Data driven strategies : theory and applications

Data driven strategies theory and applications

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 r...

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Detalles Bibliográficos
Otros Autores: Wang, Jianhong (Engineering researcher), author (author), Ramírez-Mendoza, Ricardo A., author, Morales-Menéndez, Rubén, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, Florida ; Abingdon, Oxon : CRC Press [2023]
Edición:[First edition]
Materias:
Automatic control > Data processing.
System design > Data processing.
Systems engineering > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009825848106719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Acknowledgments
  • Preface
  • Table of Contents
  • 1. Introduction of Data Driven Strategy
  • 1.1 Introduction
  • 1.2 Outline
  • 1.3 Contributions
  • 2. Data Driven Model Predictive Control
  • 2.1 Introduction
  • 2.2 Application of bounded error identification into model predictive control
  • 2.3 Application of interval predictor model into model predictive control
  • 2.4 Stability analysis in cooperative distributed model predictive control
  • 2.5 Summary
  • 3. Data Driven Identification for Closed Loop System
  • 3.1 Introduction
  • 3.2 Stealth identification strategy for closed loop linear time invariant system
  • 3.3 Performance analysis of closed loop system with a tailor made parameterization
  • 3.4 Minimum variance control strategy for the closed loop system
  • 3.5 Synthesis identification analysis for closed loop system
  • 3.6 Summary
  • 4. Data Driven Model Validation for Closed Loop System
  • 4.1 Introduction
  • 4.2 Model structure validation for closed loop system identification
  • 4.3 Non-asymptotic confidence regions in closed loop model validation
  • 4.4 Further results on model structure validation
  • 4.5 Finite sample properties for closed loop identification
  • 4.6 Summary
  • 5. Data Driven Identification for Nonlinear System
  • 5.1 Introduction
  • 5.2 Parallel distributed estimation for polynomial nonlinear state space models
  • 5.3 Recursive least squares identification for piecewise affine Hammerstein models
  • 5.4 Summary
  • 6. Data Driven Iterative Tuning Control
  • 6.1 Introduction
  • 6.2 Zonotope parameter identification for piecewise affine system
  • 6.3 Iterative correlation tuning control for closed loop linear time invariant system
  • 6.4 Controller design for many variables closed loop system under non-interaction condition.
  • 6.5 One improvement on zonotope guaranteed parameter estimation
  • 6.6 Summary
  • 7. Data Driven Applications
  • 7.1 Introduction
  • 7.2 Applying set membership strategy in state of charge estimation for Lithium-ion battery
  • 7.3 Optimal input signal design for aircraft flutter model parameters identification
  • 7.4 Synthesis cascade estimation for aircraft system identification
  • 7.5 Summary
  • 8. Data Driven Subspace Predictive Control
  • 8.1 Introduction
  • 8.2 Nearest neighbor gradient algorithm in subspace predictive control under fault condition
  • 8.3 Subspace data driven control for linear parameter varying system
  • 8.4 Local polynomial method for frequency response function identification
  • 8.5 Conclusion
  • 9. Conclusions and Outlook
  • 9.1 Introduction
  • 9.2 Outlook
  • Index.

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