Optimized control and neural observers with germinal center optimization: A review

Aarts, 1988 Aickelin, 2008, The danger theory and its application to artificial immune systems, CoRR, abs/0801.3549. Akbari, 2010, A novel bee swarm optimization algorithm for numerical function optimization, Communications in Nonlinear Science and Numerical Simulation, 15, 3142, 10.1016/j.cnsns.2009.11.003 Alanis, 2019 Alanis, 2015, Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller, Applied Mathematics and Computation, 271, 43, 10.1016/j.amc.2015.08.112 Alanis, 2016, Neural identifier for unknown discrete-time nonlinear delayed systems, Neural Computing and Applications, 27, 2453, 10.1007/s00521-015-2016-7 Åström, 1995, Vol. 2 Biyanto, 2016, Simultaneous optimization of tuning PID cascade control system using Duelist Algorithms, 601 Biyanto, 2016, Duelist algorithm: An algorithm in stochastic optimization method Boussaïd, 2013, A survey on optimization metaheuristics, Information Sciences, 237, 82, 10.1016/j.ins.2013.02.041 Censor, 1977, Pareto optimality in multiobjective problems, Applied Mathematics and Optimization, 4, 41, 10.1007/BF01442131 Chiang, 2015, Combine evolutionary optimization with model predictive control in real-time flood control of a river system, Water Resources Management, 29, 2527, 10.1007/s11269-015-0955-5 Chui, 2017 Das, 2009, Bacterial foraging optimization algorithm: Theoretical foundations, analysis, and applications, 23 De Castro, 2002, An artificial immune network for multimodal function optimization, Vol. 1, 699 De Castro, 2000, The clonal selection algorithm with engineering applications, Vol. 2000, 36 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017 Debnath, 2016, Novel PD-PID cascaded controller for automatic generation control of a multi-area interconnected power system optimized by grey wolf optimization (GWO), 1 Dorigo, 2011, Ant colony optimization, 36 Edwards, 1998 Eshelman, 1993, Real-coded genetic algorithms and interval-schemata, 2, 187 Feldkamp, 2003, Conditioned adaptive behavior from Kalman filter trained recurrent networks, 4, 3017 Glover, 1998, Tabu search, 2093 Greensmith, 2010, Artificial immune systems, 421 Hansen, 2010, Variable neighbourhood search: Methods and applications, Annals of Operations Research, 175, 367, 10.1007/s10479-009-0657-6 Haykin, 2001 Hermans, 2010, One step backpropagation through time for learning input mapping in reservoir computing applied to speech recognition, 521 Juang, 2015, Evolutionary fuzzy control and navigation for two wheeled robots cooperatively carrying an object in unknown environments, IEEE Transactions on Cybernetics, 45, 1731, 10.1109/TCYB.2014.2359966 Karaboga, 2008, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 8, 687, 10.1016/j.asoc.2007.05.007 Kennedy, 2011, Particle swarm optimization, 760 Kour, 2013, Ant colony optimization-a tool for online tuning of a PI controller for a three phase induction motor drive, IEEE Kramer, 2011, Derivative-free optimization, 61 Laware, 2018, Evolutionary optimization of sliding mode controller for level control system, ISA Transactions, 10.1016/j.isatra.2018.08.011 Lee, 1989, On-line pid controller tuning from a single, closed-loop test, AIChE Journal, 35, 329, 10.1002/aic.690350221 Leung, 2003, Dual extended Kalman filtering in recurrent neural networks, Neural Networks, 16, 223, 10.1016/S0893-6080(02)00230-7 Lin, 2017, Mobile robot wall-following control using a fuzzy cerebellar model articulation controller with group-based strategy bacterial foraging optimization, International Journal of Advanced Robotic Systems, 14, 10.1177/1729881417720872 Lopez-Franco, 2017, Visual servoing for an autonomous hexarotor using a neural network based PID controller, Sensors, 17, 1865, 10.3390/s17081865 Lopez-Franco, 2018, A soft computing approach for inverse kinematics of robot manipulators, Engineering Applications of Artificial Intelligence, 74, 104, 10.1016/j.engappai.2018.06.001 Loshchilov, 2013, Cma-es with restarts for solving CEC 2013 benchmark problems, 369 López-Franco, 2018, Inverse kinematics of mobile manipulators based on differential evolution, International Journal of Advanced Robotic Systems, 15, 10.1177/1729881417752738 Mahmoodabadi, 2015, Adaptive robust PID control subject to supervisory decoupled sliding mode control based upon genetic algorithm optimization, Transactions of the Institute of Measurement and Control, 37, 505, 10.1177/0142331214543295 Marler, 2004, Survey of multi-objective optimization methods for engineering, Structural and Multidisciplinary Optimization, 26, 369, 10.1007/s00158-003-0368-6 Marouane, 2018, Particle swarm optimization for tuning PI controller in FOC chain of induction motors, 1 Mirjalili, 2014, Grey wolf optimizer, Advances in Engineering Software, 69, 46, 10.1016/j.advengsoft.2013.12.007 Moness, 2015, Tuning a digital multivariable controller for a lab-scale helicopter system via simulated annealing and evolutionary algorithms, Transactions of the Institute of Measurement and Control, 37, 1254, 10.1177/0142331214560806 Nisi, 2018, Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry, International Journal of Machine Learning and Cybernetics O’Dwyer, 2006 Panoeiro, 2018, PI controller tuned by bee swarm for level control systems, 301 Pareek, 2014, Application of artificial bee colony optimization for optimal PID tuning, 1 Parejo, 2012, Metaheuristic optimization frameworks: A survey and benchmarking, Soft Computing, 16, 527, 10.1007/s00500-011-0754-8 Price, 2006 Puralachetty, 2016, Differential evolution and particle swarm optimization algorithms with two stage initialization for PID controller tuning in coupled tank liquid level system, 507 Reyes-Sierra, 2006, Multi-objective particle swarm optimizers: A survey of the state-of-the-art, International Journal of Computational Intelligence Research, 2, 287 Reynoso-Meza, 2013, Controller tuning by means of multi-objective optimization algorithms: A global tuning framework, IEEE Transactions on Control Systems Technology, 21, 445, 10.1109/TCST.2012.2185698 Rios, 2018, Germinal center optimization applied to recurrent high order neural network observer, IFAC-PapersOnLine, 51, 332, 10.1016/j.ifacol.2018.07.300 Rovithakis, 2012 Sanchez, 2008, Vol. 112 Selamat, 2013, Particle swarm optimization for multivariable PID controller tuning, 170 Shuai, 2014, Optimization of fuzzy control rules based on differential evolution algorithm, 2610 Simon, 2013 Slotine, 1986, Adaptive sliding controller synthesis for non-linear systems, International Journal of Control, 43, 1631, 10.1080/00207178608933564 Soubhia, 2018, Discrete optimization for actuator and sensor positioning for vibration control using genetic algorithms, Journal of Vibration and Control, 24, 4050, 10.1177/1077546317718968 Suresh, 2014, Design PID controller for TITO process based on least square optimization tuning method, 1 Tavanaei-Sereshki, 2018, Quantum genetic sliding mode controller design for depth control of an underwater vehicle, Measurement and Control, 51, 336, 10.1177/0020294018789199 Van Laarhoven, 1987, Simulated annealing, 7 Victora, 2012, Germinal centers, Annual Review of Immunology, 30, 429, 10.1146/annurev-immunol-020711-075032 Villaseñor, 2018, Germinal center optimization algorithm, International Journal of Computational Intelligence Systems, 12, 13, 10.2991/ijcis.2018.25905179 Villaseñor, 2017, Germinal center optimization applied to neural inverse optimal control for an all-terrain tracked robot, Applied Sciences, 8, 31, 10.3390/app8010031 Wang, 2018, Parameter optimization and speed control of switched reluctance motor based on evolutionary computation methods, Swarm and Evolutionary Computation, 39, 86, 10.1016/j.swevo.2017.09.004 Wolpert, 1997, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1, 67, 10.1109/4235.585893 Zhang, 2003, An improved quantum genetic algorithm and its application, 449