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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Bibliographic Details
Main Authors Novara, Carlo, Formentin, Simone
Format eBook
LanguageEnglish
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
Edition1
SeriesControl, robotics and sensors series
Subjects
Automatic control
Electronics
Electronics engineering
General References
Software Engineering
System design
Systems engineering
TECHNOLOGY & ENGINEERING
filtering theory
identification
control systems
Online AccessGet full text
ISBN9781785617126
1785617125
DOI10.1049/PBCE123E

Cover

Table of Contents:
  • 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