Embedded Adaptive Fuzzy Controller Based on Reinforcement Learning for DC Motor with Flexible Shaft

Introduction to Reinforcement Learning

Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works

Modern Control Engineering

Ogata, K., & Brewer, J. W. (1971) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.3426465 · Cited by 6,370 · see citing works

Reinforcement learning and adaptive dynamic programming for feedback control

Lewis, F. L., & Vrabie, D. (2009) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2009.933854 · Cited by 1,569 · see citing works

Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers

Lewis, F. L., Vrabie, D., & Vamvoudakis, K. G. (2012) · IEEE Control Systems · doi:10.1109/mcs.2012.2214134 · Cited by 1,107 · see citing works

Reinforcement learning is direct adaptive optimal control

Sutton, R. S., Barto, A. G., & Williams, R. J. (1992) · IEEE Control Systems · doi:10.1109/37.126844 · Cited by 528 · see citing works

Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation

Na, J., Chen, Q., Ren, X., & Guo, Y. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2240635 · Cited by 524 · see citing works

Advanced Control Engineering

(2001) · Elsevier eBooks · doi:10.1016/b978-0-7506-5100-4.x5000-1 · Cited by 350 · see citing works

A PID type fuzzy controller with self-tuning scaling factors

Woo, Z.-W., Chung, H., & Lin, J.-J. (2000) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(98)00159-6 · Cited by 340 · see citing works

Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator

Sharma, R., Rana, K. P. S., & Kumar, V. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.030 · Cited by 245 · see citing works

Parallel structure and tuning of a fuzzy PID controller

Xu, J., Hang, C.-C., & Liu, C. (2000) · Automatica · doi:10.1016/s0005-1098(99)00192-2 · Cited by 192 · see citing works

Reinforcement learning algorithms with function approximation: Recent advances and applications

Xu, X., Zuo, L., & Huang, Z. (2013) · Information Sciences · doi:10.1016/j.ins.2013.08.037 · Cited by 181 · see citing works

Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

Kara, T., & Eker, İ. (2003) · Energy Conversion and Management · doi:10.1016/j.enconman.2003.08.005 · Cited by 165 · see citing works

A PSO-based adaptive fuzzy PID-controllers

Chiou, J., Tsai, S., & Liu, M. (2012) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2012.04.001 · Cited by 162 · see citing works

Online adaptive approximate optimal tracking control with simplified dual approximation structure for continuous-time unknown nonlinear systems

Na, J., & Herrmann, G. (2014) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2014.7004668 · Cited by 140 · 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

Practical design of nonlinear fuzzy controllers with stability analysis for regulating processes with unknown mathematical models

Ying, H. (1994) · Automatica · doi:10.1016/0005-1098(94)90213-5 · Cited by 125 · see citing works

Analytical structure and stability analysis of a fuzzy PID controller

Mohan, B. M., & Sinha, A. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.06.003 · Cited by 109 · see citing works

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics

Peng, J., & Dubay, R. (2011) · ISA Transactions · doi:10.1016/j.isatra.2011.06.005 · Cited by 108 · see citing works

Tuning fuzzy PD and PI controllers using reinforcement learning

Boubertakh, H., Tadjine, M., Glorennec, P.-Y., et al. (2010) · ISA Transactions · doi:10.1016/j.isatra.2010.05.005 · Cited by 103 · see citing works

Analytical structures and analysis of the simplest fuzzy PI controllers

Patel, A. V., & Mohan, B. M. (2002) · Automatica · doi:10.1016/s0005-1098(01)00297-7 · Cited by 90 · see citing works

Fuzzy logic-based decision-making for fault diagnosis in a DC motor

Miguel, L. J., & Blázquez, L. F. (2005) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2004.11.002 · Cited by 85 · see citing works

A parallel neuro-controller for DC motors containing nonlinear friction

Jang, J. O., & Jeon, G. J. (2000) · Neurocomputing · doi:10.1016/s0925-2312(99)00128-9 · Cited by 82 · see citing works

Friction Effect Analysis of a DC Motor

Virgala, I., & Frankovský, M. K. P. (2013) · American journal of mechanical engineering · doi:10.12691/ajme-1-1-1 · Cited by 72 · 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

Adaptive control of a nonlinear dc motor drive using recurrent neural networks

Nouri, K., Dhaouadi, R., & Braïek, N. B. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.03.002 · Cited by 64 · see citing works

Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

Noel, M. M., & Pandian, B. J. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.06.037 · Cited by 64 · see citing works

Model-free learning control of neutralization processes using reinforcement learning

Syafiie, S., Tadeo, F., & Martinez, E. C. (2007) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.10.009 · Cited by 60 · see citing works

Reinforcement distribution in fuzzy Q-learning

Bonarini, A., Lazaric, A., Montrone, F., et al. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2008.11.026 · Cited by 54 · see citing works

Analytical structures and analysis of the simplest fuzzy PD controllers

Mohan, B. M., & Patel, A. V. (2002) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.990881 · Cited by 54 · see citing works

Practical Implementation for the interval type-2 fuzzy PID controller using a low cost microcontroller

El-Nagar, A. M., & El-Bardini, M. A. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2013.12.005 · Cited by 50 · see citing works

Derivation and stability analysis of the analytical structures of the interval type-2 fuzzy PID controller

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

Application of hybrid fuzzy logic proportional plus conventional integral-derivative controller to combustion control of stoker-fired boilers

Li, W., & Chang, X. (2000) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(97)00412-0 · Cited by 48 · see citing works

Parallel fuzzy P+fuzzy I+fuzzy D controller: Design and performance evaluation

Kumar, V., & Mittal, A. P. (2010) · Machine Intelligence Research · doi:10.1007/s11633-010-0528-2 · Cited by 46 · see citing works

Mathematical models of the simplest fuzzy PI/PD controllers with skewed input and output fuzzy sets

Mohan, B. M., & Sinha, A. (2008) · ISA Transactions · doi:10.1016/j.isatra.2007.03.008 · Cited by 36 · see citing works

Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control

Ramadan, E. A., El-Bardini, M. A., & Fkirin, M. A. (2014) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2014.04.002 · Cited by 30 · see citing works

Parallel Fuzzy P＋Fuzzy I＋Fuzzy D Controller： Design and Performance Evaluation

Vineet, Kumar, & Mittal (2010) · 国际自动化与计算杂志 · Cited by 29 · see citing works

Reinforcement learning of ball screw feed drive controllers

Fernandez‐Gauna, B., Ansoategui, I., Etxeberria‐Agiriano, I., et al. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.01.015 · Cited by 25 · see citing works

Reinforcement learning combined with a fuzzy adaptive learning control network (FALCON-R) for pattern classification

Quah, K. H., Quek, C. H., & Leedham, G. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.08.011 · Cited by 20 · see citing works

Embedded system based on a real time fuzzy motor speed controller

Ramadan, E. A. E.-H. M., El-Bardini, M. A., El-Rabaie, N. M., et al. (2013) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2013.10.001 · Cited by 18 · see citing works

Neuro-fuzzy networks saturation compensation of DC motor systems

Jang, J. O. (2009) · Mechatronics · doi:10.1016/j.mechatronics.2008.12.005 · Cited by 16 · see citing works

Fuzzy Q-Learning with an adaptive representation

Waldock, A., & Carse, B. (2008) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2008.4630449 · Cited by 14 · see citing works

Optimization of a Fuzzy PI Controller using Reinforcement Learning

Boubertakh, H., & Glorennec, P. Y. (2006) · doi:10.1109/ictta.2006.1684633 · Cited by 8 · see citing works

Deep learning controller for nonlinear system based on Lyapunov stability criterion

Zaki, A. M., El-Nagar, A. M., El-Bardini, M., & Soliman, F. A. S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05077-1 · Cited by 29 · see citing works

Online learning of an interval type-2 TSK fuzzy logic controller for nonlinear systems

Khater, A. A., El-Nagar, A. M., El-Bardini, M., & El-Rabaie, N. M. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2019.08.031 · Cited by 23 · see citing works

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

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Jiang, Z., & Song, G. (2022) · International Conference on Computing, Robotics and System Sciences (ICRSS) · doi:10.1109/icrss57469.2022.00031 · Cited by 17 · see citing works

Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment

Chen, D., Zhu, Y., Liu, M., et al. (2022) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3533271.3561761 · Cited by 2 · see citing works

Classification without gradients: multi-agent reinforcement learning approach to optimization (Conference Presentation)

Morcos, A., Man, H., Maguire, B., et al. (2023) · doi:10.1117/12.2664025

Performance of distributed multi-agent multi-state reinforcement spectrum management using different exploration schemes

Ko, A. H. R., Sabourin, R., & Gagnon, F. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.01.035 · Cited by 9 · see citing works

Finding shortcuts from episode in multi-agent reinforcement learning

Jin, Z., Liu, W., & Jin, J. G. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212219 · Cited by 2 · see citing works