book

Data Driven Strategies

Name: Data Driven Strategies
ISBN: 9781000860290

by Wang Jianhong, Ricardo A. Ramirez-Mendoza, Ruben Morales-Menendez

March 2023

Intermediate to advanced

362 pages

8h 52m

English

CRC Press

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Cover Page
Title Page
Copyright Page
Acknowledgements
Preface
Contents
1 Introduction of Data Driven Strategy
1.1 Introduction1.2 Outline1.3 Contributions
2 Data Driven Model Predictive Control
2.1 Introduction2.2 Application of bounded error identification into model predictive control2.1.1 Problem formulation2.2.2 Predictor based on bounded error identification2.2.3 Application bounded error identification into model predictive control2.2.4 Simulation example2.2.5 Conclusion2.3 Application of interval predictor model into model predictive control2.3.1 Interval predictor model2.3.2 One choice of a fixed non-negative number2.3.3 Newton method for interval predictor model2.3.4 Application interval predictor model into model predictive control2.3.5 Simulation examples2.4 Stability analysis in cooperative distributed model predictive control2.4.1 Preliminaries2.4.2 Distributed discrete time constrained LTI system2.4.3 MPC scheme2.4.4 Stability analysis based on LMIs2.4.5 Conclusion2.5 SummaryReferences
3 Data Driven Identification for Closed Loop System
3.1 Introduction3.2 Stealth identification strategy for closed loop linear time invariant system3.2.1 Closed loop system description3.2.2 Classical prediction error identification3.2.3 Stealth identification3.2.4 Convergence condition in stealth identification3.2.5 Simulation example3.2.6 Conclusion3.3 Performance analysis of closed loop system with a tailor made parameterization3.3.1 Closed loop system description3.3.2 Confidence interval analysis with a tailor made parameterization3.3.3 Performance analysis only on one transfer function3.3.4 Performance analysis on one transfer function matrix3.3.5 Conclusions3.4 Minimum variance control strategy for the closed loop system3.4.1 Minimum variance control for general form3.4.2 Minimum variance control for rational transfer function form3.4.3 Main results3.4.4 Extending results3.4.5 Conclusion3.5 Synthesis identification analysis for closed loop system3.5.1 Closed loop system structure3.5.2 Prediction error identification reviewed3.5.3 Synthesis identification analysis3.5.4 Replace nonlinear controller with linear equivalent controller3.5.5 Conclusion3.6 SummaryReferences
4 Data Driven Model Validation for Closed Loop System
4.1 Introduction4.2 Model structure validation for closed loop system identification4.2.1 Problem description4.2.2 Confidence region test of model parameter4.2.3 Confidence region test of cross correlation function4.2.4 Shaping filter test4.2.5 Conclusion4.3 Non-asymptotic confidence regions in closed loop model validation4.4 Further results on model structure validation4.4.1 One bound on model error4.5 Finite sample properties for closed loop identification4.5.1 Closed loop system identification4.5.2 Asymptotic analysis4.5.3 Finite sample analysis4.5.4 Conclusion4.6 SummaryReferences

5 Data Driven Identification for Nonlinear System
5.1 Introduction5.2 Parallel distributed estimation for polynomial nonlinear state space models5.2.1 Polynomial nonlinear state space model5.2.2 Transformation of nonlinear feedback system5.2.3 Transformation of block structure nonlinear system5.2.4 Parallel distributed identification for polynomial nonlinear state space models5.2.5 Simulation example5.3 Recursive least squares identification for piecewise affine Hammerstein models5.3.1 Piecewise affine Hammerstein models5.3.2 Equivalence between piecewise affine nonlinear functions5.3.3 Recursive identification of unknown parameters5.3.4 Conclusion5.4 SummaryReferences
6 Data Driven Iterative Tuning Control
6.1 Introduction6.2 Zonotope parameter identification for piecewise affine system6.2.1 Piecewise affine system6.2.2 Multi-class classification process6.2.3 Zonotope parameter identification algorithm6.2.4 Simulation example6.2.5 Conclusion6.3.1 Model description6.3.2 Iterative correlation tuning control6.3.3 Gradient algorithm for identifying unknown parameter vector6.3.4 Simulation example6.3.5 Conclusion6.4 Controller design for many variables closed loop system under non-interaction condition6.4.1 Closed loop system with many variables6.4.2 Conditions for non-interaction6.4.3 Designing controllers6.4.4 Conclusion6.5 One improvement on zonotope guaranteed parameter estimation6.5.1 Problem formulation6.5.2 Some improvements on interval analysis6.6 SummaryReferences
7 Data Driven Applications
7.1 Introduction7.2 Applying set membership strategy in state of charge estimation for Lithium-ion battery7.2.1 Battery modelling7.2.2 Interval estimation for SOC7.2.3 Ellipsoid estimation for SOC7.2.4 Simulation example7.2.5 Conclusion7.3 Optimal input signal design for aircraft flutter model parameters identification7.3.1 Aircraft flutter model7.3.2 Optimal input signal design for statistical noise7.3.3 Optimal input signal design for bounded noise7.3.4 Numerical examples7.3.5 Conclusion7.4 Synthesis cascade estimation for aircraft system identification7.4.1 Cascade system7.4.2 Cascade system identification7.4.3 Model structure validation process7.4.4 Simulation examples7.4.5 Conclusion7.5 SummaryReferences
8 Data Driven Subspace Predictive Control
8.1 Introduction8.2 Nearest neighbor gradient algorithm in subspace predictive control under fault condition8.2.1 Problem description8.2.2 Fault estimation8.2.3 Output estimation in subspace predictive control8.2.4 Residual analysis8.2.5 Nearest neighbor gradient algorithm for subspace predictive control8.2.6 Conclusion8.3 Subspace data driven control for linear parameter varying system8.3.1 Linear parameter varying system8.3.2 Subspace data driven control strategy8.3.3 Parallel distribution algorithm8.3.4 Simulation8.3.5 Conclusion8.4 Local polynomial method for frequency response function identification8.4.1 Problem description8.4.2 Local polynomial method8.4.3 Constrained local polynomial method8.4.4 Simulation example8.4.5 Conclusion8.5 ConclusionReferences
9 Conclusions and Outlook
9.1 Introduction9.2 Outlook
Index

Overview

Finding exciting and efficient ways to integrate data into control theory has been a problem of great interest. As most of the classical contributions in control strategy rely on model description, the issue of finding such a model from measured data, i.e., system identification, has become mature research filed.

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