Data-Driven Modeling, Filtering and Control Methods and applications
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...
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| Main Authors | , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Stevenage
The Institution of Engineering and Technology
2019
Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
| Edition | 1 |
| Series | Control, robotics and sensors series |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781785617126 1785617125 |
| DOI | 10.1049/PBCE123E |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. 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. |
| Author | Novara Carlo Formentin Simone |
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| DOI | 10.1049/PBCE123E |
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| Editor | Formentin, Simone Novara, Carlo |
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| Keywords | filtering theory identification control systems |
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| PublicationSeriesTitle | Control, robotics and sensors series |
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| Publisher | The Institution of Engineering and Technology Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
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| Snippet | The scientific research in many engineering fields has been shifting from traditional first-principle-based to data-driven or evidence-based theories. The... Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses... |
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| SubjectTerms | Automatic control Electronics Electronics engineering General References Software Engineering System design Systems engineering TECHNOLOGY & ENGINEERING |
| Subtitle | Methods and applications |
| TableOfContents | 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 -- -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 -- Title Page Table of Contents 1. Introduction 2. A Kernel-Based Approach to Supervised Nonparametric Identification of Wiener Systems 3. Identification of a Quasi-LPV Model for Wing-Flutter Analysis Using Machine-Learning Techniques 4. Experimental Modeling of a Web-Winding Machine: LPV Approaches 5. In situ Identification of Electrochemical Impedance Spectra for Li-Ion Batteries 6. Dynamic Measurement 7. Multivariable Iterative Learning Control: Analysis and Designs for Engineering Applications 8. Algorithms for Data-Driven ∞-Norm Estimation 9. A Comparative Study of VRFT and Set-Membership Data-Driven Controller Design Techniques: Active Suspension Tuning Case 10. Relative Accuracy of Two Methods for Approximating Observed Fisher Information 11. A Hierarchical Approach to Data-Driven LPV Control Design of Constrained Systems 12. Set Membership Fault Detection for Nonlinear Dynamic Systems 13. Robust Data-Driven Control of Systems with Nonlinear Distortions Index 9. A comparative study of VRFT and set-membership data-driven controller design techniques: active suspension tuning case / Freddy Valderrama and Fredy Ruiz -- 9.1 Introduction -- 9.2 Problem statement -- 9.3 Controller tuning from data -- 9.3.1 Set-membership approach -- 9.3.2 Tuning via VRFT -- 9.4 Active suspension tuning case study -- 9.4.1 Controller tuning problem -- 9.4.2 Monte Carlo experiment -- 9.4.3 Process disturbance experiment -- 9.5 Conclusions -- Acknowledgment -- References -- 10. Relative accuracy of two methods for approximating observed fisher information / Shenghan Guo and James C. Spall -- 10.1 Introduction -- 10.2 Background -- 10.2.1 The Central Limit Theorem -- 10.2.2 Taylor expansion (Taylor series) -- 10.3 Theoretical analysis -- 10.4 Numerical studies -- 10.5 Conclusions and future work -- 10.5.1 Conclusion -- 10.5.2 Future work -- References -- 11. A hierarchical approach to data-driven LPV control design of constrained systems / Dario Piga, Simone Formentin, Roland Toth, Alberto Bemporad, and Sergio Matteo Savaresi -- 11.1 Introduction -- 11.2 Related works -- 11.3 Problem formulation -- 11.4 A hierarchical approach -- 11.5 Data-driven inner controller design -- 11.5.1 Inversion of the reference model -- 11.5.2 Data-driven controller design -- 11.5.3 Dual problem -- 11.6 Outer controller design -- 11.7 Case study: servo-positioning system -- 11.7.1 System description -- 11.7.2 Desired inner closed-loop behavior -- 11.7.3 Inner controller design -- 11.7.4 Achieved inner closed-loop behavior -- 11.7.5 Outer controller design -- 11.8 Conclusions -- References -- 12. Set membership fault detection for nonlinear dynamic systems / Milad Karimshoushtari, Luigi Spagnolo, and Carlo Novara -- 12.1 Introduction -- 12.2 Nonlinear set membership fault detection -- 12.2.1 Problem formulation 5. In situ identification of electrochemical impedance spectra for Li-ion batteries / Tyrone Vincent, Peter J.Weddle, Aleksei La Rue, and Robert J. Kee -- 5.1 Introduction -- 5.1.1 Motivation: understanding battery dynamics -- 5.1.2 Traditional methods for measuring EIS -- 5.1.3 Related work -- 5.1.4 Outline of approach -- 5.2 Method -- 5.2.1 Data collection -- 5.2.2 Identification -- 5.2.3 Frequency response and uncertainty estimation -- 5.2.4 Combined frequency response estimate -- 5.2.5 Review of frequency identification method -- 5.3 Example experimental results -- 5.3.1 Experimental conditions for PRBS perturbation -- 5.3.2 Experimental conditions for sinusoidal perturbation -- 5.3.3 Results -- Acknowledgments -- References -- Part II. Data-driven filtering and control -- 6. Dynamic measurement / Ivan Markovsky -- 6.1 Introduction -- 6.1.1 Literature review -- 6.2 Problem setup -- 6.3 Model-based vs data-driven approaches -- 6.4 Maximum-likelihood data-driven estimation method -- 6.5 Examples -- 6.5.1 Methods and evaluation criterion -- 6.5.2 Example of temperature measurement -- 6.5.3 Example of mass measurement -- 6.5.4 Results -- 6.6 Conclusions and discussion -- Acknowledgments -- References -- 7. Multivariable iterative learning control: analysis and designs for engineering applications / Lennart Blanken, Jurgen van Zundert, Robin de Rozario, Nard Strijbosch, and Tom Oomen -- 7.1 Introduction -- 7.1.1 ILC for complex engineering applications -- 7.1.2 Design requirements for high-precision applications -- 7.1.3 Robust multivariable ILC design: the importance of (under) modeling (R1-R2) -- 7.1.4 Model-free iterative learning (R2) -- 7.1.5 ILC for varying tasks (R3) -- 7.1.6 Contributions -- 7.1.7 Notation -- 7.2 System description and problem formulation -- 7.2.1 ILC framework -- 7.2.2 Convergence and performance 12.3 Nonlinear set membership identification: global approach -- 12.3.1 Interval estimates -- 12.4 Nonlinear set membership identification: local approach -- 12.4.1 Interval estimates -- 12.4.2 Local approach-identification algorithms -- 12.5 Nonlinear set membership identification: quasi-local approach -- 12.5.1 Interval estimates -- 12.6 Parameter estimation and adaptive set membership model -- 12.6.1 Parameter estimation -- 12.6.2 Adaptive set membership model -- 12.7 Summary of set membership fault-detection procedure -- 12.8 Example: fault detection for a drone actuator -- 12.8.1 Experimental setup -- 12.8.2 Nonlinear set membership fault detection -- 12.9 Conclusions -- References -- 13. Robust data-driven control of systems with nonlinear distortions / Achille Nicoletti, Christoph Kammer, and Alireza Karimi -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Class of nonlinearities -- 13.2.2 Class of controllers -- 13.3 Frequency-domain identification -- 13.3.1 Stable plant -- 13.3.2 Unstable plant -- 13.3.3 Uncertainty filters for coprime factorisation -- 13.4 Robust controller design -- 13.4.1 Control performance -- 13.4.2 Convex formulation for robust performance -- 13.4.3 Controller design by convex optimisation -- 13.5 Case study -- 13.5.1 System description -- 13.5.2 Identification experiments -- 13.5.3 Performance specification -- 13.5.4 Experimental results -- 13.6 Conclusion -- References -- Index 7.2.3 Design conditions for convergence and performance -- 7.2.4 Modeling considerations -- 7.3 ILC design-the SISO case -- 7.3.1 Manual design in the frequency domain -- 7.3.2 Design of learning filter: SISO inversion techniques -- 7.3.3 Toward MIMO ILC design: naive SISO design for MIMO systems -- 7.4 ILC Design-the MIMO case -- 7.4.1 Interaction analysis -- 7.4.2 Decoupling transformations -- 7.4.3 Robust multi-loop SISO design -- 7.4.4 Robust decentralized MIMO design -- 7.4.5 Centralized MIMO design -- 7.5 Iterative inversion-based control: avoiding the need for parametric models -- 7.5.1 System description and procedure -- 7.5.2 Convergence analysis, modeling requirements and design -- 7.6 ILC with basis functions: enhancing flexibility to varying tasks -- 7.6.1 Flexibility in ILC-case study on a flatbed printer -- 7.6.2 Basis functions in ILC -- 7.6.3 Projection-based MIMO ILC with basis functions: frequency-domain design -- 7.7 Conclusion and ongoing work -- Acknowledgments -- References -- 8. Algorithms for data-driven H∞-norm estimation / Cristian R. Rojas and Matias I. Müller -- 8.1 Motivation and problem formulation -- 8.1.1 Problem formulation -- 8.2 Power iterations -- 8.2.1 Power iterations in linear algebra -- 8.2.2 Power iterations for linear dynamical systems -- 8.2.3 An example -- 8.3 Multi-armed bandits -- 8.3.1 Stochastic multi-arm bandits in a nutshell -- 8.3.2 H∞-norm estimation as an MAB problem -- 8.3.3 Regret lower bounds and optimal algorithms -- 8.3.4 The weighted Thompson sampling (WTS) algorithm -- 8.3.5 An illustrative example -- 8.4 Extensions to nonlinear systems -- 8.4.1 de Bruijn graphs and prime cycles -- 8.4.2 Finding the optimal stationary sequence -- 8.5 Discussion and extensions -- References Intro -- Contents -- 1. Introduction / Simone Formentin and Carlo Novara -- 1.1 Introduction -- 1.2 State-of-the-art -- 1.3 Goals and structure of the book -- References -- Part I. Data-driven modeling -- 2. A kernel-based approach to supervised nonparametric identification ofWiener systems / Fei Xiong, Yongfang Cheng, Octavia Camps, Mario Sznaier, and Constantino Lagoa -- 2.1 Introduction and motivation -- 2.2 Preliminaries -- 2.2.1 Notation and definitions -- 2.2.2 Solving polynomial optimization problems via convex optimization -- 2.2.3 Exploiting sparsity in polynomial optimization -- 2.3 Problem statement -- 2.4 Maximum margin Hankel classifiers -- 2.4.1 Further computational complexity reduction -- 2.4.2 Exploiting sparsity -- 2.5 Examples -- 2.5.1 Synthetic data -- 2.5.2 Application: activity recognition from video data -- 2.6 Conclusions -- Acknowledgments -- References -- 3. Identification of a quasi-LPV model for wing-flutter analysis using machine-learning techniques / Rodrigo Alvite Romano, Marcelo Mendes Lafetá Lima, Paulo Lopes dos Santos, and Teresa Paula Azevedo Perdicoúlis -- 3.1 Introduction -- 3.2 LPV state-space model parameterization -- 3.3 Model estimation -- 3.3.1 Parameter reconstruction -- 3.4 Ensemble estimation approach -- 3.5 Wing-flutter model identification -- 3.6 Concluding remarks -- References -- 4. Experimental modeling of a web-winding machine: LPV approaches / Jose Vuelvas, Fredy Ruiz, and Carlo Novara -- 4.1 Introduction -- 4.2 Sparse set membership identification of state-space LPV systems -- 4.3 Interpolated identification of state-space LPV systems -- 4.4 Web-winding system identification -- 4.4.1 The web-winding system -- 4.4.2 Experiment description -- 4.4.3 Sparse set membership LPV model -- 4.4.4 Interpolated LPV model -- 4.4.5 Model validation and results -- 4.5 Conclusion -- References |
| Title | Data-Driven Modeling, Filtering and Control |
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