Mostrar el registro sencillo del ítem
dc.contributor.author | Reynoso Meza, Gilberto | es_ES |
dc.contributor.author | Sanchís Saez, Javier | es_ES |
dc.contributor.author | Blasco Ferragud, Francesc Xavier | es_ES |
dc.contributor.author | Martínez Iranzo, Miguel Andrés | es_ES |
dc.date.accessioned | 2020-05-21T09:19:15Z | |
dc.date.available | 2020-05-21T09:19:15Z | |
dc.date.issued | 2013-07-09 | |
dc.identifier.issn | 1697-7912 | |
dc.identifier.uri | http://hdl.handle.net/10251/143934 | |
dc.description.abstract | [ES] Los controladores PID continúan siendo una solución fiable, robusta, práctica y sencilla para el control de procesos. Actualmente constituyen la primera capa de control de la gran mayoría de las aplicaciones industriales. De ahí que un número importante de trabajos de investigación se han orientado a mejorar su rendimiento y prestaciones. Las líneas de investigación en este campo van desde nuevos métodos de ajuste, pasando por nuevos tipos de estructura hasta metodologías de diseño integrales. Particularizando en el ajuste de parámetros, una de las formas de obtener una solución novedosa consiste en plantear un problema de optimización, el cual puede llegar a ser no-lineal, no-convexo y con restricciones. Dado que los algoritmos evolutivos han mostrado un buen desempeño para solucionar problemas complejos de optimización, han sido utilizados en diversas propuestas relacionadas con el ajuste de controladores PID. Este trabajo muestra un revisión de estas propuestas y las prestaciones obtenidas en cada caso. Así mismo, se identifican algunas tendencias y posibles líneas de trabajo futuras. | es_ES |
dc.description.abstract | [EN] PID controllers are a reliable, robust, practical and easy to implement control solution for industrial processes. They provide the first control layer for a vast majority of industrial applications. Owing to this, several researches invest time and resources to improve their performance. The research lines in this field scope with new tuning methods, new types of structures and integral design methods. For tuning methods, improvements could be fulfilled stating an optimization problem, which could be non-linear, non-convex and highly constrained. In such instances, evolutionary algorithms have shown a good performance and have been used in various proposals related with PID controllers tuning. This work shows a review of these proposals and the benefits obtained in each case. Some trends and possible future research lines are also identified. | es_ES |
dc.description.sponsorship | Este trabajo ha sido realizado parcialmente gracias al apoyo del Ministerio de Economía y Competitividad (Gobierno de España) mediante los proyectos TIN2011 - 28082, ENE2011- 25900; la Generalitat Valenciana mediante la iniciativa GV/2012/ 073 y la Universitat Politècnica de València a travès de la beca FPI-2010/19 y la iniciativa de investigacion PAID-06-11. | |
dc.language | Español | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | Revista Iberoamericana de Automática e Informática industrial | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Controlador PID | es_ES |
dc.subject | PID convencional | es_ES |
dc.subject | PID borroso | es_ES |
dc.subject | PID fraccionario | es_ES |
dc.subject | Algoritmos Evolutivos | es_ES |
dc.subject | Optimización | es_ES |
dc.subject | PID controller | es_ES |
dc.subject | Conventional PID | es_ES |
dc.subject | Fuzzy PID | es_ES |
dc.subject | Fractional order PID | es_ES |
dc.subject | Evolutionary algorithms | es_ES |
dc.subject | Optimization | es_ES |
dc.title | Algoritmos Evolutivos y su empleo en el ajuste de controladores del tipo PID: Estado Actual y Perspectivas | es_ES |
dc.title.alternative | Evolutionary Algorithms for PID controller tuning: Current Trends and Perspectives | es_ES |
dc.type | Artículo | es_ES |
dc.type | Otros | es_ES |
dc.identifier.doi | 10.1016/j.riai.2013.04.001 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TIN2011-28082/ES/DISEÑO E IMPLEMENTACION DE PILOTOS AUTOMATICOS PARA VEHICULOS AEREOS NO TRIPULADOS (UAVS) MEDIANTE TECNICAS DE OPTIMIZACION Y CONTROL AVANZADO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//ENE2011-25900/ES/GESTION OPTIMA MEDIANTE CONTROLADORES AVANZADOS DE PILAS DE COMBUSTIBLE TIPO PEM PARA APLICACIONES MOVILES Y ESTATICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//GV%2F2012%2F073/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//FPI%2F2010%2F19/ | |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//PAID-06-11/ | |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica | es_ES |
dc.description.bibliographicCitation | Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Martínez Iranzo, MA. (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. 10(3):251-268. https://doi.org/10.1016/j.riai.2013.04.001 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.riai.2013.04.001 | es_ES |
dc.description.upvformatpinicio | 251 | es_ES |
dc.description.upvformatpfin | 268 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 10 | es_ES |
dc.description.issue | 3 | es_ES |
dc.identifier.eissn | 1697-7920 | |
dc.relation.pasarela | OJS\9510 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.description.references | Algoul, S., Alam, M. S., Hossain, M. A., & Majumder, M. A. A. (2010). Multi-objective optimal chemotherapy control model for cancer treatment. Medical & Biological Engineering & Computing, 49(1), 51-65. doi:10.1007/s11517-010-0678-y | es_ES |
dc.description.references | Kiam Heong Ang, Chong, G., & Yun Li. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559-576. doi:10.1109/tcst.2005.847331 | es_ES |
dc.description.references | Åström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163-1175. doi:10.1016/s0967-0661(01)00062-4 | es_ES |
dc.description.references | Åström, K.J., Hägglund, T., 2005. Advanced PID Control. ISA - The Instrumentation, Systems, and Automation Society, Research Triangle Park, NC 27709. | es_ES |
dc.description.references | ÅSTRÖM, 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)00011-9 | es_ES |
dc.description.references | Avigad, G., Moshaiov, A., Brauner, N., (2003). june Towards a general tool for mechatronic design. In: Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on. Vol. 2. pp. 1035-1040 vol.2. | es_ES |
dc.description.references | Hultmann Ayala, H. V., & dos Santos Coelho, L. (2012). Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Systems with Applications, 39(10), 8968-8974. doi:10.1016/j.eswa.2012.02.027 | es_ES |
dc.description.references | Behbahani, S., & de Silva, C. W. (2008). System-Based and Concurrent Design of a Smart Mechatronic System Using the Concept of Mechatronic Design Quotient (MDQ). IEEE/ASME Transactions on Mechatronics, 13(1), 14-21. doi:10.1109/tmech.2007.915058 | es_ES |
dc.description.references | Beyer, H.-G., & Sendhoff, B. (2007). Robust optimization – A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33-34), 3190-3218. doi:10.1016/j.cma.2007.03.003 | es_ES |
dc.description.references | Bingül, Z., & Karahan, O. (2011). A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Systems with Applications, 38(1), 1017-1031. doi:10.1016/j.eswa.2010.07.131 | es_ES |
dc.description.references | Biswas, A., Das, S., Abraham, A., & Dasgupta, S. (2009). Design of fractional-order PIλDμ controllers with an improved differential evolution. Engineering Applications of Artificial Intelligence, 22(2), 343-350. doi:10.1016/j.engappai.2008.06.003 | es_ES |
dc.description.references | Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.010 | es_ES |
dc.description.references | Bonissone, 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.2009.933093 | es_ES |
dc.description.references | Caballero, J. A., & Grossmann, I. E. (2007). Una revisión del estado del arte en optimización. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(1), 5-23. doi:10.1016/s1697-7912(07)70188-7 | es_ES |
dc.description.references | Coello, C., 2000. Handling preferences in evolutionary multiobjective optimization: a survey. In: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. Vol. 1. pp. 30-37 vol.1. | es_ES |
dc.description.references | Coello 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)00323-1 | es_ES |
dc.description.references | Coello 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.2006.1597059 | es_ES |
dc.description.references | Coello, C., 2011. An introduction to multi-objective particle swarm optimizers. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (Eds.), Soft Computing in Industrial Applications. Vol. 96 of Advances in Intelligent and Soft Computing. Springer Berlin /Heidelberg, pp. 3-12, 10,1007/978 − 3 − 642 − 20505 − 71. | es_ES |
dc.description.references | Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning, 52(6), 894-913. doi:10.1016/j.ijar.2011.03.004 | es_ES |
dc.description.references | Corne, D.W., Knowles, J.D., 2007. Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. GECCO ‘07. ACM, New York, NY, USA, pp. 773-780. | es_ES |
dc.description.references | Cruz, C., González, J. R., & Pelta, D. A. (2010). Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing, 15(7), 1427-1448. doi:10.1007/s00500-010-0681-0 | es_ES |
dc.description.references | Das, S., Maity, S., Qu, B.-Y., & Suganthan, P. N. (2011). Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-art. Swarm and Evolutionary Computation, 1(2), 71-88. doi:10.1016/j.swevo.2011.05.005 | es_ES |
dc.description.references | Das, R. T., Ang, K. K., & Quek, C. (2010). A synergy of econometrics and computational methods (GARCH-RNFS) for volatility forecasting. IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2010.5586324 | es_ES |
dc.description.references | Das, S., Suganthan, P., 2011. Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. rep., Jadavpur university and Nanyang Technological University. | es_ES |
dc.description.references | Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 311-338. doi:10.1016/s0045-7825(99)00389-8 | es_ES |
dc.description.references | Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017 | es_ES |
dc.description.references | Dixon, R., & Pike, A. W. (2006). Alstom Benchmark Challenge II on Gasifier Control. IEE Proceedings - Control Theory and Applications, 153(3), 254-261. doi:10.1049/ip-cta:20050062 | es_ES |
dc.description.references | Eiben, A. E., & Schippers, C. A. (1998). On Evolutionary Exploration and Exploitation. Fundamenta Informaticae, 35(1-4), 35-50. doi:10.3233/fi-1998-35123403 | es_ES |
dc.description.references | Elgammal, A. A. A., & Sharaf, A. M. (2012). Self-regulating particle swarm optimised controller for (photovoltaic–fuel cell) battery charging of hybrid electric vehicles. IET Electrical Systems in Transportation, 2(2), 77. doi:10.1049/iet-est.2011.0021 | es_ES |
dc.description.references | Fazendeiro, P., de Oliveira, J. V., & Pedrycz, W. (2007). A Multiobjective Design of a Patient and Anaesthetist-Friendly Neuromuscular Blockade Controller. IEEE Transactions on Biomedical Engineering, 54(9), 1667-1678. doi:10.1109/tbme.2007.895109 | es_ES |
dc.description.references | Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., & Herrera, F. (2013). A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE Transactions on Fuzzy Systems, 21(1), 45-65. doi:10.1109/tfuzz.2012.2201338 | es_ES |
dc.description.references | Figueira, J., Greco, S., Ehrgott, M., 2005. Multiple criteria decision analysis: State of the art surveys. Springer international series. | es_ES |
dc.description.references | Fleming, P. ., & Purshouse, R. . (2002). Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10(11), 1223-1241. doi:10.1016/s0967-0661(02)00081-3 | es_ES |
dc.description.references | Fonseca, C. M., & Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 28(1), 26-37. doi:10.1109/3468.650319 | es_ES |
dc.description.references | Gaing, Z.-L. (2004). A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System. IEEE Transactions on Energy Conversion, 19(2), 384-391. doi:10.1109/tec.2003.821821 | es_ES |
dc.description.references | Goldberg, D., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA. | es_ES |
dc.description.references | Hajiloo, A., Nariman-zadeh, N., & Moeini, A. (2012). Pareto optimal robust design of fractional-order PID controllers for systems with probabilistic uncertainties. Mechatronics, 22(6), 788-801. doi:10.1016/j.mechatronics.2012.04.003 | es_ES |
dc.description.references | Herreros, A., Baeyens, E., & Perán, J. R. (2002). Design of PID-type controllers using multiobjective genetic algorithms. ISA Transactions, 41(4), 457-472. doi:10.1016/s0019-0578(07)60102-5 | es_ES |
dc.description.references | HUANG, L., WANG, N., & ZHAO, J.-H. (2008). Multiobjective Optimization for Controller Design. Acta Automatica Sinica, 34(4), 472-477. doi:10.3724/sp.j.1004.2008.00472 | es_ES |
dc.description.references | Inselberg, A. (1985). The plane with parallel coordinates. The Visual Computer, 1(2), 69-91. doi:10.1007/bf01898350 | es_ES |
dc.description.references | Iruthayarajan, M. W., & Baskar, S. (2009). Evolutionary algorithms based design of multivariable PID controller. Expert Systems with Applications, 36(5), 9159-9167. doi:10.1016/j.eswa.2008.12.033 | es_ES |
dc.description.references | Kamath, S., George, V. I., & Vidyasagar, S. (2009). A comparative study of different types of controllers used for blood glucose regulation system. The Canadian Journal of Chemical Engineering, 87(5), 812-817. doi:10.1002/cjce.20219 | es_ES |
dc.description.references | Kaveh, P., & Shtessel, Y. B. (2008). Blood glucose regulation using higher-order sliding mode control. International Journal of Robust and Nonlinear Control, 18(4-5), 557-569. doi:10.1002/rnc.1223 | es_ES |
dc.description.references | Kollat, J. B., & Reed, P. (2007). A framework for Visually Interactive Decision-making and Design using Evolutionary Multi-objective Optimization (VIDEO). Environmental Modelling & Software, 22(12), 1691-1704. doi:10.1016/j.envsoft.2007.02.001 | es_ES |
dc.description.references | Konak, A., Coit, D.W., Smith, A.E., 2006. Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 91 (9), 992-1007, special Issue - Genetic Algorithms and Reliability. | es_ES |
dc.description.references | Koza, J. R., Keane, M. A., & Streeter, M. J. (2003). What’s AI done for me lately? Genetic programming’s human-competitive results. IEEE Intelligent Systems, 18(3), 25-31. doi:10.1109/mis.2003.1200724 | es_ES |
dc.description.references | Koza, J., Poli, R., 2005. Genetic programming. In: Burke, E.K., Kendall, G. (Eds.), Search Methodologies. Springer US, pp. 127-164, 10.1007/0-387-28356-0 5. | es_ES |
dc.description.references | Lamanna, R., Vega, P., Revollar, S., & Alvarez, H. (2009). Diseño Simultáneo de Proceso y Control de una Torre Sulfitadora de Jugo de Caña de Azúcar. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(3), 32-43. doi:10.1016/s1697-7912(09)70262-6 | es_ES |
dc.description.references | Lee, C.-H., & Chang, F.-K. (2010). Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Systems with Applications, 37(12), 8871-8878. doi:10.1016/j.eswa.2010.06.009 | es_ES |
dc.description.references | PID control system analysis and design. (2006). IEEE Control Systems, 26(1), 32-41. doi:10.1109/mcs.2006.1580152 | es_ES |
dc.description.references | Lin, C.-M., Li, M.-C., Ting, A.-B., & Lin, M.-H. (2011). A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. International Journal of Machine Learning and Cybernetics, 2(4), 225-234. doi:10.1007/s13042-011-0021-4 | es_ES |
dc.description.references | Lotov, 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 /Heidelberg, pp. 213-243. | es_ES |
dc.description.references | Luyben, W. L. (1986). Simple method for tuning SISO controllers in multivariable systems. Industrial & Engineering Chemistry Process Design and Development, 25(3), 654-660. doi:10.1021/i200034a010 | es_ES |
dc.description.references | Mallipeddi, R., Suganthan, P., 2009. Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Tech. rep., Nanyang Technological University, Singapore. | es_ES |
dc.description.references | 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/s00158-003-0368-6 | es_ES |
dc.description.references | Mattson, C.A., Messac, A., 2005 Pareto frontier based concept selection under uncertainty, with visualization. Optimization and Engineering 6, 85-115, 10.1023/B:OPTE. 0000048538.35456.45. | es_ES |
dc.description.references | Menhas, M. I., Fei, M., Wang, L., & Qian, L. (2012). Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system. Energy Conversion and Management, 54(1), 67-80. doi:10.1016/j.enconman.2011.10.001 | es_ES |
dc.description.references | Menhas, M. I., Wang, L., Fei, M., & Pan, H. (2012). Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Systems with Applications, 39(4), 4390-4401. doi:10.1016/j.eswa.2011.09.152 | es_ES |
dc.description.references | Messac, A. (1996). Physical programming - Effective optimization for computational design. AIAA Journal, 34(1), 149-158. doi:10.2514/3.13035 | es_ES |
dc.description.references | Mezura-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.2011.10.001 | es_ES |
dc.description.references | Mezura-Montes, E., Reyes-Sierra, M., & Coello, C. A. C. (2008). Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art. Studies in Computational Intelligence, 173-196. doi:10.1007/978-3-540-68830-3_7 | es_ES |
dc.description.references | Oh, S.-K., Kim, W.-D., & Pedrycz, W. (2012). Design of optimized cascade fuzzy controller based on differential evolution: Simulation studies and practical insights. Engineering Applications of Artificial Intelligence, 25(3), 520-532. doi:10.1016/j.engappai.2012.01.002 | es_ES |
dc.description.references | Pan, I., Das, S., & Gupta, A. (2011). Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA Transactions, 50(1), 28-36. doi:10.1016/j.isatra.2010.10.005 | es_ES |
dc.description.references | Rao, J. S., & Tiwari, R. (2009). Design optimization of double-acting hybrid magnetic thrust bearings with control integration using multi-objective evolutionary algorithms. Mechatronics, 19(6), 945-964. doi:10.1016/j.mechatronics.2009.06.011 | es_ES |
dc.description.references | Reynoso-Meza, G., Blasco, X., & Sanchis, J. (2009). Diseño Multiobjetivo de controladores PID para el Benchmark de Control 2008–2009. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(4), 93-103. doi:10.1016/s1697-7912(09)70112-8 | es_ES |
dc.description.references | Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J., september 2011a. Handling control engineer preferences: Getting the most of PI controllers. In: Emerging Technologies Factory Automation (ETFA), 2011 IEEE 16th Conference on. pp. 1-8. | es_ES |
dc.description.references | Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J., june 2011b. Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems. In: Evolutionary Computation (CEC), 2011 IEEE Congress on. pp. 1551-1556. | es_ES |
dc.description.references | Reynoso-Meza, G., Blasco, X., Sanchis, J., March 2012a. Optimización evolutiva multi-objetivo y selección multi-criterio para la ingeniería de control. In: X Simposio CEA de Ingeniería de Control. | es_ES |
dc.description.references | Reynoso-Meza, G., García-Nieto, S., Sanchis, J., Blasco, X., 2012b. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Article in press. | es_ES |
dc.description.references | Reynoso-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.2012.01.111 | es_ES |
dc.description.references | Reynoso-Meza, G., Blasco, X., Sanchis, J., & Herrero, J. M. (2013). Comparison of design concepts in multi-criteria decision-making using level diagrams. Information Sciences, 221, 124-141. doi:10.1016/j.ins.2012.09.049 | es_ES |
dc.description.references | Romero-Pérez, J. A., Arrieta, O., Padula, F., Reynoso-Meza, G., Garcia-Nieto, S., & Balaguer, P. (2012). Estudio comparativo de algoritmos de auto-ajuste de controladores PID. Resultados del Benchmark 2010-2011 del Grupo de Ingeniería de Control de CEA. Revista Iberoamericana de Automática e Informática Industrial RIAI, 9(2), 182-193. doi:10.1016/j.riai.2012.02.009 | es_ES |
dc.description.references | Roy, R., Hinduja, S., & Teti, R. (2008). Recent advances in engineering design optimisation: Challenges and future trends. CIRP Annals, 57(2), 697-715. doi:10.1016/j.cirp.2008.09.007 | es_ES |
dc.description.references | Sanchis, 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.2010.07.005 | es_ES |
dc.description.references | Santana-Quintero, L. V., Montaño, A. A., & Coello, C. A. C. (2010). A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization. Evolutionary Learning and Optimization, 29-59. doi:10.1007/978-3-642-10701-6_2 | es_ES |
dc.description.references | Saridakis, K. M., & Dentsoras, A. J. (2008). Soft computing in engineering design – A review. Advanced Engineering Informatics, 22(2), 202-221. doi:10.1016/j.aei.2007.10.001 | es_ES |
dc.description.references | Shi, L., & Rasheed, K. (2010). A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms. Evolutionary Learning and Optimization, 3-28. doi:10.1007/978-3-642-10701-6_1 | es_ES |
dc.description.references | Panda, S. (2011). Multi-objective PID controller tuning for a FACTS-based damping stabilizer using Non-dominated Sorting Genetic Algorithm-II. International Journal of Electrical Power & Energy Systems, 33(7), 1296-1308. doi:10.1016/j.ijepes.2011.06.002 | es_ES |
dc.description.references | Skogestad, 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)00062-8 | es_ES |
dc.description.references | Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328 | es_ES |
dc.description.references | Tan, W., Liu, J., Fang, F., & Chen, Y. (2004). Tuning of PID controllers for boiler-turbine units. ISA Transactions, 43(4), 571-583. doi:10.1016/s0019-0578(07)60169-4 | es_ES |
dc.description.references | Tan, W. W., Lu, F., Loh, A. P., & Tan, K. C. (2005). Modeling and control of a pilot pH plant using genetic algorithm. Engineering Applications of Artificial Intelligence, 18(4), 485-494. doi:10.1016/j.engappai.2004.11.006 | es_ES |
dc.description.references | Vilanova, R., & Alfaro, V. M. (2011). Control PID robusto: Una visión panorámica. Revista Iberoamericana de Automática e Informática Industrial RIAI, 8(3), 141-158. doi:10.1016/j.riai.2011.06.003 | es_ES |
dc.description.references | Xue, Y., Li, D., & Gao, F. (2010). Multi-objective optimization and selection for the PI control of ALSTOM gasifier problem. Control Engineering Practice, 18(1), 67-76. doi:10.1016/j.conengprac.2009.09.004 | es_ES |
dc.description.references | Zamani, M., Karimi-Ghartemani, M., Sadati, N., & Parniani, M. (2009). Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice, 17(12), 1380-1387. doi:10.1016/j.conengprac.2009.07.005 | es_ES |
dc.description.references | Zhang, J., Zhuang, J., Du, H., & Wang, S. (2009). Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences, 179(7), 1007-1018. doi:10.1016/j.ins.2008.11.038 | es_ES |
dc.description.references | Zhao, S.-Z., Iruthayarajan, M. W., Baskar, S., & Suganthan, P. N. (2011). Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Information Sciences, 181(16), 3323-3335. doi:10.1016/j.ins.2011.04.003 | es_ES |
dc.description.references | Zhou, 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.2011.03.001 | es_ES |