Data-Driven Modeling, Filtering and Control: Methods and applications

✍ Scribed by Carlo Novara, Simone Formentin

• Publisher: The Institution of Engineering and Technology
• Year: 2019
• Tongue: English
• Leaves: 301
• Series: IET Control, Robotics and Sensors Series, 123
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Contents
1 Introduction
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 nonparametricidentification ofWiener systems
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
2.5.1.1 Performance ofW as a classifier
2.5.1.2 Advantages of using negative sequences during training
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
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
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
5 In situ identification of electrochemical impedance spectra for Li-ion batteries
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
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
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
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
7.3.1.1 Design of L by inverting Ĵ
7.3.1.2 Design of Q based on FRF measurements
7.3.2 Design of learning filter: SISO inversion techniques
7.3.2.1 Approximate inversion
7.3.2.2 H∞-optimal synthesis with preview
7.3.2.3 Stable inversion
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
7.4.5.1 Design of learning filter: MIMO inversion techniques
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
7.6.3.1 Basis functions for MIMO ILC
7.6.3.2 Projection step
7.7 Conclusion and ongoing work
Acknowledgments
References
8 Algorithms for data-driven H∞-norm estimation
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
8.3.3.1 Regret lower bound in ΠSF
8.3.3.2 Regret lower bound in
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
9 A comparative study of VRFT and set-membership data-driven controller design techniques: active suspension tuning case
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
10.1 Introduction
10.2 Background
10.2.1 The Central Limit Theorem
10.2.1.1 Lindeberg–Lévy CLT
10.2.1.2 Lyapunov CLT
10.2.1.3 Lindeberg CLT
10.2.2 Taylor expansion (Taylor series)
10.3 Theoretical analysis
Scenario A. Independent and identically distributed (i.i.d.) samples
Scenario B. Independent and nonidentically distributed (i.n.i.d.) samples
10.4 Numerical studies
10.5 Conclusions and future work
10.5.1 Conclusion
10.5.2 Future work
AppendixA
References
11 A hierarchical approach to data-driven LPV control design of constrained systems
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
12.1 Introduction
12.2 Nonlinear set membership fault detection
12.2.1 Problem formulation
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
12.4.1.1 Noise bounded in l2 norm
12.4.1.2 Noise bounded in l∞ norm
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
12.8.2.1 Global approach
12.8.2.2 Quasi-local approach
12.8.2.3 Local approach
12.9 Conclusions
References
13 Robust data-driven control of systems with nonlinear distortions
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
Back Cover