Input design for model order determination in subspace identification

Misra, Pratik; Nikolaou, Michael

doi:10.1002/aic.690490820

Cited by 22 publications

(23 citation statements)

References 15 publications

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“…Note that the PRBS signal was not an optimized choice, but only utilized to sufficiently excite the input signals in order to improve the predictive ability of the model. The identification methodology does stand to gain from past and recent advances in design of signals for identification, and explicit utilization of such techniques remains a subject of future work.…”

Section: Preliminariesmentioning

confidence: 99%

Handling sensor faults in economic model predictive control of batch processes

et al. 2018

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The problem of sensor fault detection and isolation (FDI) and fault‐tolerant economic model predictive control (FT‐EMPC) for batch processes is addressed. To this end, we first model batch processes using subspace‐based system identification techniques. The analytical redundancy within the identified model is subsequently exploited to detect, isolate, and handle the faulty measurements. The reconciled fault‐free measurements are then incorporated in an economic model predictive controller formulation. Simulation case studies involving the application of the proposed data‐driven FDI and FT‐EMPC algorithms to the energy intensive electric arc furnace process illustrate the improvement in economic performance under various fault scenarios. © 2018 American Institute of Chemical Engineers AIChE J, 65: 617–628, 2019

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Section: Preliminariesmentioning

confidence: 99%

Handling sensor faults in economic model predictive control of batch processes

et al. 2018

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“…Therefore, DOE is important for the generation of data that allow accurate estimates of model order. The determination of model order is particularly challenging for ill-conditioned systems [1,3]. Because many real processes are ill-conditioned, it is all the more necessary to develop a DOE framework for accurate estimation of the order of such systems.…”

Section: Doe For Estimation Of System Order In Subspace Identificationmentioning

confidence: 99%

“…x(k + 1) = Ax(k) + Bm(k) + Ke(k) y(k) = Cx(k) + Dm(k) + e(k) (1) where x ∈ η is the state, m ∈ m is the input, y ∈ n is the output and e ∈ n is white noise. Subspace identification (SI) methods [29][30][31][32] estimate the order of a system as in Equation (1) by:…”

Section: Relevant Background On Subspace Identificationmentioning

confidence: 99%

“…where the diagonal matrix Σ N contains "small" singular values that are due to noise and would be identically equal to zero if there were no noise in Equation (1). The matrices that appear in Equation 2are constructed from input and output data, as summarized in Appendix A.…”

Section: Relevant Background On Subspace Identificationmentioning

confidence: 99%

“…DOE that generates data allowing the efficient identification of multivariable models which satisfy these two crucial properties has been the focus of substantial work in recent years [1][2][3][4][5][6][7][8][9][10][11]. Among these publications, the works in [1,2] and [9][10][11] offer rigorous guidelines for addressing the two multivariable model properties mentioned above. Possibly because of the complexity involved in developing the results generated by that work, it appears that these results have remained largely inaccessible to the control community, despite the relative simplicity of corresponding recipes.…”

Section: Introductionmentioning

confidence: 99%

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Design of Experiments for Control-Relevant Multivariable Model Identification: An Overview of Some Basic Recent Developments

et al. 2017

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The effectiveness of model-based multivariable controllers depends on the quality of the model used. In addition to satisfying standard accuracy requirements for model structure and parameter estimates, a model to be used in a controller must also satisfy control-relevant requirements, such as integral controllability. Design of experiments (DOE), which produce data from which control-relevant models can be accurately estimated, may differ from standard DOE. The purpose of this paper is to emphasize this basic principle and to summarize some fundamental results obtained in recent years for DOE in two important cases: Accurate estimation of the order of a multivariable model and efficient identification of a model that satisfies integral controllability; both important for the design of robust model-based controllers. For both cases, we provide an overview of recent results that can be easily incorporated by the final user in related DOE. Computer simulations illustrate outcomes to be anticipated. Finally, opportunities for further development are discussed.

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Modeling and Validation of a Four‐Tank System for Level Control Process Using Black Box and White Box Model Approaches

Subramanian¹,

C²,

Sharmila³

2020

IEEJ Transactions Elec Engng

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Mathematical modeling of a system plays a vital role in controller design, tuning to meet the desired specifications and process parameter optimization. In this paper, different identification algorithms are implemented in the four-tank system to demonstrate the superiority of the recommended subspace system identification method (Numeric Algorithms for Subspace State Space System Identification [N4SID]) for the different input design. A system identification method was utilized to identify the model of a dynamic system. It is modeled using the First-Principle Method (FPM), Prediction Error Method (PEM) and Numeric Algorithms for N4SID method. The identification is performed through MATLAB. The plant input and output is acquired through LabVIEW. The developed models are validated with a new dataset based on the model performance specifications: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Best Fit Rate (BFR). The results show that the proposed N4SID method with the amplitude-modulated pseudorandom binary signal (APRBS) signal is better than the other methods with different inputs.

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Input design for model order determination in subspace identification

Cited by 22 publications

References 15 publications

Handling sensor faults in economic model predictive control of batch processes

Handling sensor faults in economic model predictive control of batch processes

Design of Experiments for Control-Relevant Multivariable Model Identification: An Overview of Some Basic Recent Developments

Modeling and Validation of a Four‐Tank System for Level Control Process Using Black Box and White Box Model Approaches

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