Application of iterative nonlinear model predictive control to a batch pilot reactor

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)The aim of this article is to present the Iterative Model Predictive Controller, inmpc, as a good candidate to control chemical batch reactors. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (mpc) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. The controller is tested on a batch pilot reactor, and a comparison with an Iterative Learning Controller (ilc) is made. Under input constraints and for this nonlinear plant, a fast convergence rate is obtained with the proposed controller, showing good operational results. Although the controller is designed for discrete-time systems, it is a necessary condition that the continuous-time model does not present blow-up characteristics. The batch pilot reactor emulates an exothermal chemical reaction by means of electrical heating

Iterative nonlinear model predictive control of a PH reactor. A comparative analysis

IFAC WORLD CONGRESS (16) (16.2005.PRAGA, REPÚBLICA CHECA)This paper describes the control of a batch pH reactor by a nonlinear predictive controller that improves performance by using data of past batches. The control strategy combines the feedback features of a nonlinear predictive controller with the learning capabilities of run-to-run control. The inclusion of real-time data collected during the on-going batch run in addition to those from the past runs make the control strategy capable not only of eliminating repeated errors but also of responding to new disturbances that occur during the run. The paper uses these ideas to devise an integrated controller that increases the capabilities of Nonlinear Model Predictive Control (NMPC) with batch-wise learning. This controller tries to improve existing strategies by the use of a nonlinear controller devised along the last-run trajectory as well as by the inclusion of filters. A comparison with a similar controller based upon a linear model is performed. Simulation results are presented in order to illustrate performance improvements that can be achieved by the new method over the conventional iterative controllers. Although the controller is designed for discrete-time systems, it can be applied to stable continuous plants after discretization

Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis

This paper presents the application of Iterative Nonlinear Model Predictive Control, INMPC, to a semibatch chemical reactor. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (MPC) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. A stability proof with unitary control horizon is given for nonlinear plants that are affine in control and have linear output map. The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear non-iterative MPC controller. The controller has applications in repetitive disturbance rejection, because they do not modify the model for control purposes. In this application, some experiments with a disturbance in inlet water temperature has been performed, getting good results.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

Control predictivo-interactivo basado en modelo y aplicado a procesos por lotes

Este artículo presenta una solución al control de procesos por lotes basado en un esquema predictivo no lineal. El controlador propuesto incluye características propias de control iterativo, como es la capacidad de cancelar las perturbaciones que se repiten. El controlador se basa en un modelo del proceso, y es capaz de aprender de un lote al siguiente. Se demuestra que el controlador propuesto es capaz de estabilizar sistemas lineales, y también cualquier planta no lineal y afín en el control con función de salida lineal. Una aplicación experimental sobre una planta real de laboratorio corrobora los resultados teóricos esperadosMinisterio de Ciencia y Tecnología DPI2004-07444-C04-0

Modelado y control de una termobatidora para extracción de aceite de oliva

XXI Jornadas de Automática 2000. 18/09/2000. SevillaEste artículo describe el proceso de modelado, mediante ecuaciones diferenciales no lineales, de una termobatidora que forma parte del proceso de extracción de aceite de oliva en una almazara. Muestra además, los resultados en simulación del empleo de diversas estrategias de control aplicadas sobre el modelo no lineal obtenido, las cuales serán diferentes versiones del algoritmo de control predictivo DMC, diferenciadas, sobre todo, por el tratamiento que se da a las perturbaciones medibles. El resultado se compara finalmente con un controlador PI estándar. Previa a la implementación del control predictivo se ha realizado una fase de modelado lineal.Comisión Europea FEDER 1FD97-083

Control predictivo para procesos repetitivos

La finalidad de este trabajo es la obtención de mejoras en el control de dos tipos bien diferenciados de procesos, Éstos pueden ser procesos definidos en torno a un punto de operación y sometidos a perturbaciones de tipo periódico, o bien procesos tipo batch o por lotes. El problema de control ha surgido en aplicaciones como la fase de preparación del proceso de extracción de aceite de oliva o una gran cantidad de procesos industriales que funcionan por lotes (químicos, polimerización, microelectrónica, producción de medicamentos, etc.). Para conseguir mejorar en el control de estos procesos, se han diseñado controladores óptimos, basados en la aplicación de la metodología del control predictivo basado en modelo en ambos casos. El control de procesos sometidos a perturbaciones periódicas se puede abordar, entre otras técnicas, mediante un control IMPC al cual se le añade un bloque de predicción óptima de las perturbaciones. En el caso delos procesos por lotes se ha incorporado al controlador la capacidad de aprendizaje de lotes anteriores usando información pasada. Los controladores propuestos, se han probado en simulación, lineal y no lineal, así como en aplicaciones reales: una planta de laboratorio y la almazara

Modelling and predictive control of an olive oil mill

This paper describes the modelling and predictive control of the extraction process in an olive oil mill. The work is focused on the thermal part of the process, where the raw material is prepared for the mechanical separation. The paper shows the development of a model based upon Orst principles combined with experimental results and validated with real data. Different control strategies have been tested under simulation, showing that good performance can be obtained by the use of a predictive controller that takes into account the measurable disturbances that appear in the process. Constraints in actuators are also included in the control strategUnión Europea 1FD97-083 (FEDER