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Instructor: Ercan Atam

Institute for Data Science & Artificial Intelligence

Course: DSAI 586– Data-Driven Model Predictive Control

��������������������Lecture 9 � � Some nonlinear SI models, MPC-relevant SI & optimal linear predictors

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List of contents for this lecture

  • NARX, NARMAX, HW model structures as nonlinear SI models

  • LPV-SI and recursive SI as advanced SI approaches

  • MPC-relevant SI

  • Optimal linear predictors.

  • Transformation of polynomial models to state-space form

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Nonlinear ARX (NARX) (1)

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Nonlinear ARX (NARX) (2)

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Nonlinear ARMAX (NARMAX)

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Remarks

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Hammerstein-Wiener (HW) model (1)

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Hammerstein-Wiener (HW) model (2)

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Hammerstein-Wiener (HW) model (3)

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SI for linear parameter-varying (LPV) models (1)

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SI for linear parameter-varying (LPV) models (2)

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SI for linear parameter-varying (LPV) models (3)

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SI for linear parameter-varying (LPV) models (4)

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Examples on LPV/quasi-LPV modelling (1)

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Examples on LPV/quasi-LPV modelling (2)

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Examples on LPV/quasi-LPV modelling (3)

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LPV input-output model example

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Some remarks on LPV-SI

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Recursive SI

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Remarks on recursive SI (1)

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Remarks on recursive SI (2)

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MPC-relevant SI (1)

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MPC-relevant SI (2)

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MPC-relevant SI (3)

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MPC-relevant SI (4)

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MPC-relevant SI (5)

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MPC-relevant SI (6)

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Optimal linear predictors (1)

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Optimal linear predictors (2)

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Optimal linear predictors (3)

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Optimal linear predictors (4)

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Optimal linear predictors (5)

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One-step ahead prediction error calculations for some models (1)

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One-step ahead prediction error calculations for some models (2)

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One-step ahead prediction error calculations for some models (3)

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One-step ahead prediction error calculations for some models (4)

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One-step ahead prediction error calculations for some models (5)

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One-step ahead prediction error calculations for some models (6)

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One-step ahead prediction error calculations for some models (7)

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Optimal N-step ahead linear predictor (1)

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Optimal N-step ahead linear predictor (2)

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Optimal N-step ahead linear predictor (3)

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Optimal N-step ahead linear predictor (4)

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Optimal N-step ahead linear predictor (5)

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Example (1)

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Example (2)

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Transformation of polynomial I/O LTI models to state-space form (1)

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Transformation of polynomial I/O LTI models to state-space form (2)

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Transformation of polynomial I/O LTI models to state-space form (3)

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Transformation of polynomial I/O LTI models to state-space form (4)

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Transformation of polynomial I/O LTI models to state-space form (5)

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Transformation of polynomial I/O LTI models to state-space form (6)

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Transformation of polynomial I/O LTI models to state-space form (7)

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Transformation of polynomial I/O LTI models to state-space form (8)

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Transformation of polynomial I/O LTI models to state-space form (9)

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Optimal prediction for state-space models in innovation form (1)

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Optimal prediction for state-space models in innovation form (2)

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Optimal prediction for state-space models in innovation form (3)

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Appendix: An alternative method for derivation of one-step ahead predictor (1)

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Appendix: An alternative method for derivation of one-step ahead predictor (2)

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Appendix: An alternative method for derivation of one-step ahead predictor (3)

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Appendix: An alternative method for derivation of one-step ahead predictor (4)

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References �(utilized for preparation of lecture notes or MATLAB code)

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  • Chapters 4, 9, 17 of Principles of System Identification: Theory and Practice”, Arun K. Tangirala, CRC Press, 2014.