Table Of ContentData Driven Strategies:
Theory and Applications
Wang Jianhong
School of Engineering and Sciences
Tecnologico de Monterrey, Monterrey, Mexico
Ricardo A. Ramirez-Mendoza
School of Engineering and Sciences
Tecnologico de Monterrey, Monterrey, Mexico
Ruben Morales-Menendez
School of Engineering and Sciences
Tecnologico de Monterrey, Monterrey, Mexico
First edition published 2023
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© 2023 Wang Jianhong, Ricardo A. Ramirez-Mendoza and Ruben Morales-Menendez
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Library of Congress Cataloging-in-Publication Data (applied for)
ISBN: 978-0-367-74659-9 (hbk)
ISBN: 978-0-367-75008-4 (pbk)
ISBN: 978-1-003-16070-0 (ebk)
DOI: 10.1201/9781003160700
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Acknowledgments
We are grateful to a number of outstanding people who we have the opportunity to
meet, learn from and work with during our PhD, postdoctoral and whose support
and encouragement have made this exciting and challenging journey possible.
Tecnologico de Monterrey is one top university all over the world. There we
can calm down to our teaching and research about advanced control theory and
engineering. Every day we always have coffee and talk about our new ideas about
the same topic. Due to the nice scenes and mountains, they often give us some
inspirations. Without these natural sceneries and support from Tecnologico de
Monterrey, we could not do any new directions for later research.
Jiangxi University of Science and Technology is one of the best places the first
author could have hoped for doing his teaching and research, and he acknowledges
the support from Jiangxi University of Science and Technology, which is an
exceptional lab and research environment with amazing and interesting people.
Finally, our special thanks go to our family and friends, without them we
would not have made it here. Thanks for understanding that time was often scarce
and for always encouraging us. We want to thank our parents and families for their
love and endless support, thanks for always being there for us.
After all, finishing this book is not the end of a journey, but the beginning of a
much bigger one. In the next years, we will try our best to combine the functional
analysis and geometry into system identification and advanced control theory.
Maybe we can open some new directions in these research fields.
Wang Jianhong
Ricardo A. Ramirez-Mendoza
Ruben Morales-Menendez
Preface
One of the motivations behind this book was to collect together the many
results of the direct data driven strategy from two different points, i.e. data
driven identification and data driven control. For this reason, we have, rather
unashamedly, included a number of ideas that were developed at Tecnologico
de Monterrey and in this sense some of the discussions in the book are included
as background material that some readers may wish to skip on an initial reading.
This book includes all of our recent contributions about direct data driven
strategy for control and identification respectively. These recent contributions are
all published in some international journals. On the other hand, we have clearly
attempted to incorporate all the major developments in this field, some of which
are rather recent and as yet may not be widely known.
Over the past few decades the state of this direct data driven strategy has come
close to our living information age. But it is still nowhere near closed enough for
many control applications. In this respect the field is wide open for researchers to
come up with fresh ideas that will bridge the gap between ideal performance and
achievable practice.
Wang Jianhong
Ricardo A. Ramirez-Mendoza
Ruben Morales-Menendez
Contents
Acknowledgments iii
Preface v
1. Introduction of Data Driven Strategy 1
1.1 Introduction 1
1.2 Outline 4
1.3 Contributions 6
2. Data Driven Model Predictive Control 8
2.1 Introduction 8
2.2 Application of bounded error identification into model
predictive control 9
2.3 Application of interval predictor model into model
predictive control 24
2.4 Stability analysis in cooperative distributed model
predictive control 41
2.5 Summary 52
3. Data Driven Identification for Closed Loop System 54
3.1 Introduction 54
3.2 Stealth identification strategy for closed loop linear time
invariant system 55
3.3 Performance analysis of closed loop system with a tailor made
parameterization 72
3.4 Minimum variance control strategy for the closed loop system 87
3.5 Synthesis identification analysis for closed loop system 96
3.6 Summary 106
viii Contents
4. Data Driven Model Validation for Closed Loop System 108
4.1 Introduction 108
4.2 Model structure validation for closed loop system identification 109
4.3 Non-asymptotic confidence regions in closed loop
model validation 121
4.4 Further results on model structure validation 125
4.5 Finite sample properties for closed loop identification 132
4.6 Summary 145
5. Data Driven Identification for Nonlinear System 147
5.1 Introduction 147
5.2 Parallel distributed estimation for polynomial nonlinear state
space models 148
5.3 Recursive least squares identification for piecewise affine
Hammerstein models 166
5.4 Summary 181
6. Data Driven Iterative Tuning Control 184
6.1 Introduction 184
6.2 Zonotope parameter identification for piecewise affine system 185
6.3 Iterative correlation tuning control for closed loop linear time
invariant system 198
6.4 Controller design for many variables closed loop system under
non-interaction condition 215
6.5 One improvement on zonotope guaranteed parameter estimation 226
6.6 Summary 229
7. Data Driven Applications 232
7.1 Introduction 232
7.2 Applying set membership strategy in state of charge estimation
for Lithium-ion battery 233
7.3 Optimal input signal design for aircraft flutter model parameters
identification 255
7.4 Synthesis cascade estimation for aircraft system identification 286
7.5 Summary 302
8. Data Driven Subspace Predictive Control 305
8.1 Introduction 305
8.2 Nearest neighbor gradient algorithm in subspace predictive
control under fault condition 306
Contents ix
8.3 Subspace data driven control for linear parameter varying system 319
8.4 Local polynomial method for frequency response function
identification 334
8.5 Conclusion 346
9. Conclusions and Outlook 348
9.1 Introduction 348
9.2 Outlook 350
Index 351
