Stability issues for First Order Predictive Functional Controllers: Extension to Handle Higher Order Internal Models

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The first order input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, it may lead to chattering and/or instability. In this paper, instability of first order PFC is addressed,and solutions to handle higher order and difficult systems are proposed

Study of Intelligent Control Techniques Applied to a Stirring Tank with Heat Exchanger

This work presents a study and evaluation of intelligent control techniques applied to the problem of temperature control of a stirring tank with heat exchanger. This problem is represented by the example provided and documented by MathWorks in MATLAB/Simulink software, called Heatex. The intelligent techniques used are Fuzzy Logic Controller (FLC), Fuzzy Cognitive Maps (FCM), Artificial Neural Networks (ANN) and the combination of these. The proportional-integral (PI) controller provided in the Heatex example is considered as a reference basis during the evaluation of the intelligent control techniques in different test scenarios. The metrics Integral of Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE), as well as the parameters overshoot percentage and settling time are the criteria used to evaluate the control techniques performance

Fuzzy modeling and control for conical magnetic bearings using linear matrix inequality

A general nonlinear model with six degree-of-freedom rotor dynamics and electromagnetic force equations for conical magnetic bearings is developed. For simplicity, a T-S (Takagi Sugeno) fuzzy model for the nonlinear magnetic bearings assumed no rotor eccentricity is first derived, and a fuzzy control design based on the T-S fuzzy model is then proposed for the high speed and high accuracy control of the complex magnetic bearing systems. The suggested fuzzy control design approach for nonlinear magnetic bearings can be cast into a linear matrix inequality (LMI) problem via robust performance analysis, and the LMI problem can be solved efficiently using the convex optimization techniques. Computer simulations are presented for illustrating the performance of the control strategy considering simultaneous rotor rotation tracking and gap deviations regulation

Comparative Study of Temperature Control in a Heat Exchanger Process

In the present work the dynamic behavior of a plate heat exchanger (PHE) (single pass counter current consists of 24 plates) studied experimentally and theoretically to control the system. Different control strategies; conventional feedback control, classical fuzzy logic control, artificial neural network (NARMAL2) control and PID fuzzy logic control were implemented to control the outlet cold water temperature. A step change was carried in the hot water flow rate which was considered as a manipulated variable. The experimental heat transfer measurements of the PHE showed that the overall heat transfer coefficient (U) is related to the hot water flow rate (mh) by a correlation having the form: U mh 0.7158 =11045 In this work the PHE model was found theoretically as a first order lead and second order overdamped lag while the experimental PHE represented dynamically (by PRC method) as a first order with negligible dead time value. A comparison between the experimental and the theoretical model is carried out and good agreement is obtained. The performance criteria used for different control modes are the integral square error (ISE) and integral time-weighted absolute error (ITAE) where the ITAE gave better performance. As well as the parameters of the step performance of the system such as overshoot value, settling time and rise time are used to evaluate the performance of different control strategies. The PID fuzzy controller gave better control results of temperature rather than PI, PID and artificial neural network controller since PID fuzzy controller combines the advantages of a fuzzy logic controller and a PID controller. MATLAB program version 7.10 was used as a tool of simulation for all the studies mentioned in this work

A novel modeling of molten-salt heat storage systems in thermal solar power plants

Many thermal solar power plants use thermal oil as heat transfer fluid, and molten salts as thermal energy storage. Oil absorbs energy from sun light, and transfers it to a water-steam cycle across heat exchangers, to be converted into electric energy by means of a turbogenerator, or to be stored in a thermal energy storage system so that it can be later transferred to the water-steam cycle. The complexity of these thermal solar plants is rather high, as they combine traditional engineering used in power stations (water-steam cycle) or petrochemical (oil piping), with the new solar (parabolic trough collector) and heat storage (molten salts) technologies. With the engineering of these plants being relatively new, regulation of the thermal energy storage system is currently achieved in manual or semiautomatic ways, controlling its variables with proportional-integral-derivative (PID) regulators. This makes the overall performance of these plants non optimal. This work focuses on energy storage systems based on molten salt, and defines a complete model of the process. By defining such a model, the ground for future research into optimal control methods will be established. The accuracy of the model will be determined by comparing the results it provides and those measured in the molten-salt heat storage system of an actual power plant

INVESTIGATION OF ADVANCED CONTROL STRATEGY FOR A pH NEUTRALIZATION PROCESS PLANT

pH neutralization is one of the crucial processes to all industries with various functions range from food processing industry to wastewater treatment. Hence, the process must be maintained at optimum performance to fulfill its functionality. However, pH neutralization is a highly nonlinear process with high sensitivity at the neutralization point. The complexity of the process has challenged the conventional control strategy's performance. Currently, the control strategy used in the pilot plant (PI controller) is adequate with certain range of error. Thus, the objective of this project is to investigate, design and implement advanced control strategy which can improve the overall performance of the conventional control strategy. The calibration results show that the pilot plant's measuring meters have poor accuracy and repeatability. Due to this, no practical experiments have been performed throughout this research. Prior to simulation works, the pilot plant's model obtained from other researcher has been validated. The simulation results show that the model has faster dynamic response compare to the pilot plant. Nevertheless, the model is still being used for simulation. Through this research, the limitation of PI control strategy in controlling nonlinear process has been observed. Fuzzy logic controller (FLC) has been developed to improve the control performance of PI controller. According to the simulation results, FLC has produced excellent control performance with the ability of controlling process' nonlinear region. As a conclusion, advanced control strategy such as FLC is more superior to PI controller in nonlinear process control. For further research, perhaps the advanced control strategy developed can be implemented in the pilot plant to examine its real time performance

Integration of the FMBPC strategy in a Closed-Loop Predictive Control structure. Application to the control of activated sludge

[ES] En este trabajo se aborda la integración de dos métodos o estrategias de Control Predictivo basado en Modelos, a saber: Control Predictivo basado en Modelos Borrosos (FMBPC) y Control Predictivo en Lazo Cerrado (CLP MPC). La primera de estas estrategias utiliza principios de Control Predictivo Funcional (PFC) y está enmarcada, al mismo tiempo, en el ámbito del Control Inteligente (IC). La integración tiene como principal objetivo proporcionar a la estrategia de control no lineal FMBPC un procedimiento de optimización que permita el manejo automático de restricciones en la variable de control. La solución propuesta consiste en hacer uso de una estructura complementaria de tipo CLP MPC para determinar mediante optimización, en cada instante de muestreo, los valores óptimos de un cierto término aditivo, a sumar a la ley de control FMBPC, de tal modo que se satisfagan las restricciones. El modelo de predicciones y la ley de control base necesarios para realizar los cálculos en la estructura CLP MPC son proporcionados por la estrategia FMBPC. La estrategia mixta FMBPC/CLP propuesta ha sido validada, en simulación, aplicándola al control de fangos activados en plantas de tratamiento de aguas residuales (EDAR), poniendo el foco en la imposición de restricciones a la acción de control. Los resultados obtenidos son satisfactorios, observando un buen rendimiento del algoritmo de control diseñado, al tiempo que se garantiza tanto la satisfacción de las restricciones, que era el principal objetivo, como la estabilidad del sistema en lazo cerrado.[EN] This work addresses the integration of two methods or strategies of Model-Based Predictive Control, namely: Fuzzy Model-Based Predictive Control (FMBPC) and Closed-Loop Predictive Control (CLP-MPC). The first of these strategies uses principles of Predictive Functional Control (PFC) and is framed, at the same time, in the field of Intelligent Control (IC). The main objective of the integration is to provide to the FMBPC nonlinear control strategy an optimization procedure that allows the automatic handling of constraints in the control variable. The proposed solution consists of making use of a complementary structure of the CLP-MPC type to determine by optimization, at each sampling instant, the optimal values of a certain additive term, to be added to the FMBPC control law, in such a way that they are satisfied the constraints. The prediction model and base control law necessary to perform the calculations on the CLP-MPC structure are provided by the FMBPC strategy. The proposed FMBPC/CLP mixed strategy has been validated, in simulation, applying it to the control of activated sludge processes in wastewater treatment plants (WWTP), focusing on the imposition of constraints on the control action. The results obtained are satisfactory, observing a good performance of the designed control algorithm, while guaranteeing both the satisfaction of the constraints, which was the main objective, and the stability of the closed-loop system.Este trabajo contó con el apoyo económico del Gobierno de España a través del proyecto MICINN PID2019-105434RB-C31 y de la Fundación Samuel Solórzano a través del proyecto FS / 20-2019.Vallejo, PM.; Vega, P. (2021). Integración de la estrategia FMBPC en una estructura de control predictivo en lazo cerrado. Aplicación al control de fangos activados. Revista Iberoamericana de Automática e Informática industrial. 19(1):13-26. https://doi.org/10.4995/riai.2021.15793OJS1326191Adetola, V., & Guay, M., 2010. 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Control predictivo basado en modelos fuzzy de sistemas complejos. Aplicación al control y supervisión de procesos de depuración de aguas

Tesis por compendio de publicaciones[ES] El Control Predictivo basado en Modelos (MPC) es un caso particular de estrategia de control automático de procesos que abarca un conjunto de procedimientos cuyo denominador común es la utilización de un modelo de predicciones para determinar una ley de control óptima. El tipo de modelo elegido, los criterios de optimización y el procedimiento de deducción de la ley de control caracterizan cada una de las múltiples alternativas de MPC que existen. El control predictivo es una consolidada y, al mismo tiempo, prometedora estrategia de control con múltiples aplicaciones en el ámbito industrial y con numerosas líneas de investigación abiertas. Una de las modalidades de este tipo de control es el denominado Control Predictivo basado en Modelos Borrosos (FMBPC), que utiliza modelos cualitativos basados en reglas, globalmente no lineales, para representar el proceso a controlar. El control FMBPC está enmarcado en el subcampo del Control Predictivo No Lineal (NLMPC/NMPC) y al mismo tiempo pertenece también, parcialmente al menos, al campo del Control Inteligente (IC), debido a que utiliza una de las herramientas características de la inteligencia artificial, como es la lógica borrosa. En la Tesis Doctoral que aquí se presenta se considera una estrategia FMBPC cuyo modelo base es un modelo borroso, o Fuzzy Model (FM) en la literatura en inglés, de tipo Takagi-Sugeno (TS), obtenido mediante identificación a partir de series de datos numéricos de entrada-salida (que pueden ser datos estrictamente experimentales o adaptaciones de estos, generados en simulación). Esta característica dota a nuestra estrategia FMBPC de una interesante cualidad que aporta valor añadido dentro del campo del control NMPC, consistente en la útil información cualitativa implícita en el modelo borroso, consecuencia de la capacidad que tiene la identificación borrosa de capturar fielmente la dinámica de un sistema a partir de datos numéricos. Esta propiedad repercute directamente de forma positiva en la validez de las predicciones y supone, en última instancia, un incremento significativo del rendimiento o desempeño del algoritmo de control predictivo, en el caso de tratar con sistemas fuertemente no lineales, complejos o desconocidos. Esta es la razón por la que en esta tesis se propone la estrategia FMBPC como la idónea para abordar el control de un cierto tipo de procesos conocidos como Procesos de Fangos Activados (ASP), muy habituales como mecanismo de depuración biológica en Estaciones Depuradoras de Aguas Residuales (EDAR) (también conocidas en la literatura en inglés como Wastewater Treatment Plants (WWTP)). El interés de la propuesta es doble: por un lado, contribuir a ampliar las líneas de investigación en el campo del control predictivo no lineal y por otro, aportar una estrategia y una metodología que puedan ser útiles en la mejora de los procesos de depuración de aguas, cuya importancia en la salud pública y en el cuidado del medio ambiente es creciente, cono así se refleja en las legislaciones medioambientales, cada vez más exigentes. Una parte importante del esfuerzo investigador desarrollado en la presente tesis ha sido enfocado a la aplicación de la estrategia FMBPC propuesta al paradigmático caso de estudio elegido (procesos biológicos ASP en plantas depuradoras de aguas residuales). Dadas las características de estos procesos, principalmente su alta no linealidad, su complejidad intrínseca y su carácter multivariable, derivadas de su naturaleza biológica, las investigaciones realizadas pueden trascender más allá del mero ámbito del propio proceso. La implementación practica se ha llevado a cabo mediante simulación y ello ha supuesto un importante reto, principalmente en dos aspectos: por un lado, el desarrollo del software necesario y por otro, la implementación de los cálculos matemáticos apropiados. La investigación realizada puede descomponerse, de una manera esquemática, en las siguientes cuatro fases o etapas: a) identificación borrosa del proceso ASP a partir de datos numéricos de entrada-salida y conversión del modelo borroso obtenido en un modelo equivalente en el espacio de estados, discreto, lineal y variante en el tiempo (DLTV); b) determinación de una ley de control predictivo de tipo FMBPC, analítica y explícita, siguiendo los principios del denominado Control Predictivo Funcional (PFC); c) análisis de estabilidad local en lazo cerrado de la estrategia FMBPC propuesta; d) integración de esta estrategia dentro de la configuración de control predictivo conocida como Paradigma de Lazo Cerrado (CLP), también llamada control predictivo en lazo cerrado, con el objetivo de imponer restricciones de manera automática en la acción de control. Los resultados obtenidos son satisfactorios, principalmente en lo que se refiere a la demostración de la utilidad de la estrategia FMBPC propuesta como una alternativa válida en el campo del control predictivo no lineal, para sistemas complejos o desconocidos, con dos ventajas destacables en relación con otras estrategias, a saber: por un lado, la útil información contenida en el modelo base de las predicciones, capturada durante el proceso de identificación borrosa previo a la aplicación de la estrategia y, por otro, la forma analítica y explicita de la ley de control deducida, que facilita tanto la implementación del algoritmo de control como las tareas de análisis (entre ellas, las de análisis estabilidad). [EN] Model Predictive Control (MPC) is a particular case of automatic process control strategy that encompasses a set of procedures whose common denominator is the use of a prediction model to determine an optimal control law. The type of model chosen, the optimization criteria and the control law deduction procedure characterize each one of the multiple MPC alternatives that exist. Predictive control is a consolidated and, at the same time, promises a control strategy with multiple applications in the industrial field and with many open lines of research. One of the modalities of this type of control is the so-called Fuzzy Model-Based Predictive Control (FMBPC), which uses qualitative models based on rules, globally non-linear, to represent the process to be controlled. The FMBPC control is framed in the subfield of Non-Linear Predictive Control (NLMPC/NMPC) and at the same time it also belongs, partially at least, to the field of Intelligent Control (IC), because it uses one of the characteristic tools of intelligence artificial, as is fuzzy logic. In the Doctoral Thesis presented here, a FMBPC strategy is considered whose base model is a fuzzy model, or Fuzzy Model (FM) in the English literature, of the Takagi-Sugeno (TS) type, obtained through identification from series of input-output numerical data (which can be strictly experimental data or adaptations of these, generated in simulation). This feature provides our FMBPC strategy with an interesting quality that provides added value within the field of NMPC control, consisting of the useful qualitative information indicated in the fuzzy model, a consequence of the fuzzy identification's ability to faithfully capture the dynamics of a system from numerical data. This property has a direct positive impact on the validity of the predictions and, ultimately, a significant increase in the performance of the predictive control algorithm, in the case of dealing with expensive non-linear, complex or unknown systems. This is the reason why in this thesis the FMBPC strategy is proposed as the ideal one to address the control of a certain type of processes known as Activated Sludge Processes (ASP), very common as a biological purification mechanism in Water Treatment Plants. Waste (WWTP) (also known in English literature as Wastewater Treatment Plants (WWTP)). The interest of the proposal is twofold: on the one hand, to contribute to expanding the lines of research in the field of nonlinear predictive control and, on the other, to provide a strategy and methodology that can be useful in improving the debugging processes of waters, whose importance in public health and in caring for the environment is growing, as reflected in the increasingly demanding environmental legislation. An important part of the research effort developed in this thesis has been focused on the application of the FMBPC strategy to the chosen paradigmatic case study (ASP biological processes in wastewater treatment plants). Given the characteristics of these processes, mainly their high non-linearity, their intrinsic complexity and their multivariable character, derived from their biological nature, the investigations carried out can transcend beyond the mere scope of the process itself. The practical implementation has been carried out through simulation and this has been an important challenge, mainly in two aspects: on the one hand, the development of the necessary software and, on the other, the implementation of the appropriate mathematical calculations. The research carried out can be broken down, schematically, into the following four phases or stages: a) fuzzy identification of the ASP process from numerical input-output data and conversion of the fuzzy model obtained into an equivalent model in the space of states , discrete, linear and variant in time (DLTV); b) determine a predictive control law of the FMBPC type, analytical and clean, following the principles of the so-called Predictive Functional Control (PFC); c) closed-loop local stability analysis of the proposed FMBPC strategy; d) integration of this strategy within the predictive control configuration known as Closed Loop Paradigm (CLP), also called closed loop predictive control, with the aim of automatically imposing restrictions on the control action. The results obtained are satisfactory, mainly in what refers to the demonstration of the utility of the FMBPC strategy as a valid alternative in the field of nonlinear predictive control, for complex or unknown systems, with two advantage