Cited by 677Adaptive Dynamic Programming ControlAdvanced Control Systems OptimizationReinforcement Learning in Robotics

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Adaptive dynamic programming

Murray, J.J., Cox, C.J., Lendaris, G.G., Saeks, R.·2002·IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)

DOI 10.1109/tsmcc.2002.801727

Open this paper in CoChat — free account

Abstract

Unlike the many soft computing applications where it suffices to achieve a "good approximation most of the time," a control system must be stable all of the time. As such, if one desires to learn a control law in real-time, a fusion of soft computing techniques to learn the appropriate control law with hard computing techniques to maintain the stability constraint and guarantee convergence is required. The objective of the paper is to describe an adaptive dynamic programming algorithm (ADPA) which fuses soft computing techniques to learn the optimal cost (or return) functional for a stabilizable nonlinear system with unknown dynamics and hard computing techniques to verify the stability and convergence of the algorithm. Specifically, the algorithm is initialized with a (stabilizing) cost functional and the system is run with the corresponding control law (defined by the Hamilton-Jacobi-Bellman equation), with the resultant state trajectories used to update the cost functional in a soft computing mode. Hard computing techniques are then used to show that this process is globally convergent with stepwise stability to the optimal cost functional/control law pair for an (unknown) input affine system with an input quadratic performance measure (modulo the appropriate technical conditions). Three specific implementations of the ADPA are developed for 1) the linear case, 2) for the nonlinear case using a locally quadratic approximation to the cost functional, and 3) the nonlinear case using a radial basis function approximation of the cost functional; illustrated by applications to flight control.

Integrity signals

Registry

Crossref · 10.1109/tsmcc.2002.801727

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Dynamic Programming
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Linear Optimal Control Systems
Kwakernaak, H., Sivan, R., & Tyreus, B. N. D. (1974) · Journal of Dynamic Systems Measurement and Control · doi:10.1115/1.3426828 · Cited by 5,010
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., & Anderson, C. W. (1983) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1983.6313077 · Cited by 3,325
Stable adaptive fuzzy control of nonlinear systems
Wang, L.-X. (1993) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.227383 · Cited by 1,622
Dynamic Programming: Deterministic and Stochastic Models
Bertsekas, D. P. (1987) · Medical Entomology and Zoology · Cited by 1,348
Introduction to dynamic systems: theory, models and applications
(1981) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(81)90069-0 · Cited by 1,136
Gaussian Networks for Direct Adaptive Control
Sanner, R. M., & Slotine, J.-J. E. (1991) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1991.4791778 · Cited by 1,079
Approximate dynamic programming for real-time control and neural modeling
Werbos, P. J. (1992) · Medical Entomology and Zoology · Cited by 971
Introduction to dynamic systems
Williams, M. (2016) · Structural Dynamics · doi:10.1201/9781315375311-5 · Cited by 568
Adaptive neural network control of nonlinear systems by state and output feedback
Ge, S. S., Hang, C. C., & Zhang, T. (1999) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.809035 · Cited by 419

Flagged first, then by citations

Show all 32 references

Research works printed but not linked: 4

2001, Jour of Spacecraft and Rockets Special Section on Hyper-X, 38, 801
sitz, 1998, HYPER-X Hypersonic experimental research vehicle
wang, 1996, stable adaptive fuzzy control of nonlinear systems, IEEE Trans Syst Man Cybern B Cybern, 26, 677
barnett, 1971, The Matrices of Control Theory

Sorted by citations

Items 101–200 of 677

Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems
Yang, Y., Wunsch, D. C., & Yin, Y. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2654324 · Cited by 96
Data-driven control of hydraulic servo actuator based on adaptive dynamic programming
Djordjevic, V., Stojanović, V., Tao, H., Song, X., He, S., & Gao, W. (2022) · Discrete and Continuous Dynamical Systems - S · doi:10.3934/dcdss.2021145 · Cited by 93
Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management
Boaro, M., Fuselli, D., Angelis, F. D., Liu, D., Wei, Q., & Piazza, F. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9191-y · Cited by 91
Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm
Peng, Z., Zhao, Y., Hu, J., & Ghosh, B. K. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.079 · Cited by 91
Online approximate optimal control for affine non‐linear systems with unknown internal dynamics using adaptive dynamic programming
Yang, X., Liu, D., & Wei, Q. (2014) · IET Control Theory and Applications · doi:10.1049/iet-cta.2014.0186 · Cited by 89
Using reinforcement learning techniques to solve continuous‐time non‐linear optimal tracking problem without system dynamics
Zhu, Y., Zhao, D., & Li, X. (2016) · IET Control Theory and Applications · doi:10.1049/iet-cta.2015.0769 · Cited by 88
An iterative -optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state
Wei, Q., & Liu, D. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.027 · Cited by 87
A novel distributed optimal adaptive control algorithm for nonlinear multi-agent differential graphical games
Mazouchi, M., Sistani, M. B. N., & Sani, S. K. H. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510784 · Cited by 87
Data-based robust adaptive control for a class of unknown nonlinear constrained-input systems via integral reinforcement learning
Yang, X., Liu, D., Luo, B., & Li, C. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.051 · Cited by 84
Comprehensive comparison of online ADP algorithms for continuous-time optimal control
Zhu, Y., & Zhao, D. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-017-9548-4 · Cited by 83
A Review on Wireless Networked Control System: The Communication Perspective
Wang, Y., Wu, S., Lei, C., Jiao, J., & Zhang, Q. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3342032 · Cited by 82
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
Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework
Nowak, X., & Söffker, D. (2016) · Nonlinear Dynamics · doi:10.1007/s11071-016-2868-0 · Cited by 79
Model-based and model-free “plug-and-play” building energy efficient control
Baldi, S., Michailidis, I., Ravanis, C., & Kosmatopoulos, E. B. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.05.081 · Cited by 78
A supervised Actor–Critic approach for adaptive cruise control
Zhao, D., Wang, B., & Liu, D. (2013) · Soft Computing · doi:10.1007/s00500-013-1110-y · Cited by 77
PDP: parallel dynamic programming
Wang, F., Zhang, J., Wei, Q., Zheng, X., & Li, L. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510310 · Cited by 74
Numerical adaptive learning control scheme for discrete‐time non‐linear systems
Wei, Q., & Liu, D. (2013) · IET Control Theory and Applications · doi:10.1049/iet-cta.2012.0486 · Cited by 73
SP-SDP for Fuel Consumption and Tailpipe Emissions Minimization in an EVT Hybrid
Tate, E., Grizzle, J. W., & Peng, H. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2009.2023512 · Cited by 73
Event-Triggered Near-Optimal Control for Unknown Discrete-Time Nonlinear Systems Using Parallel Control
Lu, J., Wei, Q., Zhou, T., Wang, Z., & Wang, F. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3164977 · Cited by 71
Control of Nonaffine Nonlinear Discrete-Time Systems Using Reinforcement-Learning-Based Linearly Parameterized Neural Networks
Yang, Q., Vance, J. B., & Jagannathan, S. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.926607 · Cited by 70
A novel policy iteration based deterministic Q-learning for discrete-time nonlinear systems
Wei, Q., & Liu, D. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5462-z · Cited by 69
Model-Free Distributed Consensus Control Based on Actor–Critic Framework for Discrete-Time Nonlinear Multiagent Systems
Wang, W., Chen, X., Fu, H., & Wu, M. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2883801 · Cited by 67
Optimal control laws for time-delay systems with saturating actuators based on heuristic dynamic programming
Song, R., Zhang, H., Luo, Y., & Wei, Q. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.07.005 · Cited by 67
A policy iteration approach to online optimal control of continuous-time constrained-input systems
Modares, H., Sistani, M. B. N., & Lewis, F. L. (2013) · ISA Transactions · doi:10.1016/j.isatra.2013.04.004 · Cited by 67
Iterative ADP learning algorithms for discrete-time multi-player games
Jiang, H., & Zhang, H. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-017-9603-1 · Cited by 66
Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks
Yadav, V. H., Padhi, R., & Balakrishnan, S. N. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.899229 · Cited by 66
Online fault compensation control based on policy iteration algorithm for a class of affine non‐linear systems with actuator failures
Zhao, B., Liu, D., & Li, Y. (2016) · IET Control Theory and Applications · doi:10.1049/iet-cta.2015.1105 · Cited by 66
Continuous-Time Time-Varying Policy Iteration
Wei, Q., Liao, Z., Yang, Z., Li, B., & Liu, D. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2926631 · Cited by 66
Data-Driven Distributed Output Consensus Control for Partially Observable Multiagent Systems
Jiang, H., & He, H. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2788819 · Cited by 64
Adaptive control of a hybrid electric vehicle
Saeks, R. E., Cox, C. J., Neidhoefer, J. C., Mays, P. R., & Murray, J. (2002) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2002.804750 · Cited by 63
Self-citation
Adaptive optimal controllers based on Generalized Policy Iteration in a continuous-time framework
Vrabie, D., Vamvoudakis, K. G., & Lewis, F. L. (2009) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2009.5164743 · Cited by 62
Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning
Yang, X., Liu, D., Wang, D., & Wei, Q. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.03.008 · Cited by 61
Adaptive optimal control algorithm for continuous-time nonlinear systems based on policy iteration
Vrabie, D., & Lewis, F. L. (2008) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2008.4738955 · Cited by 61
Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form
Jagannathan, S., & He, P. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2003295 · Cited by 60
Discrete-time nonlinear HJB solution using Approximate dynamic programming: Convergence Proof
Al-Tamimi, A., & Lewis, F. L. (2007) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2007.368167 · Cited by 61
Model-free optimal control design for a class of linear discrete-time systems with multiple delays using adaptive dynamic programming
Zhang, J., Zhang, H., Luo, Y., & Feng, T. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.12.038 · Cited by 57
Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem
Vamvoudakis, K. G., & Lewis, F. L. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178586 · Cited by 57
Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method
Zhao, W., & Zhang, H. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.01.021 · Cited by 57
From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions
Powell, W. B. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-60990-0_3 · Cited by 60
Random Sampling of States in Dynamic Programming
Atkeson, C. G., & Stephens, B. J. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.926610 · Cited by 56
Policy iterations for reinforcement learning problems in continuous time and space — Fundamental theory and methods
Lee, J. J., & Sutton, R. S. (2021) · Automatica · doi:10.1016/j.automatica.2020.109421 · Cited by 55
Actor–critic learning based PID control for robotic manipulators
Nohooji, H. R., Zaraki, A., & Voos, H. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111153 · Cited by 55
Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Method
Li, N., Li, X., Peng, J., & Xu, Z. Q. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3181248 · Cited by 54
DHP Method for Ramp Metering of Freeway Traffic
Zhao, D., Bai, X., Wang, F.-Y., Xu, J. M., & Yu, W. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2122257 · Cited by 55
Web Service QoS Prediction Based on Adaptive Dynamic Programming Using Fuzzy Neural Networks for Cloud Services
Luo, X., Lv, Y., Li, R., & Chen, Y. (2015) · IEEE Access · doi:10.1109/access.2015.2498191 · Cited by 54
Online adaptive optimal control for continuous‐time Markov jump linear systems using a novel policy iteration algorithm
He, S., Song, J. L., Ding, Z., & Liu, F. (2015) · IET Control Theory and Applications · doi:10.1049/iet-cta.2014.0944 · Cited by 53
Adaptive critic design-based robust neural network control for nonlinear distributed parameter systems with unknown dynamics
Luo, Y., Sun, Q., Zhang, H., & Cui, L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.08.049 · Cited by 52
Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors
Wei, Q., Li, B., & Song, R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2661865 · Cited by 52
Reinforcement learning and neural networks for multi-agent nonzero-sum games of nonlinear constrained-input systems
Yasini, S., Sistani, M. B. N., & Kirampor, A. (2014) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-014-0300-y · Cited by 51
Sampled‐data‐based adaptive optimal output‐feedback control of a 2‐degree‐of‐freedom helicopter
Gao, W., Huang, M., Jiang, Z., & Chai, T. (2016) · IET Control Theory and Applications · doi:10.1049/iet-cta.2015.0977 · Cited by 49
Asymptotic tracking by a reinforcement learning-based adaptive critic controller
Bhasin, S., Sharma, N., Patre, P. M., & Dixon, W. E. (2011) · Journal of Control Theory and Applications · doi:10.1007/s11768-011-0170-8 · Cited by 48
Residential energy system control and management using adaptive dynamic programming
Huang, T., & Liu, D. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033209 · Cited by 50
Continuous-Time ADP for Linear Systems with Partially Unknown Dynamics
Vrabie, D., Abu-Khalaf, M., Lewis, F. L., & Wang, Y. (2007) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2007.368195 · Cited by 48
Bias-policy iteration based adaptive dynamic programming for unknown continuous-time linear systems
Jiang, H., & Zhou, B. (2021) · Automatica · doi:10.1016/j.automatica.2021.110058 · Cited by 47
Dynamic event-triggered optimal safety control of interconnected nonlinear systems with discontinuous state constraints by adaptive dynamic programming
Qin, C., Sun, K., Sun, A., Zhang, D., Zhang, J., Wang, G., Wang, Y., & Qiu, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132467 · Cited by 47
Adaptive Optimal Control for Large-Scale Nonlinear Systems
Michailidis, I., Baldi, S., Kosmatopoulos, E. B., & Ioannou, P. (2017) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2684458 · Cited by 47
Online policy iteration based algorithms to solve the continuous-time infinite horizon optimal control problem
Vamvoudakis, K. G., Vrabie, D., & Lewis, F. L. (2009) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2009.4927523 · Cited by 46
H ∞ control with constrained input for completely unknown nonlinear systems using data-driven reinforcement learning method
Jiang, H., Zhang, H., Luo, Y., & Cui, X. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.11.041 · Cited by 45
Policy Approximation in Policy Iteration Approximate Dynamic Programming for Discrete-Time Nonlinear Systems
Guo, W., Si, J., Liu, F., & Mei, S. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2702566 · Cited by 45
A new iterative algorithm for solving H ∞ control problem of continuous-time Markovian jumping linear systems based on online implementation
Song, J. L., He, S., Ding, Z., & Liu, F. (2016) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.3531 · Cited by 44
Adaptive composite suboptimal control for linear singularly perturbed systems with unknown slow dynamics
Yang, C., Zhong, S., Liu, X., Dai, W., & Zhou, L. (2020) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.4895 · Cited by 43
On Model Identification Based Optimal Control and It’s Applications to Multi-Agent Learning and Control
Luo, R., Peng, Z., & Hu, J. (2023) · Mathematics · doi:10.3390/math11040906 · Cited by 42
Dual Heuristic dynamic Programming for nonlinear discrete-time uncertain systems with state delay
Wang, B., Zhao, D., Alippi, C., & Liu, D. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.06.037 · Cited by 42
A retrospective on Adaptive Dynamic Programming for control
Lendaris, G. G. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178716 · Cited by 42
Self-citation
Fuzzy adaptive dynamic programming-based optimal leader-following consensus for heterogeneous nonlinear multi-agent systems
Cai, Y., Zhang, H., Zhang, K., & Liu, C. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04263-0 · Cited by 42
Value-gradient learning
Fairbank, M., & Alonso, E. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252791 · Cited by 47
Data‐driven policy iteration algorithm for optimal control of continuous‐time Itô stochastic systems with Markovian jumps
Song, J. L., He, S., Liu, F., Niu, Y., & Ding, Z. (2016) · IET Control Theory and Applications · doi:10.1049/iet-cta.2015.0973 · Cited by 41
Data-driven adaptive dynamic programming schemes for non-zero-sum games of unknown discrete-time nonlinear systems
Jiang, H., Zhang, H., Zhang, K., & Cui, X. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.020 · Cited by 41
Reinforcement learning for a class of continuous-time input constrained optimal control problems
Yaghmaie, F. A., & Braun, D. J. (2018) · Automatica · doi:10.1016/j.automatica.2018.10.038 · Cited by 41
Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems With Application to Engine Emission Control
Shih, P., Kaul, B. C., Jagannathan, S., & Drallmeier, J. A. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2013272 · Cited by 40
Neuro-optimal tracking control for a class of discrete-time nonlinear systems via generalized value iteration adaptive dynamic programming approach
Wei, Q., Liu, D., & Xu, Y. (2014) · Soft Computing · doi:10.1007/s00500-014-1533-0 · Cited by 40
Event-triggered optimal control for discrete-time multi-player non-zero-sum games using parallel control
Lu, J., Wei, Q., Wang, Z., Zhou, T., & Wang, F. (2021) · Information Sciences · doi:10.1016/j.ins.2021.10.073 · Cited by 40
An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance
Chen, C., Geroliminis, N., & Zhong, R. (2024) · Transportation Science · doi:10.1287/trsc.2023.0091 · Cited by 42
Dual iterative adaptive dynamic programming for a class of discrete-time nonlinear systems with time-delays
Wei, Q., Wang, D., & Zhang, D. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1188-7 · Cited by 39
Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes
Luo, B., Wu, H., & Li, H. (2014) · Industrial & Engineering Chemistry Research · doi:10.1021/ie4031743 · Cited by 39
Neural-network-based control scheme for a class of nonlinear systems with actuator faults via data-driven reinforcement learning method
Jiang, H., Zhang, H., Liu, Y., & Han, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.047 · Cited by 39
Event-triggered adaptive dynamic programming for discrete-time multi-player games
Wang, Z., Wei, Q., & Liu, D. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.071 · Cited by 39
Online learning based on adaptive learning rate for a class of recurrent fuzzy neural network
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04372-w · Cited by 39
A Novel Structure of Actor-Critic Learning Based on an Interval Type-2 TSK Fuzzy Neural Network
Khater, A. A., El-Nagar, A. M., El-Bardini, M. A., & El-Rabaie, N. M. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2949554 · Cited by 39
Document image classification: Progress over two decades
Liu, L., Wang, Z., Qiu, T., Chen, Q., Lu, Y., & Suen, C. Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.114 · Cited by 40
Broad Learning System Approximation-Based Adaptive Optimal Control for Unknown Discrete-Time Nonlinear Systems
Yuan, L., Li, T., Tong, S., Xiao, Y., & Shan, Q. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2021.3113357 · Cited by 39
Infinite-time stochastic linear quadratic optimal control for unknown discrete-time systems using adaptive dynamic programming approach
Wang, T., Zhang, H., & Luo, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.06.053 · Cited by 39
Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems
Wei, Q., & Liu, D. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1361-7 · Cited by 38
Backstepping-Based Lyapunov Function Construction Using Approximate Dynamic Programming and Sum of Square Techniques
Wang, Z., Liu, X., Liu, K., Li, S., & Wang, H. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2574747 · Cited by 38
Stochastic linear quadratic optimal control for model-free discrete-time systems based on Q-learning algorithm
Wang, T., Zhang, H., & Luo, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.018 · Cited by 38
Actor–Critic-Based Predefined-Time Fuzzy Adaptive Optimal Control for Uncertain Nonlinear Systems With Input Saturation
Yang, W., Wang, Q., Liu, J., & Yu, J. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3355129 · Cited by 37
Distributed Adaptive Flocking Control for Large-Scale Multiagent Systems
Dey, S., & Xu, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3343666 · Cited by 37
Adaptive dynamic programming as a theory of sensorimotor control
Jiang, Y., & Jiang, Z. (2014) · Biological Cybernetics · doi:10.1007/s00422-014-0613-7 · Cited by 38
A Parallel Framework of Adaptive Dynamic Programming Algorithm With Off-Policy Learning
Sun, C., Li, X., & Sun, Y. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015767 · Cited by 38
Learning-based adaptive optimal output regulation of linear and nonlinear systems: an overview
Gao, W., & Jiang, Z. (2022) · Control Theory and Technology · doi:10.1007/s11768-022-00081-3 · Cited by 38
Nonlinear two-player zero-sum game approximate solution using a Policy Iteration algorithm
Johnson, M. L., Bhasin, S., & Dixon, W. E. (2011) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2011.6160778 · Cited by 36
Approximate dynamic programming based supplementary reactive power control for DFIG wind farm to enhance power system stability
Guo, W., Liu, F., Si, J., He, D., Harley, R. G., & Mei, S. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.03.089 · Cited by 36
Data-based adaptive neural network optimal output feedback control for nonlinear systems with actuator saturation
Wang, T., Sui, S., & Tong, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.03.053 · Cited by 36
Model-free control of Lorenz chaos using an approximate optimal control strategy
Li, S., Li, Y., Liu, B., & Murray, T. (2012) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2012.05.024 · Cited by 35
Nonlinear neuro-optimal tracking control via stable iterative Q-learning algorithm
Wei, Q., Song, R., & Sun, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.075 · Cited by 35
Application of policy iteration technique based adaptive optimal control design for automatic voltage regulator of power system
Prasad, L. B., Gupta, H. O., & Tyagi, B. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.06.057 · Cited by 34
A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints
Wang, D., Liu, D., Zhao, D., Huang, Y., & Zhang, D. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0707-2 · Cited by 33
DFIG Damping Controller Design Using Robust CKF-Based Adaptive Dynamic Programming
Mir, A. S., & Senroy, N. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2910262 · Cited by 33
Neural Networks in Feedback Control Systems
Vamvoudakis, K. G., Lewis, F. L., & Ge, S. S. (2015) · Mechanical Engineers' Handbook · doi:10.1002/9781118985960.meh223 · Cited by 33
Online adaptive optimal control algorithm based on synchronous integral reinforcement learning with explorations
Guo, L., & Zhao, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.055 · Cited by 32

Authors: Murray, J. J., Cox, C. J., Lendaris, G. G., & Saeks, R.
Affiliations: Stony Brook University (US)
Accurate Automation (United States) (US)
Portland State University (US)
ORCID: Murray, J.J.
Venue: IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2002-05
Volume: 32
Issue: 2
Pages: 140-153
ISSN: 1094-6977 (ISSN-L)
DOI: 10.1109/tsmcc.2002.801727
OpenAlex: W2160561608
Cited by: 677
References: 32
Citations by year: 2026: 16 · 2025: 36 · 2024: 31 · 2023: 26 · 2022: 34 · 2021: 34 · 2020: 39 · 2019: 28 · 2018: 27 · 2017: 42 · 2016: 36 · 2015: 40 · 2014: 42 · 2013: 43 · 2012: 45
FWCI: 4.99
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) (publishedVersion)
PDXScholar (Portland State University) (submittedVersion)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Dynamic Programming Control
Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords: adaptive dynamic programming, optimal control, Hamilton-Jacobi-Bellman equation, stability analysis, convergence analysis, unknown dynamics, nonlinear systems, input-affine systems, radial basis function approximation, flight control, soft computing

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Adaptive dynamic programming

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks