Reinforcement Learning Neural-Network-Based Controller for Nonlinear Discrete-Time Systems With Input Constraints

Pingan He; S. Jagannathan

doi:10.1109/tsmcb.2006.883869

Reinforcement Learning Neural-Network-Based Controller for Nonlinear Discrete-Time Systems With Input Constraints

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) ◽

10.1109/tsmcb.2006.883869 ◽

2007 ◽

Vol 37 (2) ◽

pp. 425-436 ◽

Cited By ~ 143

Author(s):

Pingan He ◽

S. Jagannathan

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Discrete Time ◽

Input Constraints ◽

Discrete Time Systems ◽

Time Systems

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Online Reinforcement Learning-based Neural Network Controller Design for Affine Nonlinear Discrete-time Systems

2007 American Control Conference ◽

10.1109/acc.2007.4282709 ◽

2007 ◽

Cited By ~ 4

Author(s):

Qinmin Yang ◽

S. Jagannathan

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Discrete Time ◽

Controller Design ◽

Neural Network Controller ◽

Discrete Time Systems ◽

Network Controller ◽

Time Systems

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Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

Control Theory and Technology ◽

10.1007/s11768-021-00046-y ◽

2021 ◽

Author(s):

Xin Chen ◽

Fang Wang

Keyword(s):

Neural Network ◽

Dynamic Programming ◽

Discrete Time ◽

Tracking Control ◽

Adaptive Dynamic Programming ◽

Linear Quadratic ◽

Optimal Tracking ◽

Discrete Time Systems ◽

Control Scheme ◽

Time Systems

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Output Feedback Controller for a Class of Unknown Nonlinear Discrete Time Systems Using Fuzzy Rules Emulated Networks and Reinforcement Learning

Fuzzy Information and Engineering ◽

10.1080/16168658.2021.1943887 ◽

2021 ◽

pp. 1-23

Author(s):

C. Treesatayapun

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Output Feedback ◽

Feedback Controller ◽

Fuzzy Rules ◽

Output Feedback Controller ◽

Discrete Time Systems ◽

Time Systems

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Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2018.2844165 ◽

2019 ◽

Vol 30 (1) ◽

pp. 295-305 ◽

Cited By ~ 15

Author(s):

Yan-Jun Liu ◽

Shu Li ◽

Shaocheng Tong ◽

C. L. Philip Chen

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Dead Zone ◽

Learning Control ◽

Discrete Time Systems ◽

Time Systems

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Input-to-state stable finite horizon MPC for neutrally stable linear discrete-time systems with input constraints

Systems & Control Letters ◽

10.1016/j.sysconle.2005.08.005 ◽

2006 ◽

Vol 55 (4) ◽

pp. 293-303 ◽

Cited By ~ 20

Author(s):

Jung-Su Kim ◽

Tae-Woong Yoon ◽

Ali Jadbabaie ◽

Claudio De Persis

Keyword(s):

Discrete Time ◽

Finite Horizon ◽

Input Constraints ◽

Discrete Time Systems ◽

Time Systems ◽

Stable Linear

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A Novel Reinforcement Learning Control for a class of Strict-feedback Discrete-time Systems via Multi-Gradient Recursive

10.1109/spac53836.2021.9540001 ◽

2021 ◽

Author(s):

Weiwei Bai ◽

Tieshan Li ◽

Yue Long

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Learning Control ◽

Discrete Time Systems ◽

Time Systems ◽

Strict Feedback

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Neural network-based AILC for non-repetitive trajectory tracking of non-affine pure-feedback discrete-time systems

2018 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN) ◽

10.1109/ispce-cn.2018.8805754 ◽

2018 ◽

Author(s):

Qing-Yuan Xu ◽

Choujun Zhan ◽

Yun-Shan Wei

Keyword(s):

Neural Network ◽

Discrete Time ◽

Trajectory Tracking ◽

Discrete Time Systems ◽

Time Systems

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Online optimal and adaptive integral tracking control for varying discrete‐time systems using reinforcement learning

International Journal of Adaptive Control and Signal Processing ◽

10.1002/acs.3115 ◽

2020 ◽

Vol 34 (8) ◽

pp. 971-991

Author(s):

Ibrahim Sanusi ◽

Andrew Mills ◽

Tony Dodd ◽

George Konstantopoulos

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Tracking Control ◽

Discrete Time Systems ◽

Time Systems

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Reinforcement Learning-Based Control for Nonlinear Discrete-Time Systems with Unknown Control Directions and Control Constraints

Neurocomputing ◽

10.1016/j.neucom.2020.03.061 ◽

2020 ◽

Vol 402 ◽

pp. 50-65

Author(s):

Miao Huang ◽

Cong Liu ◽

Xiaoqi He ◽

Longhua Ma ◽

Zheming Lu ◽

...

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Control Constraints ◽

Unknown Control Directions ◽

Discrete Time Systems ◽

Unknown Control ◽

And Control ◽

Time Systems

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$H_{\infty}$ Static Output-Feedback Control Design for Discrete-Time Systems Using Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2019.2901889 ◽

2020 ◽

Vol 31 (2) ◽

pp. 396-406 ◽

Cited By ~ 4

Author(s):

Amir Parviz Valadbeigi ◽

Ali Khaki Sedigh ◽

F. L. Lewis

Keyword(s):

Reinforcement Learning ◽

Discrete Time ◽

Output Feedback ◽

Output Feedback Control ◽

Control Design ◽

Static Output Feedback ◽

Discrete Time Systems ◽

Static Output Feedback Control ◽

Time Systems ◽

Static Output

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