19 research outputs found
Computing Optimal Distances to Pareto Sets of Multi-Objective Optimization Problems in Asymmetric Normed Lattices
[EN] Given a finite dimensional asymmetric normed lattice, we provide explicit formulae for the optimization of the associated (non-Hausdorff) asymmetric distance among a subset and a point. Our analysis has its roots and finds its applications in the current development of effective algorithms for multi-objective optimization programs. We are interested in providing the fundamental theoretical results for the associated convex analysis, fixing in this way the framework for this new optimization tool. The fact that the associated topology is not Hausdorff forces us to define a new setting and to use a new point of view for this analysis. Existence and uniqueness theorems for this optimization are shown. Our main result is the translation of the original abstract optimal distance problem to a clear optimization scheme. Actually, this justifies the algorithms and shows new aspects of the numerical and computational methods that have been already used in visualization of multi-objective optimization problems.This work was supported by the Ministerio de Economia y Competitividad (Spain) under grants DPI2015-71443-R and MTM2016-77054-C2-1-P.Blasco, X.; Reynoso-Meza, G.; Sánchez Pérez, EA.; Sánchez Pérez, JV. (2019). Computing Optimal Distances to Pareto Sets of Multi-Objective Optimization Problems in Asymmetric Normed Lattices. Acta Applicandae Mathematicae. 159(1):75-93. https://doi.org/10.1007/s10440-018-0184-z7593159
Tuning and comparison of design concepts applying Pareto optimality. A case study of Cholette bioreactor
[EN] The linear control PI (D) and its variants are control structures (design concepts) that are still used in industrial processes. The control engineer will prefer one over another according to a desired tradeoff among complexity and performance indices. Given that this exchange might be in conflict, an analisis using multiobjective optimisation tools could be interesting. With this perspective, different Pareto fronts from different design concetps are compared, enabling a global, and not punctual, performance comparison. In this work a global methodology for comparing design concepts in dfferent stages was developed. The first step was to establish a region of stability. In the second stage, the stability region was considered as a search space for the multiobjective optimization process, approximating a Pareto set and front. In the third stage, a multicriteria analysis of the Pareto fronts was carried out, together with the simulation in the time domain for the output and control signals. As case study to validate this proposal the Cholette’s biorreactor was selected. The proposed methodology allows a better understanding of a conceptual solution, justifies and determines the use of a design concept thus meeting the needs of the designer.[ES] El control lineal PI(D) y sus variantes, son estructuras de control (conceptos de diseño) que actualmente se siguen utilizando en procesos industriales. La elección de una estructura de control sobre otra reside en el intercambio de prestaciones entre complejidad y rendimiento. Dado que este intercambio de prestaciones normalmente estará en conflicto, un análisis desde el punto de vista multiobjetivo puede ser de interés. Desde tal perspectiva, se analizan frentes de Pareto de diferentes conceptos de diseño, con lo que se realiza una comparación global y no puntual de tales conceptos. En este trabajo se plantea una propuesta metodológica para dicha comparación en diferentes etapas. La primera, fue establecer una región de estabilidad. En la segunda etapa se consideró la región de estabilidad como espacio de búsqueda para el proceso de optimización multiobjetivo calculando un conjunto y frente de Pareto. En la tercera etapa se realizó un análisis multicriterio de los frentes de Pareto, junto con la simulación en el dominio del tiempo para las señales de salida y de control. Como caso de estudio para validar la propuesta se ha elegido el biorreactor de Cholette que presenta diferentes condiciones de operación. La metodología propuesta permite una mejor comprensión de una solución conceptual, justifica y determina el uso de un concepto de diseño cumpliendo así con las necesidades del diseñador.Torralba-Morales, L.; Reynoso-Meza, G.; Carrillo-Ahumada, J. (2020). Sintonización y comparación de conceptos de diseño aplicando la optimalidad de Pareto. Un caso de estudio del biorreactor de Cholette. Revista Iberoamericana de Automática e Informática industrial. 17(2):190-201. https://doi.org/10.4995/riai.2019.11424OJS190201172Ajmeri, M., Ali, A., 2015. Two degree of freedom control scheme for unstable processes with small time delay. ISA Transactions 56, 308-326. https://doi.org/10.1016/j.isatra.2014.12.007Aström, K., Hägglund, T., 2006. Advanced PID Control. Vol. 461. ISA-The Instrumentation, Systems, and Automation Society Research Triangle.Aström, K. J., Hägglund, T., 1995. PID Controllers: Theory, Design, and Tuning. Instrument Society of America, Research Triangle Park, NC.Carlos-Hernández, S., Sanchez, E. N., Béteau, J.-F., Jiménez, L. D., 2014. Análisis de un Proceso de Tratamiento de Efluentes para Producción de Metano. Revista Iberoamericana de Automatica e Informática Industrial RIAI 11 (2), 236 - 246. https://doi.org/10.1016/j.riai.2014.02.006Carrillo-Ahumada, J., Paramo-Calderón, D., Aparicio-Saguilán, A., Rodríguez Jimenes, G., García-Alvarado, M., 2014. Approach of a Measurement of Linearized Representation of a Nonlinear System. Application to (Bio)Chemical reactors. Revista Mexicana de Ingenier'ıa Qu'ımica 13 (2), 631-647.Carrillo-Ahumada, J., Reynoso-Meza, G., García-Nieto, S., Sanchis, J., García Alvarado, M., 2015. Sintonización de controladores Pareto-óptimo robustos para sistemas multivariables. Aplicación en un helicóptero de 2 grados de libertad. Revista Iberoamericana de Automática e Informática industrial 12, 177-188. https://doi.org/10.1016/j.riai.2015.03.002Carrillo-Ahumada, J., Rodríguez-Jimenes, G., García-Alvarado, M., 2011. Tunning optimal-robust linear MIMO controllers of chemical reactors by using Pareto optimality. Chemical Engineering Journal 174 (1), 357 - 367. https://doi.org/10.1016/j.cej.2011.09.007Chen, Z., Yuan, X., Ji, B., Wang, P., Tian, H., 2014. Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II. Energy Conversion and Management 84, 390 - 404. https://doi.org/10.1016/j.enconman.2014.04.052Chidambaram, M., Reddy, G., 1996. Nonlinear control of systems with input and output multiplicities. Computers and Chemical Engineering 20 (3), 295 - 299. https://doi.org/10.1016/0098-1354(95)00019-4Darby, M. L., Nikolaou, M., 2012. MPC: Current practice and challenges. Control Engineering Practice 20 (4), 328 - 342. https://doi.org/10.1016/j.conengprac.2011.12.004García-Alvarado, M., Ruiz-López, I., Torres-Ramos, T., 2005. Tuning of multi-variate PID controllers based on characteristic matrix eigenvalues, Lyapunov functions and robustness criteria. Chemical Engineering Science 60 (4), 897 - 905. https://doi.org/10.1016/j.ces.2004.09.047Gómez, L., Botero, H., Álvarez, H., di Sciascio, F., 2015. Análisis de la Controlabilidad de Estado de Sistemas Irreversibles Mediante teoría de conjuntos. Revista Iberoamericana de Automática e Informática Industrial RIAI 12 (2), 145 - 153.https://doi.org/10.1016/j.riai.2015.02.002Hernández, F., Herrera Fernández, F., 03 2012. Identificación Inteligente de un Proceso Fermentativo Usando el Algoritmo GMDH Modificado. Revista Iberoamericana de Automática e Informática Industrial RIAI 9, 313. https://doi.org/10.1016/j.riai.2011.11.001Huang, H., Chen, C., 1999. Autotuning of PID Controllers for Second Order Unstable Process Having Dead Time. Journal of Chemical Engineering of Japan 32 (4), 486-497. https://doi.org/10.1252/jcej.32.486Huilcapi, V., Blasco, X., Herrero, J. M., Reynoso-Meza, G., 2019. A loop pairing method for multivariable control systems under a multi-objective optimization approach. IEEE Access 7, 81994-82014. https://doi.org/10.1109/ACCESS.2019.2923654Ibarra-Junquera, V., Rosu, H., 2007. PI-controlled bioreactor as a generalized Liénard system. Computers and Chemical Engineering 31 (3), 136-141. https://doi.org/10.1016/j.compchemeng.2006.05.023Indranil, P., Saptarshi, D., 2015. Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Applied Soft Computing 29, 328 - 344. https://doi.org/10.1016/j.asoc.2014.12.032Jhunjhunwala, M. K., Chidambaram, M., 2001. PID Controller tunning for Unstable Systems by Optimization Method. Chemical Engineering Communications. 185 (1), 91-113. https://doi.org/10.1080/00986440108912857Márquez-Rubio, J., del Muro-Cuéllar, B., 2010. Control basado en un esquema observador para sistemas de primer orden con retardo. Revista Mexicana de Ingeniería Química 09, 43-52.Mattson, C. A., Messac, A., 2005. Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization. Optimization and Engineering 6 (1), 85-115. https://doi.org/10.1023/B:OPTE.0000048538.35456.45Mora, L. A., Amaya, J. E., 2017. Un nuevo Método de Identificación Basado en la Respuesta Escalón en Lazo Abierto de Sistemas Sobre-amortiguados. Revista Iberoamericana de Automática e Informática industrial 14 (1), 31- 43. https://doi.org/10.1016/j.riai.2016.09.006Naranjani, Y., Sardahi, Y., Chen, Y., Sun, J.-Q., 2015. Multi-objective optimization of distributed-order fractional damping. Communications in Nonlinear Science and Numerical Simulation 24 (1), 159 - 168. https://doi.org/10.1016/j.cnsns.2014.12.011Normey-Rico, J., Camacho, E., 2009. Unified approach for robust dead-time compensator design. Journal of Process Control 19 (1), 38-47.https://doi.org/10.1016/j.jprocont.2008.02.003O'Dwyer, A., 2009. Handbook of PI and PID controller tuning rules. IFAC Proceedings Volumes 57. https://doi.org/10.1142/p575Padma, S., Chidambaram, M., 2002. Identification of Unstable transfer Model with a Zero by Optimization method. Journal of the Indian Institute of Science 82 (5 & 6), 219-225.Padma, S., Chidambaram, M., 2005. Set Point Weighted PID Controllers For Unstable Systems. Chemical Engineering Communications 192 (1), 1-13. https://doi.org/10.1080/00986440590473137Rajinikanth, V., Latha, K., 2012a. Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm. Applied Computational Intelligence and Soft Computing 2012. https://doi.org/10.1155/2012/214264Rajinikanth, V., Latha, K., 2012b. I-PD Controller Tuning for Unstable System Using Bacterial Foraging Algorithm: A Study Based on Various Error Criterion. Applied Computational Intelligence and Soft Computing 2012. https://doi.org/10.1155/2012/329389Reynoso-Meza, G., 2014. Controller tuning by means of evolutionary multiobjective optimization: a holistic multiobjective optimization design procedure. Ph.D. thesis, Universitat Politècnica de València, http://hdl.handle.net/10251/38248.Reynoso-Meza, G., Carrillo-Ahumada, J., Boada, Y., Picó, J., 2016. PID controller tuning for unstable processes using a multi-objective optimisation design procedure. IFAC-PapersOnLine 49 (7), 284 - 289. https://doi.org/10.1016/j.ifacol.2016.07.287Reynoso-Meza, G., Garcia-Nieto, S., Sanchis, J., Blasco, F. X., 2012. 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.2012.2185698Reynoso-Meza, G., Sanchis, J., Blasco, X., Martínez, M., 2013. Algoritmos Evolutivos y su empleo en el ajuste de controladores del tipo PID: Estado Actual y perspectivas. Revista Iberoamericana de Automática e Informática Industrial RIAI 10 (3), 251-268. https://doi.org/10.1016/j.riai.2013.04.001Samad, T., Feb 2017. A survey on industry impact and challenges thereof [technical activities]. IEEE Control Systems Magazine 37 (1), 17-18. https://doi.org/10.1109/MCS.2016.2621438Sanchez, A., Rotstein, G., Alsop, N., Bromberg, J., Gollain, C., Sorensen, S., Macchietto, S., Jakeman, C., 2002. Improving the development of eventdriven control systems in the batch processing industry. A case study. ISA Transactions 41 (3), 343 - 363. https://doi.org/10.1016/S0019-0578(07)60093-7Seshagiri, R., Rao, V., Chidambaram, M., 2007. Simple Analytical Design of Modified Smith Predictor with Improved Performance for Unstable FirstOrder Plus Time Delay (FOPTD) Processes. Industrial & Engineering Chemistry Research 46 (13), 4561-4571. https://doi.org/10.1021/ie061308nShariati, A., Taghirad, H., Fatehi, A., 2014. A neutral system approach to H PD/PI controller design of processes with uncertain input delay. Journal of Process Control 24 (3), 144-157. https://doi.org/10.1016/j.jprocont.2014.01.003SivaramaKrishnan, S., Tangirla., 2008. Sliding mode controller for unstable systems. Chemical and Biochemical Engineering Quarterly 22 (1), 41-47.Smith, C. A., Corripio, A. B., Basurto, S. D. M., 1991. Control automático de procesos: teoría y práctica. No. 968-18-3791-6. Limusa.Sree, P., Chidambaram, M., 2003a. Control of unstable bioreactor with dominant unstable zero. Chemical and Biochemical Engineering Quarterly 17 (3), 139-145.Sree, P., Chidambaram, M., 2003b. A Simple Method of Tuning PI Controllers for Unstable Systems with a Zero. Chemical and Biochemical EngineeringQuarte rly 17 (3), 207-212.Vilanova, R., Alfaro, V. M., 2011. Control PID robusto: Una visión panorámica. Revista Iberoamericana de Automática e Informática Industrial RIAI 8 (3), 141 - 158. https://doi.org/10.1016/j.riai.2011.06.003Yu, W., Wilson, D., Young, B., 2010. Control performance assessment for nonlinear systems. Journal of Process Control 20 (10), 1235 - 1242. https://doi.org/10.1016/j.jprocont.2010.09.00
Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case
Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.
Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.
Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza 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. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. Report, ERASynBio. 2014. https://www.erasynbio.eu/lw_resource/datapool/_items/item_58/erasynbiostrategicvision.pdf .Way J, Collins J, Keasling J, Silver P. Integrating biological redesign: Where synthetic biology came from and where it needs to go. Cell. 2014; 157(1):151–61.Canton B, Labno A, Endy D. Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. 2008; 26(7):787–93.De Lorenzo V, Danchin A. Synthetic biology: discovering new worlds and new words. EMBO Rep. 2008; 9(9):822–7.Church GM, Elowitz MB, Smolke CD, Voigt CA, Weiss R. Realizing the potential of synthetic biology. Nat Rev Mol Cell Biol. 2014; 15(4):289–94.Takahashi CN, Miller AW, Ekness F, Dunham MJ, Klavins E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth Biol. 2015; 4(1):32–8. [doi: 10.1021/sb500165g ].Cooling MT, Rouilly V, Misirli G, Lawson J, Yu T, Hallinan J, Wipat A. Standard virtual biological parts: a repository of modular modeling components for synthetic biology. Bioinformatics. 2010; 26(7):925–31.Medema MH, van Raaphorst R, Takano E, Breitling R. Computational tools for the synthetic design of biochemical pathways. Nat Rev Microbiol. 2012; 10(3):191–202.Marchisio MA, Stelling J. Automatic design of digital synthetic gene circuits. PLoS Comput Biol. 2011; 7(2):e1001083. [doi: 10.1371/journal.pcbi.1001083 ].Rodrigo G, Carrera J, Landrain TE, Jaramillo A. Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Lett. 2012; 586(15):2037–42.Crook N, Alper HS. Model-based design of synthetic, biological systems. Chem Eng Sci. 2013; 103:2–11.Jayanthi S, Nilgiriwala K, Del Vecchio D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth Biol. 2013; 2(8):431–41.Mélykúti B, Hespanha JP, Khammash M. Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks. J R Soc Interface. 2014; 11(97):20140054.Oyarzún DA, Lugagne JB, Stan GB. Noise propagation in synthetic gene circuits for metabolic control. ACS Synth Biol. 2015; 4(2):116–25. [doi: 10.1021/sb400126a ].Picó J, Vignoni A, Picó-Marco E, Boada Y. Modelado de sistemas bioquímicos: De la ley de acción de masas a la aproximación lineal del ruido. Revista Iberoamericana de Automática e Informática Industrial RIAI. 2015; 12(3):241–52.Feng X-j-J, Hooshangi S, Chen D, Li G, Weiss R, Rabitz H. Optimizing genetic circuits by global sensitivity analysis. Biophys J. 2004; 87(4):2195–202.Dasika MS, Maranas CD. Optcircuit: An optimization based method for computational design of genetic circuits. BMC Syst Biol. 2008; 2:24.Rodrigo G, Carrera J, Jaramillo A. Genetdes. Bioinformatics. 2007; 23(14):1857–8.Otero-Muras I, Banga JR. Multicriteria global optimization for biocircuit design. 2014. arXiv preprint arXiv:1402.7323.Banga JR. Optimization in computational systems biology. BMC Syst Biol. 2008; 2:47.Sendin J, Exler O, Banga JR. Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol. 2010; 4(3):236–48.Miller M, Hafner M, Sontag E, Davidsohn N, Subramanian S, Purnick PE, Lauffenburger D, Weiss R. Modular design of artificial tissue homeostasis: robust control through synthetic cellular heterogeneity. PLoS Comput Biol. 2012; 8(7):1002579.Ellis T, Wang X, Collins JJ. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat Biotechnol. 2009; 27(5):465–71.Koeppl H, Hafner M, Lu J. Mapping behavioral specifications to model parameters in synthetic biology. BMC Bioinforma. 2013; 14(Suppl 10):9.Chiang AWT, Hwang M-JJ. A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits. BMC Bioinforma. 2013; 14 Suppl 16:5.Ma W, Trusina A, El-Samad H, Lim WA, Tang C. Defining network topologies that can achieve biochemical adaptation. Cell. 2009; 138(4):760–73.Chiang AWT, Liu W-CC, Charusanti P, Hwang M-JJ. Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters. BMC Syst Biol. 2014; 8:4.Reynoso-Meza G, Blasco X, Sanchis J, Martínez M. Controller tuning using evolutionary multi-objective optimisation: current trends and applications. Control Eng Pract. 2014; 28:58–73.Alon U. An Introduction To Systems Biology. Design Principles of Biological Circuits. London: Chapman & Hall/ CRC Mathematical and computational Biology Series; 2006.Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403(6767):335–8.Hsiao V, de los Santos ELC, Whitaker WR, Dueber JE, Murray RM. Design and implementation of a biomolecular concentration tracker. ACS Synth Biol. 2015; 4(2):150–61. [doi: 10.1021/sb500024b ].Franco E, Giordano G, Forsberg P-O, Murray RM. Negative autoregulation matches production and demand in synthetic transcriptional networks. ACS Synth Biol. 2014; 3(8):589–99. [doi: 10.1021/sb400157z ].Strelkowa N, Barahona M. Switchable genetic oscillator operating in quasi-stable mode. J R Soc Interface. 2010; 7(48):1071–82.Basu S, Mehreja R, Thiberge S, Chen MT, Weiss R. Spatiotemporal control of gene expression with pulse-generating networks. Proc Natl Acad Sci U S A. 2004; 101(17):6355–60.Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol Syst Biol. 2011; 7(519):1–12. [doi: 10.1038/msb.2011.49 ].Hart Y, Antebi YE, Mayo AE, Friedman N, Alon U. Design principles of cell circuits with paradoxical components. Proc Natl Acad Sci. 2012; 109(21):8346–51.Zhang Q, Bhattacharya S, Andersen ME. Ultrasensitive response motifs: basic amplifiers in molecular signalling networks. Open Biol. 2013; 3(4):130031.Weber M, Buceta J, Others. Dynamics of the quorum sensing switch: stochastic and non-stationary effects. BMC Syst Biol. 2013; 7(1):6.Womelsdorf T, Valiante TA, Sahin NT, Miller KJ, Tiesinga P. Dynamic circuit motifs underlying rhythmic gain control, gating and integration. Nat Neurosci. 2014; 17(8):1031–9.Arpino JAJ, Hancock EJ, Anderson J, Barahona M, Stan G-BVB, Papachristodoulou A, Polizzi K. Tuning the dials of synthetic biology. Microbiology. 2013; 159(Pt 7):1236–53.Zagaris A, Kaper HGG, Kaper TJJ. Analysis of the computational singular perturbation reduction method for chemical kinetics. J Nonlinear Sci. 2004; 14(1):59–91.Anderson J, Chang Y-C-C, Papachristodoulou A. Model decomposition and reduction tools for large-scale networks in systems biology. Automatica. 2011; 47(6):1165–74.Prescott TP, Papachristodoulou A. Layered decomposition for the model order reduction of timescale separated biochemical reaction networks. J Theor Biol. 2014; 356:113–22.Hancock EJ, Stan GB, Arpino JAJ, Papachristodoulou A. Simplified mechanistic models of gene regulation for analysis and design. J R Soc Interface. 2015; 12(108).Miettinen K, Vol. 12. Nonlinear Multiobjective Optimization. Boston: Kluwer Academic Publishers; 1999.Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multiobjective optimization: interactive approaches. In: Multiobjective Optimization. Berlin: Springer: 2008. p. 27–57.Deb K, Bandaru S, Greiner D, Gaspar-Cunha A, Tutum CC. An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Appl Soft Comput. 2014; 15(0):42–56.Ang J, Ingalls B, McMillen D. Probing the input-output behavior of biochemical and genetic systems: System identification methods from control theory In: Johnson ML, Brand L, editors. Methods in Enzymology. Academic Press: 2011. p. 279–317, doi: 10.1016/B978-0-12-381270-4.00010-X .Mattson CA, Messac A. Pareto frontier based concept selection under uncertainty, with visualization. Optim Eng. 2005; 6(1):85–115.Reynoso-Meza G, Sanchis J, Blasco X, Martínez M. Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. Appl Evol Comput. 2010;532–541.Reynoso-Meza G, García-Nieto S, Sanchis J, Blasco X. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Trans Control Syst Technol. 2013; 21(2):445–58.Reynoso-Meza G, Sanchis J, Blasco X, Herrero JM. Multiobjective evolutionary algortihms for multivariable PI controller tuning. Expert Syst Appl. 2012; 39:7895–907.Anderson C. Anderson promoter collection [online]. 2006. http://parts.igem.org/Promoters/Catalog/Anderson . Accesed 20 Feb 2015.Salis HM, Mirsky EA, Voigt CA. Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol. 2009; 27(10):946–50.Egbert RG, Klavins E. Fine-tuning gene networks using simple sequence repeats. PNAS. 2012; 109(42):16817–22. [doi: 10.1073/pnas.1205693109 ].Hair JF, Suárez MG. Análisis Multivariante vol. 491. Madrid: Prentice Hall; 1999.Blasco X, Herrero JM, Sanchis J, Martínez M. A new graphical visualization of n-dimensional pareto front for decision-making in multiobjective optimization. Inf Sci. 2008; 178(20):3908–24. [doi: 10.1016/j.ins.2008.06.010 ].Reynoso-Meza G, Blasco X, Sanchis J, Herrero JM. Comparison of design concepts in multi-criteria decision-making using level diagrams. Inform Sci. 2013; 221:124–41.Goentoro L, Shoval O, Kirschner MW, Alon U. The incoherent feedforward loop can provide fold-change detection in gene regulation. Mol Cell. 2009; 36(5):894–9.Rodrigo G, Elena SF. Structural discrimination of robustness in transcriptional feedforward loops for pattern formation. PloS ONE. 2011; 6(2):16904.Kim J, Khetarpal I, Sen S, Murray RM. Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res. 2014; 42(2):6078–89. [doi: 10.1093/nar/gku233 ].Chelliah V, Juty N, Ajmera I, Ali R, Dumousseau M, Glont M, Hucka M, Jalowicki G, Keating S, Knight-Schrijver V, et al. Biomodels: ten-year anniversary. Nucleic Acids Res. 2015; 43(D1):542–8.Ang J, Bagh S, Ingalls BP, McMillen DR. Considerations for using integral feedback control to construct a perfectly adapting synthetic gene network. J Theor Biol. 2010; 266(4):723–38.Biobrick Foundation. 2006. Part Registry [online]. http://partsregistry.org/ . Accessed 20 Feb 2015.BIOSS. 2006. BIOSS Toolbox [online]. http://www.bioss.uni-freiburg.de/cms/toolbox-home.html . Accessed 20 Feb 2015.BioFab. 2006. International Open Facility Advancing Biotechnology [online]. http://www.biofab.org/ . Accessed 20 Feb 2015.Vallerio M, Hufkens J, Van Impe J, Logist F. An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty. Expert Syst Appl. 2015; 42(21):7710–31.Frangopol DM, Maute K. Life-cycle reliability-based optimization of civil and aerospace structures. Comput Struct. 2003; 81(7):397–410.Lozano M, Molina D, Herrera F. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 2011; 15(11):2085–7.Santana-Quintero LV, Montano AA, Coello CAC. A review of techniques for handling expensive functions in evolutionary multi-objective optimization. In: Computational Intelligence in Expensive Optimization Problems. Berlin: Springer: 2010. p. 29–59
Parameter identification in synthetic biological circuits using multi-objective optimization
Tuning of Pareto-optimal robust controllers for multivariable systems. Application on helicopter of two-degress-of-freedom
[ES] La sintonización de controladores Pareto-óptimo robustos ha sido empleada para mejorar el rendimiento de un helicóptero de dos grados de libertad con un algoritmo de control lineal. El procedimiento de sintonización del controlador está basado en la minimización simultánea de las integrales de la suma del cuadrado del error y de la acción de control. Como resultado de dicha minimización y dado que los objetivos entran en conflicto, se obtiene un conjunto de soluciones que describen un frente de Pareto. Posteriormente, un proceso de análisis en los mismos es llevado a cabo para seleccionar los controladores a implementar en el sistema físico. Los resultados experimentales con los controladores seleccionados muestran que el procedimiento de ajuste es eficaz y práctico.[EN] The tuning of Pareto-optimal robust controllers was applied to improve the performance of a helicopter with two-degrees-of-freedom with a linear control algorithm. The tuning procedure is based on the simultaneous minimization of the integral of square sum of errors and the integral of square sum of control action. A 2D Pareto front is built with these integrals. Afterwards, a decision-making process is carried out to select the most preferable controller. Experimental results on the physical platform validate the tuning procedure as practical and reliable.Dirección General de Educación Tecnológica Superior (DGEST), Consejo Nacional de Ciencia y Tecnología (CONACyT), l Ministerio de Economía y Competitividad de España y Consejo Nacional de Desarrollo Científico y Tecnológico de BrasilCarrillo Ahumada, J.; Reynoso Meza, G.; Sanchís Saez, J.; García Nieto, S.; García Alvarado, M. (2015). Sintonización de controladores Pareto-óptimo robustos para sistemas multivariables. Aplicación en un helicóptero de 2 grados de libertad. Revista Iberoamericana de Automática e Informática industrial. 12(2):177-188. https://doi.org/10.1016/j.riai.2015.03.002OJS177188122Carrillo-Ahumada, J., Rodríguez-Jimenes, G. C., & García-Alvarado, M. A. (2011). Tuning optimal-robust linear MIMO controllers of chemical reactors by using Pareto optimality. Chemical Engineering Journal, 174(1), 357-367. doi:10.1016/j.cej.2011.09.007Coello 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.2006.1597059Corne, D.W., Knowles, J.D., 2007. Techniques for highly multiobjective optimisation: some nondominated points are better than others. En: Proceedings of the 9th annual conference on Genetic and evolutionary computation. GECCO ‘07. ACM, New York, NY, USA, pp. 773-780.CSS, 2012. Unmanned aerial vehicle. special issue. IEEE Control Systems magazine 32 (5).Unmanned Aerial Vehicles and Control: Lockheed Martin Advanced Technology Laboratories. (2012). IEEE Control Systems, 32(5), 32-34. doi:10.1109/mcs.2012.2205474Gabriel, C., 2008. Modelling, simulation and control of a twin rotor mimo-system. Master thesis, Polytechnic University of Valencia, Spain.García-Sanz, M., & Elso, J. (2007). Ampliación del benchmark de diseño de controladores para el cabeceo de un helicóptero. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(1), 107-110. doi:10.1016/s1697-7912(07)70196-6García-Sanz, M., & Elso, J. (2007). Resultados Del Benchmark de Diseño De Controladores Para el Cabeceo de un Helicóptero. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(4), 117-120. doi:10.1016/s1697-7912(07)70251-0Garcia-Alvarado, M. A., & Ruiz-López, I. I. (2010). A design method for robust and quadratic optimal MIMO linear controllers. Chemical Engineering Science, 65(11), 3431-3438. doi:10.1016/j.ces.2010.02.033Hernández, L. H., Pestana, J., Palomeque, D. C., Campoy, P., & Sanchez-Lopez, J. L. (2013). Identificación y control en cascada mediante inversión de no linealidades del cuatrirrotor para el Concurso de Ingeniería de Control CEA IFAC 2012. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(3), 356-367. doi:10.1016/j.riai.2013.05.008Huba, M. (2013). Performance measures, performance limits and optimal PI control for the IPDT plant. Journal of Process Control, 23(4), 500-515. doi:10.1016/j.jprocont.2013.01.002Ishibuchi, H., Tsukamoto, N., Nojima, Y., 2008. Evolutionary many-objective optimization: A short review. En: Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on.Juang, J.-G., Lin, R.-W., & Liu, W.-K. (2008). Comparison of classical control and intelligent control for a MIMO system. Applied Mathematics and Computation, 205(2), 778-791. doi:10.1016/j.amc.2008.05.061Juang, J.-G., Liu, W.-K., & Lin, R.-W. (2011). A hybrid intelligent controller for a twin rotor MIMO system and its hardware implementation. ISA Transactions, 50(4), 609-619. doi:10.1016/j.isatra.2011.06.006Lotov, A., Miettinen, K., 2008. Visualizing the Pareto frontier. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (Eds.), Multiobjective Optimization. Vol. 5252 of Lecture Notes in Computer Science. Springer Berlin /Heidel-berg, pp. 213-243.Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369-395. doi:10.1007/s00158-003-0368-6Rahideh, A., Shaheed, M., april 2009. Robust model predictive control of a twin rotor mimo system. En: Mechatronics, 2009. ICM 2009. IEEE International Conference on. pp. 1-6.Rahideh, A., & Shaheed, M. H. (2012). Constrained output feedback model predictive control for nonlinear systems. Control Engineering Practice, 20(4), 431-443. doi:10.1016/j.conengprac.2011.12.003Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J.M., August 2014a. A stabilizing PID controller sampling procedure for stochastic optimizers. En: Memories of the 19th World Congress IFAC 2014. pp. 8158-8163.Reynoso-Meza, G., Blasco, X., Sanchis, J., & Martínez, M. (2014). Controller tuning using evolutionary multi-objective optimisation: Current trends and applications. Control Engineering Practice, 28, 58-73. doi:10.1016/j.conengprac.2014.03.003Reynoso-Meza, G., Sánchez, H.S., Blasco, X., Vilanova, R., August 2014c. Reliability based multiobjective optimization design procedure for PI controller tuning. En: Memories of the 19th World Congress IFAC 2014. pp. 10263-10268.Ruiz-López, I. I., Rodríguez-Jimenes, G. C., & García-Alvarado, M. A. (2006). Robust MIMO PID controllers tuning based on complex/real ratio of the characteristic matrix eigenvalues. Chemical Engineering Science, 61(13), 4332-4340. doi:10.1016/j.ces.2006.02.015Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. doi:10.1016/s0959-1524(02)00062-8Sánchez, H.S., Vilanova, R., 2013a. Multiobjective tuning of PI controller using the NNC method: Simplified problem definition and guidelines for decision making. En: Proceedings of the 18th. IEEE Conference on emerging technologies & factory automation (ETFA).Sánchez, H.S., Vilanova, R., 2013b. Nash-based criteria for selection of pareto optimal controller. En: Proceedings of the 17th. International Conference on System Theory, Control anmd Computing.Tao, C. W., Taur, J. S., & Chen, Y. C. (2010). Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system. Fuzzy Sets and Systems, 161(15), 2081-2103. doi:10.1016/j.fss.2009.12.007Tao, C.-W., Taur, J.-S., Chang, Y.-H., & Chang, C.-W. (2010). A Novel Fuzzy-Sliding and Fuzzy-Integral-Sliding Controller for the Twin-Rotor Multi-Input–Multi-Output System. IEEE Transactions on Fuzzy Systems, 18(5), 893-905. doi:10.1109/tfuzz.2010.2051447Toha, S., Tokhi, M., nov. 2009. Dynamic nonlinear inverse-model based control of a twin rotor system using adaptive neuro-fuzzy inference system. En: Computer Modeling and Simulation, 2009. EMS ‘09. Third UKSim European Symposium on. pp. 107-111.Vargas, F. J., Salgado, M. E., & Silva, E. I. (2011). Optimal ripple-free deadbeat control using an integral of time squared error (ITSE) index. Automatica, 47(9), 2134-2137. doi:10.1016/j.automatica.2011.06.006Velasco, J., García-Nieto, S., Reynoso-Meza, G., Sanchis, J., 2013. Implementación de un sistema hardware-in-the-loop para la simulación en tiempo real de pilotos automáticos para uavs. En: de Automática, C.E. (Ed.), Memorias de las XXXIV Jornadas de Automática.Vilanova, R., Alfaro, V. M., & Arrieta, O. (2012). Simple robust autotuning rules for 2-DoF PI controllers. ISA Transactions, 51(1), 30-41. doi:10.1016/j.isatra.2011.09.001Wen, P., & Lu, T.-W. (2008). Decoupling control of a twin rotor MIMO system using robust deadbeat control technique. IET Control Theory & Applications, 2(11), 999-1007. doi:10.1049/iet-cta:20070335Witczak, M., Puig, V., de Oca, S., oct. 2010. A fault-tolerant control scheme for non-linear discrete-time systems: Application to the twin-rotor system. En: Control and Fault-Tolerant Systems (SysTol), 2010 Conference on. pp. 861-866
Comparative study of auto-tuning algorithms for PID controllers. Results of the 2010-2011 match of the control engineering group of CEA
In this paper three PID auto-tuning algorithms which are mainly focused on the disturbance rejection problem are compared. The algorithms differ in both the experiments carried out to obtain information of the process dynamic and the methods for calculating the controller parameters. The algorithms were presented in the 2010-2011 Match of the Control Engineering Group of CEA. The comparison is based on an evaluation methodology that takes into account the experimental phase as well as the closed loop performance during the control phase. © 2011 CEA. Publicado por Elsevier España, S.L