A General Framework for Predictors Based on Bounding Techniques and Local Approximation

Bravo, José Manuel; Álamo, T.; Vasallo, Manuel Jesús; Gegúndez, M. E.

doi:10.1109/tac.2016.2612538

Cited by 37 publications

(30 citation statements)

References 21 publications

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“…While measurement noise e is discussed in several papers, few consider process noise w . Reference [10] is one exception where both the input and output are subject to noise. Consistency of the estimate method is, however, not discussed in that paper.…”

Section: A Standard Methods and Possible Bias Problemsmentioning

confidence: 99%

Identification of Nonlinear Model with General Disturbances

Wang¹

2018

IOTCC

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The nonlinear model has a linear dynamic system following some static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. For the special case of Gaussian input signals, we estimate the linear part of the Hammerstein model using the Bussgang's classic theorem. For the case with general disturbances, we derive the Maximum Likelihood method. Finally one simulation example is used to prove the efficiency of our theory.

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Section: A Standard Methods and Possible Bias Problemsmentioning

confidence: 99%

Identification of Nonlinear Model with General Disturbances

Wang¹

2018

IOTCC

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“…When the controller takes into account this constraint, the optimal solution mostly satisfies it. Figure 10 shows the average head estimation signal error for the demand node 4, which is calculated as the difference between the real average head, obtained by simulation, and the estimation of the average head, obtained as in (22). We can observe that the error signal has approximately zero mean (0.21%).…”

Section: Examplementioning

confidence: 96%

“…18 A different technique used by Canale et al 19 based on nonlinear set membership, 20 was used to obtain an approximate model with a bound on the worst-case model error that can be used to infer closed-loop-stability properties. Prediction models are also inferred from experimental data of inputs and outputs of the plant in the work of Limon et al 21 Prediction methods can also return an interval obtained from historic input-output measurements that bounds the system output as in the work of Bravo et al 22 A different approach is to completely avoid the model estimation phase. Favoreel et al 23 used this approach to derive, by means of matrix decompositions, an LQG control law directly from data.…”

Section: Introductionmentioning

confidence: 99%

Predictive control of a water distribution system based on process historian data

Salvador

Peña

Ramı́rez

et al. 2019

Optim Control Appl Methods

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Summary In this paper, a heuristic historian data‐based predictive control strategy is presented and used to control a water distribution system simulated using the EPANET software, in particular, the Richmond water distribution system. The control actions are computed based on past historian data. The historian stores closed‐loop operation data of the process with different controllers used in the past, which may not provide sufficient information for a precise system nor controller identification. The proposed predictive controller computes the current control actions as a weighted sum of past control actions so that an estimation of the performance cost over a prediction horizon is minimized. Only a subset of the past control actions in the historian close to the current state of the process is considered in the current control computations to carry out a local linearization. This predictive strategy is well suited to control applications of large and complex processes for which it is difficult to carry out identification experiments such as water distribution systems. In the application example, the trajectories of a set of relay controllers are used through the proposed approach to take into account pressure constraints and periodic references.

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“…El término Kinky Inference hace referencia al resultado del predictor descrito en la ecuación (3). Debido al tipo de interpolación que se hace basada en los datos y la continuidad de Lipschitz, la función resultantef no es diferenciable, sino que presenta una forma angulosa, que propicia el término kinky en inglés.…”

Section: Filtrado Del Predictorunclassified

Control predictivo basado en datos

Manzano¹,

Limón²,

Cantarero³

et al. 2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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Este artículo presenta el diseño estabilizante de un controlador predictivo a partirúnicamente de datos de entrada-salida de un sistema a controlar. El modelo que incluye este controlador es una función no lineal estimada usando una técnica de aprendizaje automático no paramétrica conocida como Kinky Inference. Como primeras pruebas en esta nueva línea de investigación, se diseñan y se prueban controladores en un reactor continuamente agitado, considerando las cuestiones necesarias para una correcta implementación práctica. Palabras clave: Control predictivo; Control basado en datos; Aprendizaje automático; Sistemas no lineales con restricciones.

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A General Framework for Predictors Based on Bounding Techniques and Local Approximation

Cited by 37 publications

References 21 publications

Identification of Nonlinear Model with General Disturbances

Identification of Nonlinear Model with General Disturbances

Predictive control of a water distribution system based on process historian data

Control predictivo basado en datos

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