Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion

Identification and control of dynamical systems using neural networks

Narendra, K. S., & Parthasarathy, K. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80202 · Cited by 8,158

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks

Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369

System Identification

Lennart Ljung, A. (1998) · Wiley Encyclopedia of Electrical and Electronics Engineering · doi:10.1002/047134608x.w1046 · Cited by 4,118

Neural networks for control systems—A survey

Hunt, K. J., Sbarbaro, D. G., Żbikowski, R., & Gawthrop, P. J. (1992) · Automatica · doi:10.1016/0005-1098(92)90053-i · Cited by 1,874

Modelling and Control of Robot Manipulators

Sciavicco, L., & Siciliano, B. (2000) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0449-0 · Cited by 1,710

Discrete-Time Control Systems

(2005) · Springer eBooks · doi:10.1007/0-387-28154-1_8 · Cited by 1,550

Approximation of dynamical systems by continuous time recurrent neural networks

Funahashi, K.-I., & Nakamura, Y. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80125-x · Cited by 980

Diagonal recurrent neural networks for dynamic systems control

Ku, C.-C., & Lee, K. Y. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363441 · Cited by 726

Chemical Process Control: An Introduction to Theory and Practice

Stephanopoulos, G. (1983) · Prentice Hall eBooks · Cited by 644

Gradient methods for the optimization of dynamical systems containing neural networks

Narendra, K. S., & Parthasarathy, K. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80336 · Cited by 644

A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control

Wang, T., Gao, H., & Qiu, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2411671 · Cited by 636

Identification and control of dynamic systems using recurrent fuzzy neural networks

Lee, C., & Teng, C. (2000) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.868943 · Cited by 603

Global asymptotic stability of a general class of recurrent neural networks with time-varying delays

Cao, J., & Wang, J. (2003) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2002.807494 · Cited by 555

Robot manipulators : mathematics, programming, and control : the computer control of robot manipulators

Paul, R. P. (1981) · CERN Document Server (European Organization for Nuclear Research) · Cited by 294

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance

Huang, Y., Na, J., Wu, X. F., Liu, X., & Guo, Y. (2014) · ISA Transactions · doi:10.1016/j.isatra.2014.05.025 · Cited by 244

A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

Chow, T. W. S., & Fang, Y. (1998) · IEEE Transactions on Industrial Electronics · doi:10.1109/41.661316 · Cited by 233

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network

Han, H., Zhang, L., Hou, Y., & Qiao, J. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2465174 · Cited by 195

Neural networks for control systems

Antsaklis, P. J. (1990) · IEEE Transactions on Neural Networks · doi:10.1109/72.80237 · Cited by 178

Fuzzy Control and Identification

Lilly, J. H. (2010) · Wiley eBooks · doi:10.1002/9780470874240 · Cited by 172

Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities

Mathiyalagan, K., Park, J. H., & Sakthivel, R. (2015) · Applied Mathematics and Computation · doi:10.1016/j.amc.2015.03.022 · Cited by 168

Nonlinear model identification and adaptive model predictive control using neural networks

Akpan, V. A., & Hassapis, G. D. (2011) · ISA Transactions · doi:10.1016/j.isatra.2010.12.007 · Cited by 126

Stability analysis of discrete-time recurrent neural networks

Barabanov, N., & Prokhorov, D. V. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991416 · Cited by 125

Discrete time recurrent neural network architectures: A unifying review

Tsoi, A. C., & Back, A. D. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)00161-6 · Cited by 123

Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks

Liang, J. H., NIKIFORUK, P. N., & Gupta, Μ. Μ. (1995) · IEEE Transactions on Automatic Control · doi:10.1109/9.400480 · Cited by 114

Improved nuclear reactor temperature control using diagonal recurrent neural networks

Ku, C.-C., Lee, K. Y., & Edwards, R. M. (1992) · IEEE Transactions on Nuclear Science · doi:10.1109/23.211440 · Cited by 112

Fuzzy Control and Identification

Lilly, J. H. (2010) · Cited by 102

Neural networks and dynamical systems

Narendra, K. S., & Parthasarathy, K. (1992) · International Journal of Approximate Reasoning · doi:10.1016/0888-613x(92)90014-q · Cited by 100

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems

Han, S.-I., & Lee, J.-M. (2013) · ISA Transactions · doi:10.1016/j.isatra.2013.08.012 · Cited by 98

A novel auto-tuning PID control mechanism for nonlinear systems

Çetin, M., & İplikçi, S. (2015) · ISA Transactions · doi:10.1016/j.isatra.2015.05.017 · Cited by 83

Neural-network-based adaptive optimal tracking control scheme for discrete-time nonlinear systems with approximation errors

Wei, Q., & Liu, D. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.069 · Cited by 79

New fuzzy wavelet neural networks for system identification and control

Srivastava, S., Singh, M., Hanmandlu, M., & JHA, A. N. (2004) · Applied Soft Computing · doi:10.1016/j.asoc.2004.10.001 · Cited by 74

Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays

Liu, P., Zeng, Z., & Wang, J. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.03.010 · Cited by 69

Modeling of an intelligent pressure sensor using functional link artificial neural networks

Patra, J. C., & van den Bos, A. (2000) · ISA Transactions · doi:10.1016/s0019-0578(99)00035-x · Cited by 67

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Sharma, R., Kumar, V., Gaur, P., & Mittal, A. (2016) · ISA Transactions · doi:10.1016/j.isatra.2016.01.016 · Cited by 50

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

Savran, A., Taşaltı́n, R., & Becerikli, Y. (2006) · ISA Transactions · doi:10.1016/s0019-0578(07)60192-x · Cited by 47

Neural networks for process control and optimization: Two industrial applications

Bloch, G., & Denœux, T. (2003) · ISA Transactions · doi:10.1016/s0019-0578(07)60112-8 · Cited by 46

Fault detection and classification in chemical processes based on neural networks with feature extraction

Zhou, Y., Hahn, J., & Mannan, M. S. (2003) · ISA Transactions · doi:10.1016/s0019-0578(07)60013-5 · Cited by 41

Dynamic model of a one-link robot manipulator with both structural and joint flexibility

Yang, G., & Donath, M. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1988.12097 · Cited by 38

Recurrent neural network for solving model predictive control problem in application of four-tank benchmark

Xu, J., Li, C., He, X., & Huang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.020 · Cited by 37

Robust nonlinear adaptive observer design using dynamic recurrent neural networks

Zhu, R., Chai, T., & Shao, C. (1997) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.1997.609702 · Cited by 30

Identification and Control of a Nonlinear System using Neural Networks by Extracting the System Dynamics

Singh, M., Srivastava, S., Gupta, J. R. P., & Handmandlu, M. (2007) · IETE Journal of Research · doi:10.1080/03772063.2007.10876120 · Cited by 29

Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays

Zhou, L., & Zhang, Y. (2015) · ISA Transactions · doi:10.1016/j.isatra.2015.11.008 · Cited by 28

Short-term load forecasting using diagonal recurrent neural network

Lee, K. Y., Choi, T. I., Ku, C.-C., & Park, J.-S. (2002) · International Forum on Applications of Neural Networks to Power Systems · doi:10.1109/ann.1993.264286 · Cited by 27

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

Wang, Z., Zhou, T., Mao, Y., & Chen, Q. J. (2012) · International Journal of Systems Science · doi:10.1080/00207721.2012.724116 · Cited by 27

A simple nonlinear controller with diagonal recurrent neural network

Gao, F., Wang, F., & Li, M. (2000) · Chemical Engineering Science · doi:10.1016/s0009-2509(99)00399-1 · Cited by 27

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

Li, J., Li, S., Chen, X., & Yang, J. (2014) · ISA Transactions · doi:10.1016/j.isatra.2014.05.003 · Cited by 25

Diagonal recurrent neural network based control using adaptive learning rates

Ku, C.-C., & Lee, K. Y. (2005) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1992.371204 · Cited by 23

Modeling and control for pneumatic manipulator based on dynamic neural network

Wang, X., & Peng, G. (2004) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2003.1244215 · Cited by 19

Modeling of non‐linear systems by FWNNs and their intelligent control

Srivastava, S., Singh, M., Hanmandlu, M., & JHA, A. N. (2005) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.863 · Cited by 13

Design and analysis of a novel chaotic diagonal recurrent neural network

Wang, L., Meng, Z., Sun, Y., Guo, L., & Zhou, M. (2015) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2015.01.021 · Cited by 12

Adaptive Friction Compensation of Robot Manipulator

Sanxiu, W., & Shengtao, J. (2011) · Lecture notes in electrical engineering · doi:10.1007/978-3-642-21697-8_17 · Cited by 11

Design of a decoupled AP1000 reactor core control system using digital proportional–integral–derivative (PID) control based on a quasi-diagonal recurrent neural network (QDRNN)

Wei, X., Wang, P., & Zhao, F. (2016) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2016.04.022 · Cited by 10

A neural-network-based controller for a single-link flexible manipulator: Comparison of FFNN and DRNN controllers

Amiri, M., Menhaj, M. B., & Yazdanpanh, M. J. (2008) · IEEE International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4634024 · Cited by 8

Practical considerations for Kalman filter training of recurrent neural networks

Puskorius, G. V., & Feldkamp, L. A. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1993.298726 · Cited by 6

Research of a Diagonal Recurrent Neural Network and Artificial Neural Networks

Yang, W., Zhang, D., & Fu, Y. (2016) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c.2016.103 · Cited by 6

A learning automaton approach to trajectory learning and control system design using dynamic recurrent neural networks

Condarcure, T. A., & Sundareshan, M. K. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1994.374646 · Cited by 5

PMSM servo system based on dynamic Recurrent Neural Networks PID controller

Guo, B., Hu, L., & Bai, Y. (2012) · doi:10.1109/ipemc.2012.6259234 · Cited by 4

Variable flow heating control system based on neural network decoupling

Liu, C., Li, K., Song, Z., Song, X., & Wang, H. (2007) · IEEE International Conference on Grey systems and Intelligent Services · doi:10.1109/gsis.2007.4443396 · Cited by 4

The research of loading model of eddy current dynamometer based on DRNN with double hidden layers

Fan, S., Junzheng, W., Shen, W. Q., & Guangrong, C. (2015) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2015.7162339 · Cited by 3

Sonar array servo system based on diagonal recurrent neural network

Liu, S., Du, Y., Li, W., & Zheng, X.-L. (2006) · International Conference on Mechatronics and Automation · doi:10.1109/icma.2005.1626854 · Cited by 3

Diagonal Recurrent Neural Network-Based Electro-Hydraulic Servo System Control

Ye, L., Ma, J., He, D. C., & Gao, X. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.336-338.581 · Cited by 2

Fractional-Order Terminal Sliding-Mode Control Using Self-Evolving Recurrent Chebyshev Fuzzy Neural Network for MEMS Gyroscope

Wang, Z., & Fei, J. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3094717 · Cited by 93

Recurrent Neural Network Based Sliding Mode Control for an Uncertain Tilting Quadrotor UAV

Xiong, J., Wang, X., & Li, C. (2025) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.70108 · Cited by 67

Event-Triggered Reinforcement Learning-Based Adaptive Tracking Control for Completely Unknown Continuous-Time Nonlinear Systems

Guo, X., Yan, W., & Cui, R. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2903108 · Cited by 63

Comparative study of neural networks for dynamic nonlinear systems identification

Kumar, R., Srivastava, S., Gupta, J. R. P., & Mohindru, A. (2018) · Soft Computing · doi:10.1007/s00500-018-3235-5 · Cited by 61

Predictive control of slurry pressure balance in shield tunneling using diagonal recurrent neural network and evolved particle swarm optimization

Li, X., & Gong, G. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.102928 · Cited by 60

Diagonal recurrent neural networks for control of dynamic systems

Ku, C.-C. (1993) · Cited by 2

Diagonal recurrent neural network-based control: convergence and stability

Ku, C.-C., & Lee, K. Y. (2005) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.1994.735193 · Cited by 7

Diagonal recurrent neural networks for dynamic systems control

Ku, C.-C., & Lee, K. Y. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.363441 · Cited by 726

Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates

Kumar, R., Srivastava, S., Gupta, J. R. P., & Mohindru, A. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.01.073 · Cited by 70

Diagonal recurrent neural network based control using adaptive learning rates

Ku, C.-C., & Lee, K. Y. (2005) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1992.371204 · Cited by 23