Control predictivo basado en modelos mediante técnicas de optimización heurística. Aplicación a procesos no lineales y multivariables

La Tesis Doctoral se fundamenta, principalmente, en la exploración de nuevos métodos de Control Predictivo Basado en Modelos (MBPC) mediante la incorporación de herramientas de optimización heurística y las mejoras en las prestaciones que se pueden conseguir con ello. La metodología de MBPC constituye un campo cada vez más importante en el control de procesos debido a que se trata de una formulación muy intuitiva, y a la vez muy potente, de un problema de control (por tanto es más fácilmente aceptable en el ámbito industrial). A pesar de ello, presenta limitaciones cuando se quiere aplicar a ciertos procesos complejos. Un elemento fundamental y al mismo tiempo limitante de ésta metodología lo constituye la técnica de optimización que se utilice. Simplificando mucho, el MBPC se convierte en un problema de minimización en cada periodo de muestreo, y la complejidad del problema de control se refleja directamente en la función a minimizar en cada instante. Si se incorporan modelos no lineales, restricciones en las variables, e índices de funcionamiento sofisticados, todo ello asociado a los problemas de tiempo real, se va a requerir algoritmos de optimización adecuados que garanticen el mínimo global en un tiempo acotado. En este sentido, la tesis incluye un análisis de las metodologías de Optimización Heurísticas, Simulated Annealing y Algoritmos Genéticos, como candidatas a la resolución de ese tipo de problemas y apartir de ellas realiza una implementación novedosa (denominada ASA) dentro del grupo de los algoritmos de Simulated Annealing que reduce el coste computacional. En los Algoritmos Genéticos, se obtienen las combinaciones de codificación y operadores genéticos más adecuadas para conseguir buenas relaciones de 'calidad de la solución/coste computacional' en la resolución de problemas de minimización complejos (no convexos, con discontinuidades, restricciones, etc.). Todo este análisis previo, permite la adaptación adecuada de estas técnicas heurísticas......Blasco Ferragud, FX. (1999). Control predictivo basado en modelos mediante técnicas de optimización heurística. Aplicación a procesos no lineales y multivariables [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15995Palanci

Preference driven multi-objective optimization design procedure for industrial controller tuning

Multi-objective optimization design procedures have shown to be a valuable tool for con- trol engineers. These procedures could be used by designers when (1) it is difficult to find a reasonable trade-off for a controller tuning fulfilling several requirements; and (2) if it is worthwhile to analyze design objectives exchange among design alternatives. Despite the usefulness of such methods for describing trade-offs among design alterna- tives (tuning proposals) with the so called Pareto front, for some control problems finding a pertinent set of solutions could be a challenge. That is, some control problems are com- plex in the sense of finding the required trade-off among design objectives. In order to improve the performance of MOOD procedures for such situations, preference handling mechanisms could be used to improve pertinency of solutions in the approximated Pareto front. In this paper an overall MOOD procedure focusing in controller tuning applications using designer s preferences is proposed. In order to validate such procedure, a bench- mark control problem is used and reformulated into a multi-objective problem statement, where different preference handling mechanisms in the optimization process are evalu- ated and compared. The obtained results validate the overall proposal as a potential tool for industrial controller tuning.This work was partially supported by projects TIN2011-28082, ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness. First author gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work.Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Martínez Iranzo, MA. (2016). Preference driven multi-objective optimization design procedure for industrial controller tuning. Information Sciences. 339:108-131. doi:10.1016/j.ins.2015.12.002S10813133

Asymmetric distances to improve n-dimensional Pareto fronts graphical analysis

isualization tools and techniques to analyze n-dimensional Pareto fronts are valuable for designers and decision makers in order to analyze straightness and drawbacks among design alternatives. Their usefulness is twofold: on the one hand, they provide a practical framework to the decision maker in order to select the preferable solution to be imple- mented; on the other hand, they may improve the decision maker s design insight,i.e. increasing the designer s knowledge on the multi-objective problem at hand. In this work, an order based asymmetric topology for finite dimensional spaces is introduced. This asymmetric topology, associated to what we called asymmetric distance, provides a theoretical and interpretable framework to analyze design alternatives for n-dimensional Pareto fronts. The use of this asymmetric distance will allow a new way to gather dominance and relative distance together. This property can be exploited inside interactive visualization tools. Additionally, a composed norm based on asymmetric distance has been developed. The composed norm allows a fast visualization of designer preferences hypercubes when Level Diagram visualization is used for multidimensional Pareto front analysis. All these proposals are evaluated and validated through different engineering benchmarks; the presented results show the usefulness of this asymmetric topology to improve visualization interpretability.This work was partially supported by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain) and the National Council of Scientific and Technologic Development of Brazil (CNPq) with the postdoctoral fellowship BJT-304804/2014-2.Blasco Ferragud, FX.; Reynoso Meza, G.; Sánchez Pérez, EA.; Sánchez Pérez, JV. (2016). Asymmetric distances to improve n-dimensional Pareto fronts graphical analysis. Information Sciences. 340:228-249. doi:10.1016/j.ins.2015.12.039S22824934

A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm

[EN] Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the nor- malized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is pre- sented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.This work was partially supported by the FPI-2010/19 grant and the PAID-06-11 project from the Universitat Politècnica de València, projects TIN2011-28082 and ENE2011-25900 (Spanish Ministry of Economy and Competitiveness) and the GV/2012/073 (Generalitat Valenciana).Herrero Durá, JM.; Reynoso Meza, G.; Martínez Iranzo, MA.; Blasco Ferragud, FX.; Sanchís Saez, J. (2014). A Smart-Distributed Pareto Front Using ev-MOGA Evolutionary Algorithm. International Journal of Artificial Intelligence Tools. 23(2):1-22. https://doi.org/10.1142/S021821301450002XS122232Marler, R. T., & Arora, J. S. (2009). The weighted sum method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 41(6), 853-862. doi:10.1007/s00158-009-0460-7Messac, A., & Mattson, C. A. (2002). Optimization and Engineering, 3(4), 431-450. doi:10.1023/a:1021179727569Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2), 86-98. doi:10.1007/s00158-002-0276-1Martínez, M., Sanchis, J., & Blasco, X. (2006). Global and well-distributed Pareto frontier by modified normalized normal constraint methods for bicriterion problems. Structural and Multidisciplinary Optimization, 34(3), 197-209. doi:10.1007/s00158-006-0071-5Martínez, M., García-Nieto, S., Sanchis, J., & Blasco, X. (2009). Genetic algorithms optimization for normalized normal constraint method under Pareto construction. Advances in Engineering Software, 40(4), 260-267. doi:10.1016/j.advengsoft.2008.04.004Mattson, C. A., & Messac, A. (2005). Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization. Optimization and Engineering, 6(1), 85-115. doi:10.1023/b:opte.0000048538.35456.45Mattson, C. A., Mullur, A. A., & Messac, A. (2004). Smart Pareto filter: obtaining a minimal representation of multiobjective design space. Engineering Optimization, 36(6), 721-740. doi:10.1080/0305215042000274942Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. doi:10.1109/mci.2006.1597059Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49. doi:10.1016/j.swevo.2011.03.001Bonissone, P., Subbu, R., & Lizzi, J. (2009). Multicriteria decision making (mcdm): a framework for research and applications. IEEE Computational Intelligence Magazine, 4(3), 48-61. doi:10.1109/mci.2009.933093Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-1Mezura-Montes, E., & Coello Coello, C. A. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194. doi:10.1016/j.swevo.2011.10.001ÅSTRÖM, K. J., PANAGOPOULOS, H., & HÄGGLUND, T. (1998). Design of PI Controllers based on Non-Convex Optimization. Automatica, 34(5), 585-601. doi:10.1016/s0005-1098(98)00011-9Reynoso-Meza, G., Sanchis, J., Blasco, X., & Herrero, J. M. (2012). Multiobjective evolutionary algorithms for multivariable PI controller design. Expert Systems with Applications, 39(9), 7895-7907. doi:10.1016/j.eswa.2012.01.11

Modeling and Control of a Rotational Inverted Pendulum Applying Multi-objective Optimization Techniques

This article shows the application of multi-objective optimization techniques, both for the identification of parameters of a model and for the adjustment of controllers. In particular, we propose a technique to identify the parameters of a first principles model of a rotational inverted pendulum applying a methodology of multi- objective optimization and experimental data. Also the methodology extends to the tuning of PID and PI controllers for the mentioned system. For multiobjective optimization, an implementation based on evolutionary algorithms has been used, ev-MOGA  Herrero et al., 2007). For the analysis phase of the front solutions, we use the Pareto front visualization tool called level diagram (Blasco et al., 2017), which allows to successfully explore a set of Pareto optimal solutions and select one of them according to the preferences of the designer. The advantage oered by this methodology is the easy understanding of the conflicts that appear among the design objectives, allowing to select a compromise solution according to the preferences of the designer, without losing sight of the set of optimal solutions found

Explicit predictive control with non-convex polyhedral constraints

This paper proposes an explicit solution to the model predictive control of linear systems subject to non-convex polyhedral constraints. These constraints are modeled as the union of a finite number of convex polyhedra. The algorithm is based on calculating the explicit solution to a modified problem with linear constraints defined as the convex hull of the original ones and classifying its regions by their relation with the regions of the explicit solution to the original problem. Some of the regions are divided, and a procedure based on sum-of-squares programming is designed to determine which of the possible solutions are in fact optimal. Finally, the online algorithm is shown to be better in terms of computational cost and memory requirements than an algorithm based on obtaining and comparing the solutions of the problem using as constraints the polyhedra whose union forms the non-convex regions, both theoretically and by the results of an exampleThis work was partially funded by projects DPI2008-02133 and DPI2008-06731-C02-01(and -02)/DPI Ministerio de Ciencia e Innovacion-Spanish Government.Pérez Soler, E.; Ariño Latorre, CV.; Blasco Ferragud, FX.; Martínez Iranzo, MA. (2012). Explicit predictive control with non-convex polyhedral constraints. Automatica. 48(2):419-424. doi:10.1016/j.automatica.2011.07.01141942448

Methodology for energy management strategies design based on predictive control techniques for smart grids

This article focuses on the development of a general energy management system (EMS) design methodology using on model-based predictive control (MPC) for the control and management of microgrids. Different MPCbased EMS for microgrids have been defined in the literature; however, there is a lack of generality in the proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily interpretable, and reproducible in different microgrid architectures is presented. This is the result of the development of a novel methodology, which is also presented. It considers the state space formulation of the controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their models to the subsequent definition of the optimisation problem. This is developed through the design of the general cost function and the formulation of constrains, by means of general guidelines and reference values. To evaluate the performance of the developed methodology, simulation tests were carried out for four different microgrid architectures, with different applications and objectives, also considering different generation conditions, demand profiles, and initial conditions. The results showed that, with some simple guidelines and regardless of the case study, the developed MPC controller design methodology can address the technicaleconomic optimisation problem associated with energy management in microgrids in an easy and intuitive way.This work was supported in part by grant PID2020-116616RB-C31 and grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/ 501100011033 and by “ERDF A way of making Europe”; by the Generalitat Valenciana regional government through project CIAICO/2021/ 064, by Andalusian Regional Program of R + D + i (P20- 00730), and by the project “The green hydrogen vector. Residential and mobility application”, approved in the call for research projects of the Cepsa Foundation Chair of the University of Huelva

An application of genetic algorithms to robust control design

[ES] La estrategia de optimización multiobjetivo denominada Programación Física o Physical Programming (PP) permite al diseñador expresar sus preferencias explícitamente para cada objetivo de diseño (tiempo de establecimiento, estabilidad, etc.) de una forma flexible y con un claro significado ''físico''. Estas preferencias se formulan a través de categorías del tipo deseable, tolerable, inaceptable, etc. asociadas a unos rangos numéricos que el diseñador fija para cada especificación. En este artículo se muestra cómo se puede plantear un problema de control robusto como un problema de optimización multiobjetivo y cómo se puede utilizar PP con Algoritmos Genéticos (AGs) para salvar los problemas que presenta esta técnica frente a funciones con numerosos mínimos locales. En el artículo se resuelve el problema tipo para control robusto de un proceso de masa-muelle y se comparan las soluciones a este problema desarrolladas por otros autores con las obtenidas empleando PP y AGs.[EN] Physical Programming (PP) is a multiobjective optimization technique where the designer, for each objective or specification of the problem, declares his preferences in a flexible way. These preferences (e.g. overshoot, settling time, gain margin) are expressed with linguistic terms such as tolerable, desirable, undesirable, etc. and they are associated with numeric ranges in the same physical units as the objective itself is (e.g. seconds, percentages). This paper shows how PP can be applied to the design of robust controllers from a multiobjective optimization point of view. Non linear optimization used in the original PP method has been substituted by a Genetic Algorithm to avoid local minima which can usually arise in these multimodal problems. The ACC Robust Control Benchmark has been solved and the result obtained is compared with solutions from other authors.Financiado parcialmente por los proyectos de investigacion del MEC: FEDER DPI2004-8383-C03-02 y DPI2005-07835Martínez Iranzo, MA.; Sanchís Saez, J.; Blasco Ferragud, FX. (2010). Algoritmos Genéticos Aplicados al Diseño de Controladores Robustos. Revista Iberoamericana de Automática e Informática industrial. 3(1):39-51. http://hdl.handle.net/10251/146402OJS39513

Control-Oriented Modeling of the Cooling Process of a PEMFC-Based μ-CHP System

Here you will find a model of the cooling system of a PEMFC-based micro-CHP system. This model is implemented in Matlab/Simulink, version 9.3.0.713579 (R2017b). The model and the tests conducted for its development are described in detail in the following paper: Title: Control-Oriented Modeling of the Cooling Process of a PEMFC-Based micro-CHP System Authors: SANTIAGO NAVARRO GIMÉNEZ, JUAN MANUEL HERRERO DURÁ, FRANCESC XAVIER BLASCO FERRAGUD and RAÚL SIMARRO FERNÁNDEZ. Instituto Universitario de Automática e Informática Industrial Universidad Politécnica de Valencia In this folder (uCHP_Cooling_System), you will find the following files: Complete_Model_M.slx Complete_Model_V.slx Filter.m Modeling_Test.mat Parameters.mat S_Complete_Model_M.m S_Complete_Model_V.m Validation_Test.mat In both Complete_Model_M.slx and Complete_Model_V.slx you will find the implementation of the complete model in Simulink. The only difference between them is that the former is prepared for the data set from the modeling test (Modeling_Test.mat) and the latter for the data set from the modeling test (Validation_Test.mat). In order to launch the simulations, you have to run the scripts S_Complete_Model_M.m and S_Complete_Model_V.m, respectively. The file Parameters.mat contain the values of the parameters of the model. The file Filter.m is just a filter, which is used in both scripts. Please, read the comments in the scripts mentioned for more information. Thanks.Simarro Fernández, R.; Blasco Ferragud, FX.; Herrero Durá, JM.; Navarro Giménez, S. (2019). Control-Oriented Modeling of the Cooling Process of a PEMFC-Based μ-CHP System. Universitat Politècnica de València. https://doi.org/10.4995/Dataset/10251/11833

Robust identification of a biomedical process using evolutionary algorithms

[EN] In this paper, a non-linear robust identification (RI) methodology to characterize the feasible parameter set (FPS), when the identification error is unknown but bounded simultaneously by several norms, is presented. For that, the Robust Identification (RI) problem is transformed into a multimodal optimization one with an infinite number of global minima which constitute the FPS. For the optimization task a special Genetic Algorithm (e-GA), inspired by Multiobjective Evolutionary Algorithms (MOEA), is presented, which characterizes the FPS by means of a discrete set of models (FPS*) well distributed along the FPS. An application example to a biomedical model which shows the blockage that produces a given drug on the ionic currents of a cardiac cell is presented to illustrate the methodology.[ES] En este trabajo se presenta una metodología de identificación robusta (IR) en sistemas no lineales que permite caracterizar el conjunto de parámetros factible (FPS) cuando se asume que el error de identificación (EI) permanece acotado por varias normas simultáneamente. Para ello, se transforma el problema de IR en un problema de optimización multimodal donde los infinitos mínimos globales constituyen el FPS. Para abordar la optimización se presenta un algoritmo evolutivo (EA) específico denominado ε-GA, inspirado en los algoritmos evolutivos multiobjetivo, el cual caracteriza el FPS mediante un conjunto discreto de modelos FPS* adecuadamente distribuido a lo largo del FPS. Como ejemplo de aplicación del procedimiento, se presenta la IR de un modelo biomédico que refleja el bloqueo que produce un determinado fármaco sobre las corrientes iónicas de una célula cardíaca.Financiado parcialmente por los proyectos de investigación del MEC del Gobierno Español FEDER DPI2005-07835 y FEDER DPI2004-8383-C03-02.Herrero Durá, JM.; Blasco Ferragud, FX.; Martínez Iranzo, MA.; Sanchís Saez, J. (2009). Identificación Robusta de un Proceso Biomédico Mediante Algoritmos Evolutivos. Revista Iberoamericana de Automática e Informática industrial. 3(4):75-86. http://hdl.handle.net/10251/146251OJS75863