Data Driven Strategies: Theory and Applications 1st Edition by Ricardo 9781000860290 1000860299

Data Driven Strategies: Theory and Applications 1st Edition Ricardo A. Ramirez-Mendoza – Ebook Instant Download/Delivery ISBN(s):9781000860290, 1000860299

Product details:

• ISBN 10:1000860299
• ISBN 13:9781000860290
• Author: Ricardo 

Data Driven Strategies
Theory and Applications

Table 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
2.1.1 Problem formulation
2.2.2 Predictor based on bounded error identification
2.2.3 Application bounded error identification into model predictive control
2.2.4 Simulation example
2.2.5 Conclusion
2.3 Application of interval predictor model into model predictive control
2.3.1 Interval predictor model
2.3.2 One choice of a fixed non-negative number
2.3.3 Newton method for interval predictor model
2.3.4 Application interval predictor model into model predictive control
2.3.5 Simulation examples
2.4 Stability analysis in cooperative distributed model predictive control
2.4.1 Preliminaries
2.4.2 Distributed discrete time constrained LTI system
2.4.3 MPC scheme
2.4.4 Stability analysis based on LMIs
2.4.5 Conclusion
2.5 Summary
References
3 Data Driven Identification for Closed Loop System
3.1 Introduction
3.2 Stealth identification strategy for closed loop linear time invariant system
3.2.1 Closed loop system description
3.2.2 Classical prediction error identification
3.2.3 Stealth identification
3.2.4 Convergence condition in stealth identification
3.2.5 Simulation example
3.2.6 Conclusion
3.3 Performance analysis of closed loop system with a tailor made parameterization
3.3.1 Closed loop system description
3.3.2 Confidence interval analysis with a tailor made parameterization
3.3.3 Performance analysis only on one transfer function
3.3.4 Performance analysis on one transfer function matrix
3.3.5 Conclusions
3.4 Minimum variance control strategy for the closed loop system
3.4.1 Minimum variance control for general form
3.4.2 Minimum variance control for rational transfer function form
3.4.3 Main results
3.4.4 Extending results
3.4.5 Conclusion
3.5 Synthesis identification analysis for closed loop system
3.5.1 Closed loop system structure
3.5.2 Prediction error identification reviewed
3.5.3 Synthesis identification analysis
3.5.4 Replace nonlinear controller with linear equivalent controller
3.5.5 Conclusion
3.6 Summary
References
4 Data Driven Model Validation for Closed Loop System
4.1 Introduction
4.2 Model structure validation for closed loop system identification
4.2.1 Problem description
4.2.2 Confidence region test of model parameter
4.2.3 Confidence region test of cross correlation function
4.2.4 Shaping filter test
4.2.5 Conclusion
4.3 Non-asymptotic confidence regions in closed loop model validation
4.4 Further results on model structure validation
4.4.1 One bound on model error
4.5 Finite sample properties for closed loop identification
4.5.1 Closed loop system identification
4.5.2 Asymptotic analysis
4.5.3 Finite sample analysis
4.5.4 Conclusion
4.6 Summary
References
5 Data Driven Identification for Nonlinear System
5.1 Introduction
5.2 Parallel distributed estimation for polynomial nonlinear state space models
5.2.1 Polynomial nonlinear state space model
5.2.2 Transformation of nonlinear feedback system
5.2.3 Transformation of block structure nonlinear system
5.2.4 Parallel distributed identification for polynomial nonlinear state space models
5.2.5 Simulation example
5.3 Recursive least squares identification for piecewise affine Hammerstein models
5.3.1 Piecewise affine Hammerstein models
5.3.2 Equivalence between piecewise affine nonlinear functions
5.3.3 Recursive identification of unknown parameters
5.3.4 Conclusion
5.4 Summary
References
6 Data Driven Iterative Tuning Control
6.1 Introduction
6.2 Zonotope parameter identification for piecewise affine system
6.2.1 Piecewise affine system
6.2.2 Multi-class classification process
6.2.3 Zonotope parameter identification algorithm
6.2.4 Simulation example
6.2.5 Conclusion
6.3.1 Model description
6.3.2 Iterative correlation tuning control
6.3.3 Gradient algorithm for identifying unknown parameter vector
6.3.4 Simulation example
6.3.5 Conclusion
6.4 Controller design for many variables closed loop system under non-interaction condition
6.4.1 Closed loop system with many variables
6.4.2 Conditions for non-interaction
6.4.3 Designing controllers
6.4.4 Conclusion
6.5 One improvement on zonotope guaranteed parameter estimation
6.5.1 Problem formulation
6.5.2 Some improvements on interval analysis
6.6 Summary
References
7 Data Driven Applications
7.1 Introduction
7.2 Applying set membership strategy in state of charge estimation for Lithium-ion battery
7.2.1 Battery modelling
7.2.2 Interval estimation for SOC
7.2.3 Ellipsoid estimation for SOC
7.2.4 Simulation example
7.2.5 Conclusion
7.3 Optimal input signal design for aircraft flutter model parameters identification
7.3.1 Aircraft flutter model
7.3.2 Optimal input signal design for statistical noise
7.3.3 Optimal input signal design for bounded noise
7.3.4 Numerical examples
7.3.5 Conclusion
7.4 Synthesis cascade estimation for aircraft system identification
7.4.1 Cascade system
7.4.2 Cascade system identification
7.4.3 Model structure validation process
7.4.4 Simulation examples
7.4.5 Conclusion
7.5 Summary
References
8 Data Driven Subspace Predictive Control
8.1 Introduction
8.2 Nearest neighbor gradient algorithm in subspace predictive control under fault condition
8.2.1 Problem description
8.2.2 Fault estimation
8.2.3 Output estimation in subspace predictive control
8.2.4 Residual analysis
8.2.5 Nearest neighbor gradient algorithm for subspace predictive control
8.2.6 Conclusion
8.3 Subspace data driven control for linear parameter varying system
8.3.1 Linear parameter varying system
8.3.2 Subspace data driven control strategy
8.3.3 Parallel distribution algorithm
8.3.4 Simulation
8.3.5 Conclusion
8.4 Local polynomial method for frequency response function identification
8.4.1 Problem description
8.4.2 Local polynomial method
8.4.3 Constrained local polynomial method
8.4.4 Simulation example
8.4.5 Conclusion
8.5 Conclusion
References
9 Conclusions and Outlook
9.1 Introduction
9.2 Outlook

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