Improving performance and stability of MPC relevant identification methods

Potts, Alain Segundo; Romano, Rodrigo Alvite; Garcia, Cláudio

doi:10.1016/j.conengprac.2013.09.007

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…In this sense, our program provides access to three MRI approaches: the method proposed by Gopaluni et al (2004), based on the direct optimization of the disturbance model and filter W k ; the algorithm proposed by Huang and Wang (1999), based on the prefiltering of the input/output signals; and the method proposed by Potts et al (2014) that gathers the advantages of the two previous methods and ensures the stability of the predictor. The method used to obtain the filter W k defines the characteristic of the different MRI methods.…”

Section: Model Predictive Controller Relevant Identification Methodsmentioning

confidence: 99%

Algorithms and Methods for Identification of Multivariable Plants

Garcia

Potts

Godoy

et al. 2014

Computer Aided Chemical Engineering

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Section: Model Predictive Controller Relevant Identification Methodsmentioning

confidence: 99%

Algorithms and Methods for Identification of Multivariable Plants

Garcia

Potts

Godoy

et al. 2014

Computer Aided Chemical Engineering

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“…Several approaches address the multi-step-ahead identification problem, see e.g. [15], [23], [26], [27], mainly in a stochastic framework. These approaches do not provide a way to quantify the model quality in terms of bounds on the simulation error, which could be directly exploited in robust decision making.…”

Section: Introductionmentioning

confidence: 99%

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Lauricella

Fagiano

2020

IEEE Trans. Automat. Contr.

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The problem of model identification for linear systems is considered, using a finite set of sampled data affected by a bounded measurement noise, with unknown bound. The objective is to identify one-step-ahead models and their accuracy in terms of worst-case simulation error bounds. To do so, the Set Membership identification framework is exploited. Theoretical results are derived, allowing one to estimate the noise bound and system decay rate. Then, these quantities and the data are employed to define the Feasible Parameter Set (FPS), which contains all possible models compatible with the available information.Here, the estimated decay rate is used to refine the standard FPS formulation, by adding constraints that enforce the desired converging behavior of the models' impulse response. Moreover, guaranteed simulation error bounds for an infinite future horizon are derived, improving over recent results pertaining to finite simulation horizon only. These bounds are the basis for a result and method to guarantee asymptotic stability of the identified model. Finally, the desired one-step-ahead model is identified by means of numerical optimization, and the related simulation error bounds are evaluated. Both input-output and state-space model structures are addressed. The approach is showcased on a numerical example and on real-world experimental data of the roll rate dynamics of an autonomous glider.

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“…Early results using this approach require identification at every time step. In later contributions, , input excitation constraint is implemented based on a trigger, activated on poor model prediction. In a recent contribution, in order to avoid additional constraints in MPC, previous data was also used in reidentification step with recent data of the plant.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Model Predictive Batch Process Monitoring and Control

Kheradmandi

Mhaskar

2018

Ind. Eng. Chem. Res.

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The present work addresses the problem of loss of model validity in batch process control via online monitoring and adaptation based model predictive control. To this end, a state space subspace-based model identification method suitable for batch processes is utilized and then a model predictive controller is designed. To monitor model performance, a model validity index is developed for batch processes. In the event of poor prediction (observed via breaching of a threshold by the model validity index), reidentification is triggered to identify a new model and thus adapt the controller. In order to capture the most recent process dynamics, the identification is appropriately designed to emphasize more the recent process data. The efficacy of the proposed method is demonstrated using an electric arc furnace as a simulation test bed.

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Improving performance and stability of MPC relevant identification methods

Cited by 19 publications

References 26 publications

Algorithms and Methods for Identification of Multivariable Plants

Algorithms and Methods for Identification of Multivariable Plants

Set Membership Identification of Linear Systems With Guaranteed Simulation Accuracy

Adaptive Model Predictive Batch Process Monitoring and Control

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