Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.
Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.
1. Chapter 1: Introduction 2. 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 3. 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
1. Chapter 1: Introduction 2. 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 3. 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
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