Genetic Algorithm Based Proportional- Integral Controller for Synchronous VAR Compensator

[1] Xiangnan, Z., Haibo, H., Huaguang, Z., Zhanshan, W. (2015). “A neural network based online learning
and control approach for Markov jump systems”. Neurocomputing, 149(3): 116-123.
[2] Minghui, J., Shuangtao, W., Jun, M., Yanjun, Sh. (2015). “Finite-time synchronization control of a
class of memristor-based recurrent neural networks”. Neural Networks, 63(2): 133-140
[3] Jin, W. (2015). “Integrated control of the cooling system and surface openings using the artificial
neural networks”. Applied Thermal Engineering, 78(3): 150-161.
[4] Yanhong, L., Qiuye, S., Huaguang, Z., Lili, C. (2015). “Adaptive critic design-based robust neural
network control for nonlinear distributed parameter systems with unknown dynamics”.
Neurocomputing, 148(3): 200-208.
[5] Arunkumar, A., Sakthivel, R., Mathiyalagan, R. (2015). “Robust reliable H∞ control for stochastic
neural networks with randomly occurring delays”. Neurocomputing, 149(3):1524-1534.
[6] Wang, X., Li, C.H., Huang, T., Chen, I. (2015). “Dual-stage impulsive control for synchronization of
memristive chaotic neural networks with discrete and continuously distributed delays”.
Neurocomputing, 149( 3 ): 621-628.
[7] Raimondi, A., Favela-Contreras, A., Beltrán-Carbajal, F. (2015). “Alejandro Piñón-Rubio, Jose Luis
de la Peña-Elizondo. Design of an adaptive predictive control strategy for crude oil atmospheric
distillation process”. Control Engineering Practice, 34(3):39-48.
[8] Nguyen, K., Dankowicz, H. (2015). “Adaptive control of underactuated robots with unmodeled
dynamics”. Robotics and Autonomous Systems, 64(3): 84-99.
[9] Naveed, A., Volker J. (2015). Adaptive time step control for higher order variational time
discretizations applied to convection–diffusion–reaction equations. Computer Methods in Applied
Mechanics and Engineering, 285(3):83-101.
[10] Qinglai W., Derong L. (2015). Neural-network-based adaptive optimal tracking control scheme for
discrete-time nonlinear systems with approximation errors. Neurocomputing, 149(4): 106-115.
[11] Ferrer, M., de Diego, M., Piñero, G., Gonzalez, A. (2015). Active noise control over adaptive
distributed networks. Signal Processing, 107(3):82-95.
[12] Farokhi, F., Johansson, K. (2015). “Adaptive control design under structured model information
limitation: A cost-biased maximum-likelihood approach”. Systems & Control Letters, 75(3): 8-13.
[13] Xudong Z., Xiaolong Z., Ben N., Liang L. (2015). “Adaptive tracking control for a class of uncertain
switched nonlinear systems”. Automatica, 52(30):185-191.
FATHALLAHNEZHAD, ESLAMI
76
[14] Wuxi Sh. (2015). “Observer-based fuzzy adaptive control for multi-input multi-output nonlinear
systems with a nonsymmetric control gain matrix and unknown control direction”. Fuzzy Sets and
Systems, 263(3): 1-26.
[15] Klug, M., Eugênio B., Castelan, C., Valter J.S. Leite, L., F.P. Silva. (2015). “Fuzzy dynamic output
feedback control through nonlinear Takagi–Sugeno models”. Fuzzy Sets and Systems, 263(15): 92-
111.
[16] Fang W., Zhi L., Guanyu L. (2015). “Fuzzy adaptive control of nonlinear uncertain plants with
unknown dead zone output”. Fuzzy Sets and Systems, 263(3): 27-48.
[17] Sergei S., Sergey, M., Baydali, A., Manenti, F. (2015). “Development of a tunable method for PID
controllers to achieve the desired phase margin”. Journal of Process Control, 25(10): 28-34
[18] Abdullah J.H. Al Gizi, M.W. Mustafa, Nasir A. Al-geelani, Malik A. Alsaedi. (2015). “Sugeno fuzzy
PID tuning, by genetic-neutral for AVR in electrical power generation”. Applied Soft Computing,
28(2): 226-236.
[19] Zhanybai, T., Zhusubaliyev, A. Medvedev., Margarida M. (2015). Bifurcation analysis of PIDcontrolled
neuromuscular blockade in closed-loop anesthesia. Journal of Process Control, 25(2): 152-
163
[20] Mernik, M., Shih-His, L., Karaboga, D. (2015). “On clarifying misconceptions when comparing
variants of the Artificial Bee Colony Algorithm by offering a new implementation”. Information
Sciences, 291(10): 115-127.
[21] Ozturk, C., Hancer, E., Karaboga, D. (2015). “A novel binary artificial bee colony algorithm based on
genetic operators”. Information Sciences, 297(10): 154-170
[22] Maeda, M., Tsuda. S.H. (2015). Reduction of artificial bee colony algorithm for global optimization.
Neurocomputing, 148(3): 70-74.
[23] Mansouri, P., Asady, B., Gupta, N. (2015). The Bisection–Artificial Bee Colony algorithm to solve
Fixed point problems. Applied Soft Computing, 26(2): 143-148
[24] Ozturk, C., Hancer, E., Karaboga, D. (2015). “Dynamic clustering with improved binary artificial bee
colony algorithm”. Applied Soft Computing, 28(3): 69-80.
[25] Zhang, C.H., Zheng, J., Zhou, J. (2015). “Two modified Artificial Bee Colony algorithms inspired by
Grenade Explosion Method”. Neurocomputing, 151(3): 1198-1207.
[26] Wei Yu., Baizhan Li., Hongyuan J., Ming Z., Wang, D. (2015). Application of multi-objective genetic
algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings,
88(1): 135-143
[27] Gopalakrishnan, H. Kosanovic, G. (2015). Operational planning of combined heat and power plants
through genetic algorithms for mixed 0–1 nonlinear programming. Computers & Operations
Research, 56(3): 51-67.
[28] Chiu-Hung C.H., Tung-Kuan L., Jyh-Horng C.H., Chung-Hung T. (2015). “Optimization of teacher
volunteer transferring problems using greedy genetic algorithms”. Expert Systems with Applications,
42(1): 668-678.
[29] Cheng, R., Yaochu J. (2015). “A social learning particle swarm optimization algorithm for scalable
optimization”. Information Sciences, 291(10): 43-60.
[30] Yangyang, L., Licheng J., Ronghua Sh., Stolkin. R. (2015). “Dynamic-context cooperative quantumbehaved
particle swarm optimization based on multilevel thresholding applied to medical image
segmentation”. Information Sciences, 294 (2): 408-422.
[31] Tanweer, M.R., Suresh, S., Sundararajan, N. (2015). “Self regulating particle swarm optimization
algorithm”. Information Sciences, 294 (3):182-202
[32] Yanmin L., Ben N., Yuanfeng L. (2015). Hybrid learning particle swarm optimizer with genetic
disturbance. Neurocomputing, 151 (2):1237-1247.
Genetic Algorithm Based Proportional- Integral Controller for Synchronous VAR Compensator
77
[33] Ahmadizar, F., Soltanian, K.H., AkhlaghianTab, F., Tsoulos. L. (2015). Artificial neural network
development by means of a novel combination of grammatical evolution and genetic algorithm.
Engineering Applications of Artificial Intelligence, 39(3): 1-13
[34] Quiroz-Castellanos, M., Cruz-Reyes, L., Torres-Jimenez, J. (2015). Claudia Gómez S., Héctor J.
Fraire Huacuja, Adriana C.F. Alvim. A grouping genetic algorithm with controlled gene transmission
for the bin packing problem. Computers & Operations Research, 55(2): 52-64.
[35] Anglada, E., Garmendia. I. (2015). Correlation of thermal mathematical models for thermal control of
space vehicles by means of genetic algorithms. Acta Astronautica, 108(2): 1-17.
[36] Hai-liang L., Xi-shan W., Lei L., Yun-zhu A., Xiao-ping L. (2015). A self-adaptive genetic algorithm
to estimate JA model parameters considering minor loops. Journal of Magnetism and Magnetic
Materials, 374 (21): 502-507.
[37] Han-Xiong, H., Jiong-Cheng, L., Cheng-Long, X. (2015). A proposed iteration optimization approach
integrating backpropagation neural network with genetic algorithm. Expert Systems with Applications,
42(4):146-155.
[38] Király, A., Abonyi, J. (2015). Redesign of the supply of mobile mechanics based on a novel genetic
optimization algorithm using Google Maps API. Engineering Applications of Artificial Intelligence,
38(3):122-130.
[39] Ozturk, N., Hancer, E., Karaboga, D. (2015). A novel binary artificial bee colony algorithm based on
genetic operators. Information Sciences, 297(3): 154-170.