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.
- Part I: Data-driven modeling
- Chapter 2: A kernel-based approach to supervised nonparametric identification of Wiener systems
- Chapter 3: Identification of a quasi-LPV model for wing-flutter analysis using machine-learning techniques
- Chapter 4: Experimental modeling of a web-winding machine: LPV approaches
- Chapter 5: In situ identification of electrochemical impedance spectra for Li-ion batteries
- Part II: Data-driven filtering and control
- Chapter 6: Dynamic measurement
- Chapter 7: Multivariable iterative learning control: analysis and designs for engineering applications
- Chapter 8: Algorithms for data-driven H8-norm estimation
- Chapter 9: A comparative study of VRFT and set-membership data-driven controller design techniques: active suspension tuning case
- Chapter 10: Relative accuracy of two methods for approximating observed Fisher information
- Chapter 11: A hierarchical approach to data-driven LPV control design of constrained systems
- Chapter 12: Set membership fault detection for nonlinear dynamic systems
- Chapter 13: Robust data-driven control of systems with nonlinear distortions
Politecnico di Torino, Italy.
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 Assistant Professor.
Politecnico di Milano, Italy.
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.