Información del artículo
Resumen
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.
Palabras clave:
Controlador PID
PID convencional
PID borroso
PID fraccionario
Algoritmos Evolutivos
Optimización
Abstract
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.
Keywords:
PID controller
Conventional PID
Fuzzy PID
Fractional order PID
Evolutionary algorithms
Optimization.
Texto completo
Referencias no citadas
Algoul et al., 2011, Ang et al., 2005, Åström and Hägglund, 2001, Åström and Hägglund, 2005, Åström et al., 1998, Avigad et al., 2003, Ayala and dos Santos Coelho, 2012, Behbahani and de Silva, 2008, Beyer and Sendhoff, 2007, Bingul and Karahan, 2011, Biswas et al., 2009, Blasco et al., 2008, Bonissone et al., 2009, Caballero and Grossmann, 2011, Coello, 2000, Coello, 2002, Coello Coello, 2006, Coello, 2011, Cordón, 2011, Corne and Knowles, 2007, Cruz et al., 2011, Das et al., 2011, Das and Suganthan, 2010, Das and Suganthan, 2011, Deb, 2000, Deb et al., 2002, Dixon and Pike, 2006, Eiben and Schippers, 1998, Elgammal and Sharaf, 2012, Elsayed et al., 2011, Fazendeiro et al., 2007, Fazzolar et al., 2013, Figueira et al., 2005, Fleming and Purshouse, 2002, Fonseca and Fleming, 1998a, Fonseca and Fleming, 1998b, Gaing, 2004, Goldberg, 1989, Hajiloo et al., 2012, Hansen, 2006, Harik et al., 1999, Herreros et al., 2002, Holland, 1975, Huang et al., 2008, Hung et al., 2008, Inselberg, 1985, Iruthayarajan and Baskar, 2009, Ishibuchi et al., 2008, Jiachuan et al., 2005, Juang et al., 2008, Kamath et al., 2009, Kaveh and Shtessel, 2008, Kennedy and Eberhart, 1995, Kollat and Reed, 2007, Konak et al., 2006, Koza et al., 2003, Koza and Poli, 2005, Lamanna et al., 2009, Lee and Chang, 2010, Li et al., 2006, Lin et al., 2011, Lotov and Miettinen, 2008, Lozano et al., 2011, Luyben, 1986, Mallipeddi and Suganthan, 2009, Marler and Arora, 2004, Mattson and Messac, 2005, Menhas et al., 2012a, Menhas et al., 2012b, Messac, 1996, Mezura-Montes and Coello, 2011, Mezura-Montes et al., 2008, Miettinen, 1998, Mininno et al., 2011, Oh et al., 2012, Pan et al., 2011, Podlubny, 1999, Rao and Tiwari, 2009, Reynoso-Meza et al., 2009, Reynoso-Meza et al., 2011, Reynoso-Meza et al., 2011a, Reynoso-Meza et al., 2012a, Reynoso-Meza et al., 2012b, Reynoso-Meza et al., 2012c, Reynoso-Meza et al., 2013, Romero-Pérez et al., 2012, Roy et al., 2008, Sanchis et al., 2010, Santana-Quintero et al., 2010, Saridakis and Dentsoras, 2008, Shi and Rasheed, 2010, Sidhartha Panda, 2011, Skogestad, 2003, Stewart and Samad, 2011, Storn and Price, 1997, Tan et al., 2004, Tan et al., 2005, Tavakoli et al., 2007, Vilanova and Alfaro, 2011, Xue et al., 2010, Zamani et al., 2009, Zhang et al., 2009, Zhang and Li, 2007, Zhao et al., 2011 and Zhou et al., 2011.
Referencias
[Algoul et al., 2011]
S. Algoul, M. Alam, M. Hossain, M. Majumder.
Multi-objective optimal chemotherapy control model for cancer treatment.
Medical and Biological Engineering and Computing, 49 (2011), pp. 51-65
[Ang et al., 2005]
K.H. Ang, G. Chong, Y. Li.
PID control system analysis, design, and technology.
Control Systems Technology, IEEE Transactions on, 13 (2005 july), pp. 559-576
[Åström and Hägglund, 2001]
K. Åström, T. Hägglund.
The future of PID control.
Control Engineering Practice, 9 (2001), pp. 1163-1175
[Åström and Hägglund, 2005]
Åström, K.J., Hägglund, T., 2005. Advanced PID Control. ISA - The Instrumentation, Systems, and Automation Society, Research Triangle Park, NC 27709.
[Åström et al., 1998]
K. Åström, H. Panagopoulos, T. Hägglund.
Design of PI controllers based on non-convex optimization.
Automatica, 34 (1998), pp. 585-601
[Avigad et al., 2003]
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.
[Ayala and dos Santos Coelho, 2012]
H.V.H. Ayala, L. dos Santos Coelho.
Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator.
Expert Systems with Applications, 39 (2012), pp. 8968-8974
[Behbahani and de Silva, 2008]
S. Behbahani, C. de Silva.
System-based and concurrent design of a smart mechatronic system using the concept of mechatronic design quotient (MDQ). Mechatronics.
IEEE/ASME Transactions on, 13 (2008 feb.), pp. 14-21
[Beyer and Sendhoff, 2007]
H.-G. Beyer, B. Sendhoff.
Robust optimization a comprehensive survey.
Computer Methods in Applied Mechanics and Engineering, 196 (2007), pp. 3190-3218
[Bingul and Karahan, 2011]
Z. Bingul, O. Karahan.
A fuzzy logic controller tuned with PSO for 2 dof robot trajectory control.
Expert Systems with Applications, 38 (2011), pp. 1017-1031
[Biswas et al., 2009]
A. Biswas, S. Das, A. Abraham, S. Dasgupta.
Design of fractionalorder PIλDμ controllers with an improved differential evolution.
Engineering Applications of Artificial Intelligence, 22 (2009), pp. 343-350
[Blasco et al., 2008]
X. Blasco, J. Herrero, J. Sanchis, M. Martínez.
A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization.
Information Sciences, 178 (2008), pp. 3908-3924
[Bonissone et al., 2009]
P. Bonissone, R. Subbu, J. Lizzi.
Multicriteria decision making (mcdm): a framework for research and applications.
Computational Intelligence Magazine, IEEE, 4 (2009 aug), pp. 48-61
[Caballero and Grossmann, 2011]
J.A. Caballero, I.E. Grossmann.
Una revisión del estado del arte en optimización.
Revista Iberoamericana de Automática e Informática Industrial, 4 (2011), pp. 5-23
[Coello, 2000]
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.
[Coello, 2002]
C.A.C. Coello.
Theorical 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 (2002), pp. 1245-1287
[Coello Coello, 2006]
C. Coello Coello.
Evolutionary multi-objective optimization: A historical view of the field.
Computational Intelligence Magazine, IEEE, 1 (2006 feb.), pp. 28-36
[Coello, 2011]
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.
[Cordón, 2011]
O. Cordón.
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 (2011), pp. 894-913
[Corne and Knowles, 2007]
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.
[Cruz et al., 2011]
C. Cruz, J. González, D.A. Pelta.
Optimization in dynamic environments: a survey on problems, methods and measures.
Soft Computing, 15 (2011), pp. 1427-1448
[Das et al., 2011]
S. Das, S. Maity, B.-Y. Qu, P. Suganthan.
Real-parameter evolutionary multimodal optimization a survey of the state-of-the-art.
Swarm and Evolutionary Computation, 1 (2011), pp. 71-88
[Das and Suganthan, 2010]
S. Das, P.N. Suganthan.
Differential evolution: A survey of the state-of-the-art.
Evolutionary Computation, IEEE Transactions on, PP (2010), pp. 1-28
[Das and Suganthan, 2011]
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.
[Deb, 2000]
K. Deb.
An efficient constraint handling method for genetic algorithms.
Computer Methods in Applied Mechanics and Engineering, 186 (2000), pp. 311-338
[Deb et al., 2002]
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan.
A fast and elitist multi-objective genetic algorithm: NSGA-II.
IEEE Transactions on Evolutionary Computation, 6 (2002), pp. 124-141
[Dixon and Pike, 2006]
R. Dixon, A. Pike.
ALSTOM benchmark challenge II on gasifier control.
Control Theory and Applications, IEE Proceedings -, 153 (2006 may), pp. 254-261
[Eiben and Schippers, 1998]
A. Eiben, C. Schippers.
On evolutionary exploration and exploitation.
Fundamenta Informaticae, 35 (1998), pp. 35-50
[Elgammal and Sharaf, 2012]
A. Elgammal, A. Sharaf.
Self-regulating particle swarm optimised controller for (photovoltaic-fuel cell) battery charging of hybrid electric vehicles.
Electrical Systems in Transportation, IET, 2 (2012 june), pp. 77-89
[Elsayed et al., 2011]
S. Elsayed, R. Sarker, D. Essam.
june 2011. GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: Evolutionary Computation (CEC).
IEEE Congress on., (2011), pp. 1034-1040
[Fazendeiro et al., 2007]
P. Fazendeiro, J. de Oliveira, W. Pedrycz.
A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller.
Biomedical Engineering, IEEE Transactions on, 54 (2007 sept.), pp. 1667-1678
[Fazzolar et al., 2013]
M. Fazzolar, R. Alcalá, Y. Nojima, H. Ishibuchi, F. Herrera.
A review of the application of multi-objective evolutionary fuzzy systems: Current status and further directions.
IEEE Transactions on Fuzzy Systems, 21 (2013 feb.), pp. 45-65
[Figueira et al., 2005]
Figueira, J., Greco, S., Ehrgott, M., 2005. Multiple criteria decision analysis: State of the art surveys. Springer international series.
[Fleming and Purshouse, 2002]
P. Fleming, R. Purshouse.
Evolutionary algorithms in control systems engineering: a survey.
Control Engineering Practice, 10 (2002), pp. 1223-1241
[Fonseca and Fleming, 1998a]
C. Fonseca, P. Fleming.
Multiobjective optimization and multiple constraint handling with evolutionary algorithms-I: A unified formulation.
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 28 (1998 jan), pp. 26-37
[Fonseca and Fleming, 1998b]
C. Fonseca, P. Fleming.
Multiobjective optimization and multiple constraint handling with evolutionary algorithms-II: Application example. Systems, Man and Cybernetics, Part A: Systems and Humans.
IEEE Transactions on, 28 (1998 jan), pp. 38-47
[Gaing, 2004]
Z.-L. Gaing.
A particle swarm optimization approach for optimum design of PID controller in AVR system.
Energy Conversion, IEEE Transactions on, 19 (2004 june), pp. 384-391
[Goldberg, 1989]
Goldberg, D., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
[Hajiloo et al., 2012]
A. Hajiloo, N. Nariman-zadeh, A. Moeini.
Pareto optimal robust design of fractional-order PID controllers for systems with probabilistic uncertainties.
Mechatronics, 22 (2012), pp. 788-801
[Hansen, 2006]
N. Hansen.
The CMA evolution strategy: a comparing review.
Towards a new evo-lutionary computation. Advances on estimation of distribution algorithms., pp. 75-102
[Harik et al., 1999]
G. Harik, F. Lobo, D. Goldberg.
The compact genetic algorithm. Evolutionary Computation.
IEEE Transactions on, 3 (1999 nov), pp. 287-297
[Herreros et al., 2002]
A. Herreros, E. Baeyens, J.R. Perán.
Design of PID-type controllers using multiobjective genetic algorithms.
ISA Transactions, 41 (2002), pp. 457-472
[Holland, 1975]
J.H. Holland.
Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence.
U. Michigan Press, (1975),
[Huang et al., 2008]
L. Huang, N. Wang, J.-H. Zhao.
Multiobjective optimization for controller design.
Acta Automatica Sinica, 34 (2008), pp. 472-477
[Hung et al., 2008]
M.-H. Hung, L.-S. Shu, S.-J. Ho, S.-F. Hwang, S.-Y. Ho.
A novel intelligent multiobjective simulated annealing algorithm for designing robust PID controllers. Systems, Man and Cybernetics, Part A: Systems and Humans.
IEEE Transactions on, 38 (2008 march), pp. 319-330
[Inselberg, 1985]
A. Inselberg.
The plane with parallel coordinates.
The Visual Computer, 1 (1985), pp. 69-91
[Iruthayarajan and Baskar, 2009]
M.W. Iruthayarajan, S. Baskar.
Evolutionary algorithms based design of multivariable PID controller.
Expert Systems with applications, 3 (2009), pp. 9159-9167
[Ishibuchi et al., 2008]
H. Ishibuchi, N. Tsukamoto, Y. Nojima.
Evolutionary many-objective optimization: A short review. In: Evolutionary Computation, 2008. CEC.
(IEEE World Congress on Computational Intelligence) IEEE Congress on., (2008 june), pp. 2419-2426
[Jiachuan et al., 2005]
W. Jiachuan, F. Zhun, J. Terpenny, E. Goodman.
Knowledge interaction with genetic programming in mechatronic systems design using bond graphs. Systems, Man, and Cybernetics, Part C: Applications and Reviews.
IEEE Transactions on, 35 (2005 may), pp. 172-182
[Juang et al., 2008]
J.-G. Juang, M.-T. Huang, W.-K. Liu.
PID control using presearched genetic algorithms for a mimo system Systems, Man, and Cybernetics, Part C: Applications and Reviews.
IEEE Transactions on, 38 (2008 sept.), pp. 716-727
[Kamath et al., 2009]
S. Kamath, V.I. George, S. Vidyasagar.
A comparative study of different types of controllers used for blood glucose regulation system.
The Canadian Journal of Chemical Engineering, 87 (2009), pp. 812-817
[Kaveh and Shtessel, 2008]
P. Kaveh, Y.B. Shtessel.
Blood glucose regulation using higher-order sliding mode control.
International Journal of Robust and Nonlinear Control, 18 (2008), pp. 557-569
[Kennedy and Eberhart, 1995]
J. Kennedy, R. Eberhart.
Particle swarm optimization. In: Neural Networks, 1995. Proceedings.
IEEE International Conference on., 4 (1995 nov/dec), pp. 1942-1948
[Kollat and Reed, 2007]
J.B. Kollat, P. Reed.
A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VI- DEO).
Environmental Modelling & Software, 22 (2007), pp. 1691-1704
[Konak et al., 2006]
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.
[Koza et al., 2003]
J. Koza, M. Keane, M. Streeter.
What's AI done for me lately genetic programming's human-competitive results.
Intelligent Systems, IEEE, 18 (2003 may-jun), pp. 25-31
[Koza and Poli, 2005]
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.
[Lamanna et al., 2009]
R. Lamanna, P. Vega, S. Revollar, H. Alvarez.
Diseño simultáneo de proceso y control de una torre sulfitadora de jugo de caña de azúcar.
Revista Iberoamericana de Automática e Informática Industrial, 6 (2009), pp. 32-43
[Lee and Chang, 2010]
C.-H. Lee, F.-K. Chang.
Fractional-order PID controller optimization via improved electromagnetism-like algorithm.
Expert Systems with Applications, 37 (2010), pp. 8871-8878
[Li et al., 2006]
Y. Li, K.H. Ang, G. Chong.
Pid control system analysis and design.
Control Systems, IEEE, 26 (2006 feb.), pp. 32-41
[Lin et al., 2011]
C.-M. Lin, M.-C. Li, A.-B. Ting, M.-H. Lin.
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 (2011), pp. 225-234
[Lotov and Miettinen, 2008]
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.
[Lozano et al., 2011]
M. Lozano, D. Molina, F. Herrera.
Soft Computing: Special Issue on scalability of evolutionary algorithms and other metaheuristics for largescale continuous optimization problems.
Springer-Verlag, (2011),
[Luyben, 1986]
W.L. Luyben.
Simple method for tuning SISO controllers in multivariable systems.
Industrial and Engineering Chemistry Process Design, 25 (1986), pp. 654-660
[Mallipeddi and Suganthan, 2009]
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.
[Marler and Arora, 2004]
R. Marler, J. Arora.
Survey of multi-objective optimization methods for engineering.
Structural and multidisciplinary optimization, 26 (2004), pp. 369-395
[Mattson and Messac, 2005]
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.
[Menhas et al., 2012a]
M.I. Menhas, M. Fei, L. Wang, L. Qian.
Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system.
Energy Conversion and Management, 54 (2012), pp. 67-80
[Menhas et al., 2012b]
M.I. Menhas, L. Wang, M. Fei, H. Pan.
Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design.
Expert Systems with Applications, 39 (2012), pp. 4390-4401
[Messac, 1996]
A. Messac.
Physical programming: effective optimization for computational design.
AIAA Journal, 34 (1996), pp. 149-158
[Mezura-Montes and Coello, 2011]
E. Mezura-Montes, C.A.C. Coello.
Constraint-handling in nature-inspired numerical optimization: Past, present and future.
Swarm and Evolutionary Computation, 1 (2011 December), pp. 173-194
[Mezura-Montes et al., 2008]
E. Mezura-Montes, M. Reyes-Sierra, C. Coello.
Multi-objective optimization using differential evolution: A survey of the state-of-the-art.
Advances in Differential Evolution, SCI143 (2008), pp. 173-196
[Miettinen, 1998]
K.M. Miettinen.
Nonlinear multiobjective optimization.
Kluwer Academic Publishers, (1998),
[Mininno et al., 2011]
E. Mininno, F. Neri, F. Cupertino, D. Naso.
Compact differential evolution Evolutionary Computation.
IEEE Transactions on, 15 (2011 feb.), pp. 32-54
[Oh et al., 2012]
S.-K. Oh, W.-D. Kim, W. Pedrycz.
Design of optimized cascade fuzzy controller based on differential evolution: Simulation studies and practical insights.
Engineering Applications of Artificial Intelligence, 25 (2012), pp. 520-532
[Pan et al., 2011]
I. Pan, S. Das, A. Gupta.
Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.
ISA Transactions, 50 (2011), pp. 28-36
[Podlubny, 1999]
I. Podlubny.
Fractional-order systems and PIλDμ-controllers. Automatic Control.
IEEE Transactions on, 44 (1999 jan.), pp. 208-214
[Rao and Tiwari, 2009]
J.S. Rao, R. Tiwari.
Design optimization of double-acting hybrid magnetic thrust bearings with control integration using multi-objective evolutionary algorithms.
Mechatronics, 19 (2009), pp. 945-964
[Reynoso-Meza et al., 2009]
G. Reynoso-Meza, X. Blasco, J. Sanchis.
Diseño multiobjetivo de controladores PID para el benchmark de control 2008-2009.
Revista Iberoamericana de Automática e Informática Industrial, 6 (2009), pp. 93-103
[Reynoso-Meza et al., 2011]
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.
[Reynoso-Meza et al., 2011a]
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.
[Reynoso-Meza et al., 2012a]
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.
[Reynoso-Meza et al., 2012b]
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.
[Reynoso-Meza et al., 2012c]
G. Reynoso-Meza, J. Sanchis, X. Blasco, J.M. Herrero.
Multiobjective evolutionary algortihms for multivariable PI controller tuning.
Expert Systems with Applications, 39 (2012), pp. 7895-7907
[Reynoso-Meza et al., 2013]
G. Reynoso-Meza, X. Blasco, J. Sanchis, J.M. Herrero.
Comparison of design concepts in multi-criteria decision-making using level diagrams.
Information Sciences, 221 (2013), pp. 124-141
[Romero-Pérez et al., 2012]
J.A. Romero-Pérez, O. Arrieta, F. Padula, G. Reynoso-Meza, S. Garcia-Nieto, P. Balaguer.
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, 9 (2012), pp. 182-193
[Roy et al., 2008]
R. Roy, S. Hinduja, R. Teti.
Recent advances in engineering design optimisation: Challenges and future trends.
CIRP Annals - Manufacturing Technology, 57 (2008), pp. 697-715
[Sanchis et al., 2010]
J. Sanchis, M.A. Martínez, X. Blasco, G. Reynoso-Meza.
Modelling preferences in multiobjective engineering design.
Engineering Applications of Artificial Intelligence, 23 (2010), pp. 1255-1264
[Santana-Quintero et al., 2010]
L. Santana-Quintero, A. Montaño, C. Coello.
A review of techniques for handling expensive functions in evolutionary multi-objective optimization.
Computational Intelligence in Expensive Optimization Problems Vol. 2 of Adaptation Learning and Optimization, pp. 29-59
[Saridakis and Dentsoras, 2008]
K. Saridakis, A. Dentsoras.
Soft computing in engineering design a review.
Advanced Engineering Informatics, 22 (2008), pp. 202-221
[Shi and Rasheed, 2010]
L. Shi, K. Rasheed.
A survey of fitness approximation methods applied in evolutionary algorithms.
Computational Intelligence in Expensive Optimization Problems Vol. 2 of Adaptation Learning and Optimization, pp. 3-28
[Sidhartha Panda, 2011]
Sidhartha Panda.
Multi-objective PID controller tuning for a facts-based damping stabilizer using non-dominated sorting genetic algorithm-II.
International Journal of Electrical Power and Energy Systems, 33 (2011), pp. 1296-1308
[Skogestad, 2003]
S. Skogestad.
Simple analytic rules for model reduction and PID controller tuning.
Journal of Process Control, 13 (2003), pp. 291-309
[Stewart and Samad, 2011]
G. Stewart, T. Samad.
Cross-application perspectives: Application and market requirements.
The Impact of Control Technology IEEE Control Systems Society, pp. 95-100
[Storn and Price, 1997]
R. Storn, K. Price.
Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces.
Journal of Global Optimization, 11 (1997), pp. 341-359
[Tan et al., 2004]
W. Tan, J. Liu, F. Fang, Y. Chen.
Tuning of PID controllers for boilerturbine units.
ISA Transactions, 43 (2004), pp. 571-583
[Tan et al., 2005]
W. Tan, F. Lu, A. Loh, K. Tan.
Modeling and control of a pilot pH plant using genetic algorithm.
Engineering Applications of Artificial Intelligence, 18 (2005), pp. 485-494
[Tavakoli et al., 2007]
S. Tavakoli, I. Griffin, P.J. Fleming.
Multi-objective optimization approach to the PI tuning problem.
In: Proceedings of the IEEE congress on evolutionary computation (CEC2007)., (2007 September), pp. 3165-3171
[Vilanova and Alfaro, 2011]
R. Vilanova, V.M. Alfaro.
Control pid robusto: una visión panorámica.
Revista Iberoamericana de Automática e Informática Industrial, 8 (2011), pp. 141-158
[Xue et al., 2010]
Y. Xue, D. Li, F. Gao.
Multi-objective optimization and selection for the PI control of ALSTOM gasifier problem.
Control Engineering Practice, 18 (2010), pp. 67-76
[Zamani et al., 2009]
M. Zamani, M. Karimi-Ghartemani, N. Sadati, M. Parniani.
Design of a fractional order PID controller for an AVR using particle swarm optimization.
Control Engineering Practice, 17 (2009), pp. 1380-1387
[Zhang et al., 2009]
J. Zhang, J. Zhuang, H. Du, S. Wang.
Self-organizing genetic algorithm based tuning of PID controllers.
Information Sciences, 179 (2009), pp. 1007-1018
[Zhang and Li, 2007]
Q. Zhang, H. Li.
MOEA/D: A multiobjective evolutionary algorithm based on decomposition. Evolutionary Computation.
IEEE Transactions on, 11 (2007 december), pp. 712-731
[Zhao et al., 2011]
S.-Z. Zhao, M.W. Iruthayarajan, S. Baskar, P. Suganthan.
Multiobjective robust PID controller tuning using two lbests multi-objective particle swarm optimization.
Information Sciences, 181 (2011), pp. 3323-3335
[Zhou et al., 2011]
A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P.N. Suganthan, Q. Zhang.
Multiobjective evolutionary algorithms: A survey of the state of the art.
Swarm and Evolutionary Computation, 1 (2011), pp. 32-49
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