Online learning of an interval type-2 TSK fuzzy logic controller for nonlinear systems
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The Lyapunov stability-based update overcomes limitations of gradient descent methods such as instability and local minima trapping.
“which overcomes the limitations of gradient descent (GD) methods such as instability and local minima trapping.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [25]
Cites
Mendel, J. M., & John, R. I. (2002) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.995115 · Cited by 2,620 · see citing works
Interval Type-2 Fuzzy Logic Systems Made Simple
Mendel, J. M., John, R. I., & Liu, F. (2006) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.879986 · Cited by 2,120 · see citing works
Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions
Mendel, J. M. (2001) · Cited by 1,909 · see citing works
Uncertain rule-based fuzzy logic systems: introduction and new directions
Gomide, F. A. C. (2002) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(02)00359-7 · Cited by 1,515 · see citing works
Proceedings of IEEE International Conference on Systems, Man and Cybernetics
(1994) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1994.399796 · Cited by 1,342 · see citing works
Online learning control by association and reinforcement
Si, J., & Wang, Y.-T. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914523 · Cited by 778 · see citing works
Castillo, O., Amador-Angulo, L., Castro, J. R., & García-Valdéz, M. (2016) · Information Sciences · doi:10.1016/j.ins.2016.03.026 · Cited by 437 · see citing works
Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization
Liang, H., Zhang, Y., Huang, T., et al. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2893645 · Cited by 353 · see citing works
A review on the design and optimization of interval type-2 fuzzy controllers
Castillo, O., & Melín, P. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.12.010 · Cited by 334 · see citing works
On the Stability of Interval Type-2 TSK Fuzzy Logic Control Systems
Biglarbegian, M., Melek, W., & Mendel, J. M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2029986 · Cited by 300 · see citing works
Sanchez, M. A., Castillo, O., & Castro, J. R. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.03.024 · Cited by 275 · see citing works
Zhang, Z., Liang, H., Wu, C., et al. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2891236 · Cited by 245 · see citing works
A generalized type-2 fuzzy granular approach with applications to aerospace
Castillo, O., Cervantes, L., Soria, J., Sanchez, M. A., & Castro, J. R. (2016) · Information Sciences · doi:10.1016/j.ins.2016.03.001 · Cited by 222 · see citing works
He, H., Ni, Z., & Fu, J. Y. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.05.031 · Cited by 221 · see citing works
Optimal Guaranteed Cost Sliding-Mode Control of Interval Type-2 Fuzzy Time-Delay Systems
Li, H., Wang, J., Wu, L., et al. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2017.2648855 · Cited by 214 · see citing works
Adaptive Fuzzy Control of Nonlinear Systems in Pure Feedback Form Based on Input-to-State Stability
Zhang, T., Wen, H., & Zhu, Q. (2009) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2009.2036906 · Cited by 198 · see citing works
Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control
Cervantes, L., & Castillo, O. (2015) · Information Sciences · doi:10.1016/j.ins.2015.06.047 · Cited by 190 · see citing works
Dierks, T., Thumati, B. T., & Jagannathan, S. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.014 · Cited by 186 · see citing works
Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control
Juang, C., & Hsu, C.-H. (2009) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2017557 · Cited by 185 · see citing works
Comparative analysis of noise robustness of type 2 fuzzy logic controllers
Ontiveros-Robles, E., Melín, P., & Castillo, O. (2018) · Kybernetika · doi:10.14736/kyb-2018-1-0175 · Cited by 178 · see citing works
Castillo, O., & Amador-Angulo, L. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.032 · Cited by 146 · see citing works
Adaptive Dynamic Programming for a Class of Complex-Valued Nonlinear Systems
Song, R., Xiao, W., Zhang, H., et al. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2306201 · Cited by 145 · see citing works
Sanchez, M. A., Castillo, O., & Castro, J. R. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.05.036 · Cited by 144 · see citing works
Introduction to Type-2 Fuzzy Logic Control: Theory and Applications
Mendel, J. M., Hagras, H., Tan, W., et al. (2014) · International Linear Collider · Cited by 137 · see citing works
Interval type-2 fuzzy PID controller for uncertain nonlinear inverted pendulum system
El-Bardini, M. A., & El-Nagar, A. M. (2014) · ISA Transactions · doi:10.1016/j.isatra.2014.02.007 · Cited by 136 · see citing works
Self-citationAdaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives With a Modified Reference Frame
Chaoui, H., Khayamy, M., & Aljarboua, A. A. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2650858 · Cited by 128 · see citing works
Robust Non-Fragile Fuzzy Control of Uncertain DC Microgrids Feeding Constant Power Loads
Vafamand, N., Khooban, M. H., Dragičević, T., et al. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2896019 · Cited by 112 · see citing works
Big Bang–Big Crunch optimization based interval type-2 fuzzy PID cascade controller design strategy
Kumbasar, T., & Hagras, H. (2014) · Information Sciences · doi:10.1016/j.ins.2014.06.005 · Cited by 109 · see citing works
Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems
Lin, T.-C., Liu, H.-L., & Kuo, M.-J. (2008) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2008.10.024 · Cited by 103 · see citing works
T–S fuzzy model predictive speed control of electrical vehicles
Khooban, M. H., Vafamand, N., & Niknam, T. (2016) · ISA Transactions · doi:10.1016/j.isatra.2016.04.019 · Cited by 89 · see citing works
Model‐predictive control based on Takagi‐Sugeno fuzzy model for electrical vehicles delayed model
Khooban, M. H., Vafamand, N., Niknam, T., et al. (2016) · IET Electric Power Applications · doi:10.1049/iet-epa.2016.0508 · Cited by 85 · see citing works
A simple design method for interval type-2 fuzzy pid controllers
Kumbasar, T. (2013) · Soft Computing · doi:10.1007/s00500-013-1144-1 · Cited by 80 · see citing works
Khooban, M. H., Niknam, T., Blaabjerg, F., et al. (2016) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2016.0091 · Cited by 74 · see citing works
Intelligent control for nonlinear inverted pendulum based on interval type-2 fuzzy PD controller
El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2013) · Alexandria Engineering Journal · doi:10.1016/j.aej.2013.11.006 · Cited by 66 · see citing works
Self-citationKhooban, M. H., Niknam, T., & Shasadeghi, M. (2016) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2015.0033 · Cited by 61 · see citing works
Melin, P., Ontiveros-Robles, E., González, C. I., Castro, J. R., & Castillo, O. (2018) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-018-3503-4 · Cited by 58 · see citing works
Vafamand, N., Arefi, M. M., Khooban, M. H., Dragičević, T., & Blaabjerg, F. (2018) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2018.2884346 · Cited by 50 · see citing works
El-Nagar, A. M., & El-Bardini, M. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.08.040 · Cited by 48 · see citing works
Self-citationLv, Y., Na, J., & Ren, X. (2017) · International Journal of Control · doi:10.1080/00207179.2017.1381763 · Cited by 48 · see citing works
Khooban, M. H., Shasadeghi, M., Niknam, T., et al. (2016) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2016.0277 · Cited by 47 · see citing works
Optimal design of interval type 2 fuzzy controllers based on a simple tuning algorithm
Ríos, J. C. C., Gomez-Ramirez, E., Ortiz-de-la-Vega, H. A., et al. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.06.015 · Cited by 39 · see citing works
Khooban, M. H., Gheisarnejad, M., Vafamand, N., et al. (2019) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2019.2904415 · Cited by 39 · see citing works
Bernal, E., Castillo, O., Soria, J., et al. (2019) · Axioms · doi:10.3390/axioms8010026 · Cited by 35 · see citing works
Intelligent robust PI adaptive control strategy for speed control of EV(s)
Khooban, M. H., Almasi, O. N., Niknam, T., & Shasadeghi, M. (2016) · IET Science Measurement & Technology · doi:10.1049/iet-smt.2015.0190 · Cited by 34 · see citing works
Adaptive T–S fuzzy controller using reinforcement learning based on Lyapunov stability
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2018) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2018.06.031 · Cited by 32 · see citing works
Self-citationParallel realization for self-tuning interval type-2 fuzzy controller
El-Nagar, A. M., & El-Bardini, M. A. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.02.011 · Cited by 29 · see citing works
Self-citationShen, H., & Guo, C. G. (2016) · International Conference on Information Science and Technology · doi:10.1109/icist.2016.7483431 · Cited by 27 · see citing works
An adaptive neuro-control approach for multi-machine power systems
Ni, Z., Tang, Y., Sui, X., et al. (2015) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2015.08.012 · Cited by 25 · see citing works
El-Nagar, A. M., & El-Bardini, M. A. (2015) · Applied Mathematical Modelling · doi:10.1016/j.apm.2015.09.005 · Cited by 25 · see citing works
Self-citationPractical Realization for the Interval Type-2 Fuzzy PD+I Controller Using a Low-Cost Microcontroller
El-Nagar, A. M., & El-Bardini, M. A. (2014) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-014-1153-0 · Cited by 24 · see citing works
Self-citationA practical approach for design of PD and PI like Interval Type-2 TSK fuzzy controllers
Biglarbegian, M., Melek, W., & Mendel, J. M. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5345937 · Cited by 23 · see citing works
Huang, X., Naghdy, F., Du, H., et al. (2015) · IEEE Conference on Control Applications (CCA) · doi:10.1109/cca.2015.7320733 · Cited by 22 · see citing works
Nonlinear Controllers for a Light-Weighted All-Electric Vehicle Using Chebyshev Neural Network
Sharma, V., & Purwar, S. (2014) · International Journal of Vehicular Technology · doi:10.1155/2014/867209 · Cited by 22 · see citing works
Khooban, M. H. (2018) · Transactions of the Institute of Measurement and Control · doi:10.1177/0142331218764784 · Cited by 22 · see citing works
Embedded intelligent adaptive PI controller for an electromechanical system
El-Nagar, A. M. (2016) · ISA Transactions · doi:10.1016/j.isatra.2016.06.006 · Cited by 21 · see citing works
Self-citationEmbedded Adaptive Fuzzy Controller Based on Reinforcement Learning for DC Motor with Flexible Shaft
Aziz Khater, A., El-Bardini, M., & El-Rabaie, N. M. (2015) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-015-1752-4 · Cited by 20 · see citing works
Self-citationChen, C.-H. (2013) · Applied Mathematics and Computation · doi:10.1016/j.amc.2013.02.026 · Cited by 14 · see citing works
Research on Type-2 TSK Fuzzy Logic Systems
Zheng, G., Wang, J., & Jiang, L. (2009) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-03664-4_54 · Cited by 13 · see citing works
Nagayoshi, M., Murao, H., & Tamaki, H. (2012) · Artificial Life and Robotics · doi:10.1007/s10015-012-0041-5 · Cited by 6 · see citing works
Adaptive Fuzzy Sliding Mode Optimum Control, Using the Fuzzy Control in Flat Robot
Ghadiri, J. S. H. (2016) · IOSR Journal of Electrical and Electronics Engineering · doi:10.9790/1676-110401109118 · Cited by 3 · see citing works
Online critic-identifier-actor algorithm for optimal control of nonlinear systems
Lin, H., Wei, Q., & Liu, D. (2015) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2015.7388204 · Cited by 1 · see citing works
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Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system
Khater, A. A., Fekry, M., El-Bardini, M., & El-Nagar, A. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10741-x · Cited by 17 · see citing works
“To overcome the limitations of the GD-based update equations like instability or stocking in local minima [25, 26], the Lyapunov stability method is used to derive the adaptation law of weights.”
Self-citation
- Authors
- Khater, A. A., El-Nagar, A. M., El-Bardini, M., & El-Rabaie, N. M.
- Affiliations
- Menoufia University (EG)
- Corresponding author
- El-Nagar, Ahmad M.
- ORCID
- El-Nagar, Ahmad M.
- Venue
- Journal of the Franklin Institute
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 356
- Issue
- 16
- Pages
- 9254-9285
- ISSN
- 0016-0032 (ISSN-L)
- OpenAlex
- W2975286643
- Cited by
- 23
- References
- 61
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 3 · 2020: 3
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Franklin Institute (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Keywords
- interval type-2 fuzzy logic controller, Takagi-Sugeno-Kang fuzzy systems, reinforcement learning, online learning, nonlinear systems, guaranteed cost control, Lyapunov stability, critic neural network, learning rate selection, robust control, uncertain systems, external disturbances
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