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.

• Chapter 1: Introduction
• 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 H_∞-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