control, Robotics and Sensors

1 667 kr

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Beskrivning

The scientific research in many engineering fields has been shifting from traditional first-principle-based to data-driven or evidence-based theories. The latter methods may enable better system design, based on more accurate and verifiable information.In the era of big data, IoT and cyber-physical systems, this subject is of growing importance, as data-driven approaches are key enablers to solve problems that could not be addressed by standard approaches. This book presents a number of innovative data-driven methodologies, complemented by significant application examples, to show the potential offered by the most recent advances in the field. Applicable across a range of disciplines, the topics discussed here will be of interest to scientists, engineers and students in automatic control and learning systems, automotive and aerospace engineering, electrical engineering and signal processing.

Produktinformation

Utgivningsdatum:2019-09-04
Mått:156 x 234 x 18 mm
Vikt:635 g
Format:Inbunden
Språk:Engelska
Serie:Control, Robotics and Sensors
Antal sidor:304
Förlag:Institution of Engineering and Technology
ISBN:9781785617126

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Mer om författaren

Carlo Novara is an Associate Professor at Politecnico di Torino, Italy. He holds a Ph.D. degree in Computer and System Engineering. His research interests include nonlinear and LPV system identification, filtering/estimation, time series prediction, nonlinear control, data-driven methods, Set Membership methods, sparse methods, and automotive, aerospace, biomedical and sustainable energy applications.Simone Formentin is a Tenure-track Assistant Professor at Politecnico di Milano, Italy. He obtained his Ph.D. degree in Information Technology. His research interests include machine learning and automatic control, with a focus on mechatronics and automotive applications.

Innehållsförteckning

Chapter 1: IntroductionPart I: Data-driven modelingChapter 2: A kernel-based approach to supervised nonparametric identification of Wiener systemsChapter 3: Identification of a quasi-LPV model for wing-flutter analysis using machine-learning techniquesChapter 4: Experimental modeling of a web-winding machine: LPV approachesChapter 5: In situ identification of electrochemical impedance spectra for Li-ion batteries Part II: Data-driven filtering and controlChapter 6: Dynamic measurementChapter 7: Multivariable iterative learning control: analysis and designs for engineering applicationsChapter 8: Algorithms for data-driven H∞-norm estimationChapter 9: A comparative study of VRFT and set-membership data-driven controller design techniques: active suspension tuning caseChapter 10: Relative accuracy of two methods for approximating observed Fisher informationChapter 11: A hierarchical approach to data-driven LPV control design of constrained systemsChapter 12: Set membership fault detection for nonlinear dynamic systemsChapter 13: Robust data-driven control of systems with nonlinear distortions