RPN tuning strategy for model predictive control

Trierweiler, Jorge Otávio; Farina, Luciano André

doi:10.1016/s0959-1524(02)00093-8

Cited by 63 publications

(36 citation statements)

References 9 publications

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“…The controller parameters are determined using the RPN methodology developed by (Trierweiler and Farina, 2003), the control objective being a decrease of the closed-loop rise time to 1/6 of the open-loop rise time. For testing the closed-loop system, a series of random set point changes is applied to system controlled variable.…”

Section: Methodology and Control Objectivesmentioning

confidence: 99%

NMPC With State-Space Models Obtained Through Linearization on Equilibrium Manifold

Koch

Duraiski

Fernandes

et al. 2006

IFAC Proceedings Volumes

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This paper presents a Nonlinear Model Predictive Control approach for nonlinear state-space models obtained with the modelling and identification technique recently proposed in literature as Linearization on the Equilibrium Manifold (LEM). The predictive controller that will be applied to the LEM uses the Local Linearization on the Trajectory algorithm (LLT) which simulates the nonlinear plant and calculates optimal control actions based on local linearizations around the simulated trajectory by online minimization of an objective function. The proposed combination of the LEM and LLT techniques is tested with two nonlinear SISO systems.

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Section: Methodology and Control Objectivesmentioning

confidence: 99%

NMPC With State-Space Models Obtained Through Linearization on Equilibrium Manifold

Koch

Duraiski

Fernandes

et al. 2006

IFAC Proceedings Volumes

View full text Add to dashboard Cite

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“…Por tal razón se propone que la malla sea inicializada en un valor mínimo de 1 y con valores máximos de 30, conservando las proporcionalidades anteriormente mencionadas para evitar incertidumbres e inestabilidades aportadas por el P. b) Condiciones iniciales para el valor M. Se debe garantizar que el valor de M sea entero debido a que conél se proporcionan los grados de libertad en la selección de futuros controles. Existen diversas propuestas para obtener el valor del M, donde se plantea que para sistemas estables se utilice un valor menor al de P (Genceli and Nikolaou, 1993;Trierweiler and Farina, 2003). Otros trabajos con buenos resultados proponen sintonizar a M con un valor de uno, o inicializar el controlador con M = P, lo cual reduce al mínimo la varianza del controlador (Banerjee and Shah, 1992;Yamuna and Unbehauen, 1997).…”

Section: Definición De Mallasunclassified

Metodología de Ajuste de un NMPC con Sistema de Inferencia Borrosa Takagi Sugeno y Conjuntos Borrosos Multidimensionales para Aplicaciones en Procesos Químicos no Lineales

Morales

Hurtado

Mejia

et al. 2018

Rev. iberoam. autom. inform. ind.

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<p>En este artículo se presenta una metodología para la sintonización de los parámetros de un controlador predictivo no lineal basado en modelo (NMPC) basado en una técnica de optimización, aplicado en el control de procesos químicos no lineales, con el propósito de facilitar la etapa de determinación de los valores de ajuste de este tipo de estrategias de control. Los resultados permiten determinar el desempeño de los NMPC sintonizados con la propuesta metodológica planteada y se validan los resultados con sintonizaciones desarrolladas mediante otras estrategias. La metodología propuesta se aplica al control de la concentración de un reactivo en un tanque reactor continuamente agitado (CSTR).</p>

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“…The various MPC algorithms may use different cost functions for obtaining the control law [49]. MPC controller design or generation involves many parameters that strongly affect performance and robustness of the controller, making controller tuning a nontrivial task [40].…”

Section: Model Predictive Controller (Mpc)mentioning

confidence: 99%

Advanced Control of Continuous Pharmaceutical Tablet Manufacturing Processes

Singh

Velázquez

Sahay

et al. 2016

Methods in Pharmacology and Toxicology

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A novel manufacturing strategy based on continuous processing, integrated with online/inline monitoring tools, coupled with an advanced automatic feedback control system is highly desired for efficient Quality by Design (QbD)-based manufacturing of the next generation of pharmaceutical products with optimal consumption of time, space and resources. In this work, an advanced hybrid MPC-PID control system as well as a simpler PID controller for a direct compaction continuous tablet manufacturing process has been designed and implemented for a pilot-scale pharmaceutical process. An NIR sensor, an online NIR prediction tool, a PAT data management tool, an OPC communication protocol, a standard control platform and control hardware have been used to close the control loop. A systematic methodology to design and implement the control system has been also proposed. A control framework with features such as the option to run the plant in open-loop as well as in a closed-loop scenario has been developed. Furthermore, within the closed-loop scenario, options for a simpler PID, a dead time compensator (Smith predictor) as well as an advanced model predictive controller have been included. The feature to run the control strategy in simulation mode has been added to the control platform to facilitate virtual control system design and performance evaluation. Two case studies involving a direct compaction continuous tablet manufacturing process have been considered to demonstrate the closed-loop operation. Case Study 1 was completed at Rutgers University and constituted the use of a continuous cylindrical blender with a rotating screw. Case Study 2 was based on a continuous tumble mixer and was completed at the University of Puerto Rico-Mayaguez Campus (UPRM).

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RPN tuning strategy for model predictive control

Cited by 63 publications

References 9 publications

NMPC With State-Space Models Obtained Through Linearization on Equilibrium Manifold

NMPC With State-Space Models Obtained Through Linearization on Equilibrium Manifold

Metodología de Ajuste de un NMPC con Sistema de Inferencia Borrosa Takagi Sugeno y Conjuntos Borrosos Multidimensionales para Aplicaciones en Procesos Químicos no Lineales

Advanced Control of Continuous Pharmaceutical Tablet Manufacturing Processes

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