Influence of hyper-parameters in algorithms based on Differential Evolution for the adjustment of PID-type controllers in SISO processes through mono and multi-objective optimisation

[EN] PID Controllers remain as the reliable front-line solution in feedback control loops. Even when their simplicity is one of the main reasons for this, the right tuning of their parameters is essential to guarantee their performance. As consequence, several tuning methods are available. Nowadays performing a tuning process via stochastic optimisation is an attractive solution for complex processes. Nevertheless, the solution obtained using such optimisation methods is very sensitive to the hyper-parameters used. In this paper, we propose to designers a set of hyper-parameters for different algorithms based on Differential Evolution in SISO processes. Obtained results show several aspects to consider regarding the most promising values for several optimisation instances, facilitating knowledge transfer for new optimisation instances.[ES] Los controladores PID se mantienen como una solución confiable de primera línea en sistemas de control retroalimentado. Incluso cuando su sencillez es una de las principales razones de ello, un correcto ajuste de sus parámetros es fundamental para garantizar un rendimiento satisfactorio. Como consecuencia, se encuentran disponibles varios métodos de ajuste. En la actualidad, realizar un proceso de ajuste mediante optimización estocástica es una solución atractiva para controlar procesos complejos. No obstante, la solución obtenida con estos métodos de optimización es muy sensible a los hiper-parámetros utilizados. En este artículo proponemos a los diseñadores un conjunto de hiper-parámetros para configurar diferentes algoritmos basados en Evolución Diferencial en sistemas de una entrada y una salida (SISO). Los resultados obtenidos muestran varios aspectos a considerar sobre los valores más prometedores para varias instancias de optimización facilitando la transferencia de conocimiento para nuevas instancias de optimización.Trabajo financiado parcialmente por el Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), y la Fundação Araucária (FAPPR) - Brasil - proyectos 310079/2019-5-PQ2, 4408164/2021-2-Univ y PRONEX-51432/2018-PPP.Martínez-Luzuriaga, PN.; Reynoso-Meza, G. (2022). Influencia de los hiper-parámetros en algoritmos basados en Evolución Diferencial para el ajuste de controladores del tipo PID en procesos SISO. Revista Iberoamericana de Automática e Informática industrial. 20(1):44-55. https://doi.org/10.4995/riai.2022.16517445520

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).Reynoso Meza, G.; Sanchís Saez, J.; Blasco, X.; Freire, RZ. (2016). Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning. Expert Systems with Applications. 51:120-133. doi:10.1016/j.eswa.2015.11.028S1201335

Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop

[EN] In general, the starting point for the complex task of designing a robust and efficient control system is the use of nominal models that allow to establish a first set of parameters for the selected control scheme. Once the initial stage of design is achieved, control engineers face the difficult task of Fine-Tuning for a more realistic environment, where the environment conditions are as similar as possible to the real system. For this reason, in the last decades the use of Hardware-in-The-Loop (HiL) systems has been introduced. This simulation technique guarantees realistic simulation environments to test the designs but without danger of damaging the equipment. Also, in this iterative process of Fine-Tuning, it is usual to use different (generally conflicting/opposed) criteria that take into account the sensitivities that always appear in every project, such as economic, security, robustness, performance, for example. In this framework, the use of multi-objective techniques are especially useful since they allow to study the different design alternatives based on the multiple existing criteria. Unfortunately, the combination of multi-objective techniques and verification schemes based on Hardware-In-The-Loop presents a high incompatibility. Since obtaining the optimal set of solutions requires a high computational cost that is greatly increased when using Hardware- In-the-Loop. For this reason, it is often necessary to use less realistic but more computationally efficient verification schemes such as Model in the Loop (MiL), Software in the Loop (SiL) and Processor in the Loop (PiL). In this paper, a combined methodology is presented, where multi-objective optimisation and multi-criteria decision making steps are sequentially performed to achieve a final control solution. The authors claim that while going towards the optimisation sequence over MiL -> SiL -> PiL -> HiL platforms, the complexity of the problem is unveiled to the designer, allowing to state meaningful design objectives. In addition, safety in the step between simulation and reality is significantly increased.The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for providing funding through the project DPI2015-71443-R and the grant BES-2012-056210. This work has been partially supported by the National Council of Scientific and Technological Development of Brazil (CNPq) through the BJT/304804/2014-2 and PQ-2/304066/2016-8 grants.Reynoso Meza, G.; Velasco-Carrau, J.; Garcia-Nieto, S.; Blasco, X. (2017). Enhancing controller's tuning reliability with multi-objective optimisation: From Model in the loop to Hardware in the loop. Engineering Applications of Artificial Intelligence. 64:52-66. https://doi.org/10.1016/j.engappai.2017.05.005S52666

Controller tuning by means of multi-objective optimization algorithms: a global tuning framework

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821

Resultados del Concurso de Ingeniería de Control 2012 y Convocatoria 2013

Este trabajo ha sido realizado parcialmente gracias al apoyo del Ministerio de Ciencia e Innovacion (Gobierno de España) mediante la Acciones Complementarias DPI2010-12026-E y DPI2011-15857-E, y la Universitat Politecnica de València a ` traves de la beca UPV-FPI-2010/19.Blasco, X.; Reynoso Meza, G.; García Nieto, S. (2013). Resultados del Concurso de Ingeniería de Control 2012 y Convocatoria 2013. Revista Iberoamericana de Automática e Informática Industrial (RIAI). 10(2):240-244. https://doi.org/10.1016/j.riai.2013.03.015S240244102Blasco, X., García-Nieto, S., & Reynoso-Meza, G. (2012). Control autónomo del seguimiento de trayectorias de un vehículo cuatrirrotor. Simulación y evaluación de propuestas. Revista Iberoamericana de Automática e Informática Industrial RIAI, 9(2), 194-199. doi:10.1016/j.riai.2012.01.001García-Nieto, S., Blasco, X., Sanchiz, J., Herrero, J., Reynoso-Meza, G., Martínez, M., 2012. Trackdrone lite. Riunet: http://hdl.handle.net/10251/16427.Messac, A. (1996). Physical programming - Effective optimization for computational design. AIAA Journal, 34(1), 149-158. doi:10.2514/3.13035Sanchis, J., Martínez, M. A., Blasco, X., & Reynoso-Meza, G. (2010). Modelling preferences in multi-objective engineering design. Engineering Applications of Artificial Intelligence, 23(8), 1255-1264. doi:10.1016/j.engappai.2010.07.00

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

Physical programming for preference driven evolutionary multi-objective optimization

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.This work was partially supported by the FPI-2010/19 grant and the PAID-2011/2732 project from the Universitat Polittccnica de Valencia and the projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Garcia Nieto, S. (2014). Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing. 24:341-362. https://doi.org/10.1016/j.asoc.2014.07.009S3413622

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

Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits

[EN] Synthetic biology is reaching the situation where tuning devices by hand is no longer possible due to the complexity of the biological circuits being designed. Thus, mathematical models need to be used in order, not only to predict the behavior of the designed synthetic devices; but to help on the selection of the biological parts, i.e., guidelines for the experimental implementation. However, since uncertainties are inherent to biology, the desired dynamics for the circuit usually requires a trade-off among several goals. Hence, a multi-objective optimization design (MOOD) naturally arises to get a suitable parametrization (or range) of the required kinetic parameters to build a biological device with some desired properties. Biologists have classically addressed this problem by evaluating a set of random Monte Carlo simulations with parameters between an operation range. In this paper, We propose solving the MOOD by means of dynamic programming using both a global multi-objective evolutionary algorithm (MOLA) and a local gradient-based nonlinear programming (NLP) solver. The performance of both alternatives is then checked in the design of a well-known biological circuit: a genetic incoherent feed-forward loop showing adaptive behavior. (C) 2016, IFAC (International Federation of Antomatic Control) Hosting by Elsevier Ltd. All rights reserved.The research leading to these results has received funding from the European Union (FP7/2007-2013 under grant agreement no604068), the Spanish Government (FEDER-CICYT DPI2011-524 28112-C04-01, DPI2014-55276-C5-1-R, DPI2015-70975-P) and the National Council of Scientific and Technologic Development of Brazil (BJT-304804/2014-2). Yadira Boada thanks also grant FPI/2013-3242 of the Universitat Politecnica de ValenciaBoada-Acosta, YF.; Pitarch Pérez, JL.; Vignoni, A.; Reynoso Meza, G.; Picó, J. (2016). Optimization Alternatives for Robust Model-based Design of Synthetic Biological Circuits. IFAC-PapersOnLine. 49(7):821-826. https://doi.org/10.1016/j.ifacol.2016.07.291S82182649

A Simple Proposal for Including Designer Preferences in Multi-Objective Optimization Problems

[EN] Including designer preferences in every phase of the resolution of a multi-objective optimization problem is a fundamental issue to achieve a good quality in the final solution. To consider preferences, the proposal of this paper is based on the definition of what we call a preference basis that shows the preferred optimization directions in the objective space. Associated to this preference basis a new basis in the objective space-dominance basis-is computed. With this new basis the meaning of dominance is reinterpreted to include the designer's preferences. In this paper, we show the effect of changing the geometric properties of the underlying structure of the Euclidean objective space by including preferences. This way of incorporating preferences is very simple and can be used in two ways: by redefining the optimization problem and/or in the decision-making phase. The approach can be used with any multi-objective optimization algorithm. An advantage of including preferences in the optimization process is that the solutions obtained are focused on the region of interest to the designer and the number of solutions is reduced, which facilitates the interpretation and analysis of the results. The article shows an example of the use of the preference basis and its associated dominance basis in the reformulation of the optimization problem, as well as in the decision-making phase.This work has been supported by the Ministerio de Ciencia, Innovacion y Universidades, Spain, under Grant RTI2018-096904-B-I00.Blasco, X.; Reynoso Meza, G.; Sánchez Pérez, EA.; Sánchez Pérez, JV.; Jonard Pérez, N. (2021). A Simple Proposal for Including Designer Preferences in Multi-Objective Optimization Problems. Mathematics. 9(9):1-19. https://doi.org/10.3390/math90909911199