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Editorial Special Issue on Adaptive Dynamic Programming and Reinforcement Learning

Derong Liu, Frank L. Lewis, Qinglai Wei
2020 IEEE Transactions on Systems, Man & Cybernetics. Systems  
The article by Peng et al. studies an online reinforcement Q-learning algorithm for H ∞ tracking control of unknown discrete-time linear systems, where both state-data-driven and output-data-driven reinforcement  ...  The next article by Hou et al. develops an H ∞ optimal tracking controller for completely unknown discrete-time nonlinear systems with control constraints by using an iterative adaptive learning algorithm  ... 
doi:10.1109/tsmc.2020.3025549 fatcat:fjskp6mfb5hc3hm27vkdhd7pni

2019 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 49

2019 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Event-Triggered Optimal Neuro-Controller Design With Reinforcement Learning for Unknown Nonlinear Systems.  ...  ., +, Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems With State Constraints.  ...  Open loop systems  ... 
doi:10.1109/tsmc.2019.2956665 fatcat:xhplbanlyne7nl7gp2pbrd62oi

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Stochas- Control for a Class of Stochastic Nonlinear Systems.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

Optimal control and learning for cyber‐physical systems

Yan Wan, Tao Yang, Ye Yuan, Frank L. Lewis
2021 International Journal of Robust and Nonlinear Control  
The papers received span broad topics including learning and data-driven optimal control to address physical unknowns and disturbances, estimation techniques to deal with uncertainties; secure and resilient  ...  The physical components include system dynamics, sensors, controllers, and the uncertain environment in which the system operates.  ...  Reinforcement learning is data-driven adaptive optimal control that does not require the full knowledge of physicals dynamics.  ... 
doi:10.1002/rnc.5442 fatcat:2sqn5j3urrgcrbjxfx6vsgvnci

2020 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 50

2020 IEEE Transactions on Systems, Man & Cybernetics. Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, TSMC Feb. 2020 609-616 Quantized Data Driven Iterative Learning Control for a Class of Nonlinear Systems With Sensor Saturation.  ... 
doi:10.1109/tsmc.2021.3054492 fatcat:zartzom6xvdpbbnkcw7xnsbeqy

2020 Index IEEE Transactions on Cybernetics Vol. 50

2020 IEEE Transactions on Cybernetics  
., Reference Trajectory Reshaping Optimi-zation and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation; TCYB Aug. 2020 3740-3751 Wu, X., Jiang, B., Yu, K., Miao, c., and Chen, H  ...  NN Reinforcement Learning Adaptive Control for a Class of Nonstrict-Feedback Discrete-Time Systems.  ...  ., +, Adaptive Neural Control of a Class of Stochastic Nonlinear Uncertain Systems With Guaranteed Transient Performance.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a

Table of contents

2021 IEEE Transactions on Cybernetics  
Lu 624 Decentralized Event-Triggered Control for a Class of Nonlinear-Interconnected Systems Using Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Wu, and T.Huang 927 Barrier Lyapunov Function-Based Adaptive Fault-Tolerant Control for a Class of Strict-Feedback Stochastic Nonlinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcyb.2021.3049976 fatcat:7dkbojkbazh2nmjvwwtrsmavxm

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Yiming Jiang, Chenguang Yang, Jing Na, Guang Li, Yanan Li, Junpei Zhong
2017 Complexity  
This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications.  ...  As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex  ...  For a class of uncertain nonlinear systems with unknown hysteresis, NN was used for compensation of the nonlinearities [61] .  ... 
doi:10.1155/2017/1895897 fatcat:t4rq6ux7brhnjicextnyh5o6dq

2020 Index IEEE Transactions on Automatic Control Vol. 65

2020 IEEE Transactions on Automatic Control  
Gasparri, A., +, TAC May 2020 1825-1840 Data-Driven Economic NMPC Using Reinforcement Learning.  ...  ., +, TAC Nov. 2020 4800-4807 Data-Driven Economic NMPC Using Reinforcement Learning.  ...  Linear programming A Decentralized Event-Based Approach for Robust Model Predictive Control.  ... 
doi:10.1109/tac.2020.3046985 fatcat:hfiqhyr7sffqtewdmcwzsrugva

Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning [article]

Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, Siqi Zhou, Jacopo Panerati, Angela P. Schoellig
2021 arXiv   pre-print
Our review includes: learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods  ...  The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities.  ...  contributions to this work by Karime Pereida and Sepehr Samavi, the invaluable suggestions and feedback by Hallie Siegel, as well as the support from the Natural Sciences and Engineering Research Council of  ... 
arXiv:2108.06266v2 fatcat:gbbe3qyatfgelgzhqzglecr5qm

Verification for Machine Learning, Autonomy, and Neural Networks Survey [article]

Weiming Xiang and Patrick Musau and Ayana A. Wild and Diego Manzanas Lopez and Nathaniel Hamilton and Xiaodong Yang and Joel Rosenfeld and Taylor T. Johnson
2018 arXiv   pre-print
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof.  ...  Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this  ...  In [30] , a method for learning from adaptive neural network control of a class of nonaffine nonlinear systems in uncertain dynamic environments is studied.  ... 
arXiv:1810.01989v1 fatcat:a5ax66lsxbho3fuxuh55ypnm6m

Industrial data science – a review of machine learning applications for chemical and process industries

Max Mowbray, Mattia Vallerio, Carlos Perez-Galvan, Dongda Zhang, Antonio Del Rio Chanona, Francisco J. Navarro-Brull
2022 Reaction Chemistry & Engineering  
Understand and optimize industrial processes via machine learning and chemical engineering principles.  ...  Acknowledgements The authors appreciate the support from JMP (SAS Institute Inc.) for facilitating the open access of this manuscript.  ...  For example, nonlinear dynamic optimization and particularly nonlinear model predictive control (NMPC) are powerful methodologies to address uncertain dynamic systems, however, there are several properties  ... 
doi:10.1039/d1re00541c fatcat:q7ielo4h2bgudlypi4vjd4aajm

Evolution of adaptive learning for nonlinear dynamic systems: a systematic survey

Mouhcine Harib, Hicham Chaoui, Suruz Miah
2022 Intelligence & Robotics  
In the 1990s, the field of Artificial Neural Networks was hugely investigated in general, and for control of dynamical systems in particular.  ...  AI in nonlinear dynamical systems and particularly in robotics.  ...  Nonaffine nonlinear systems Dai et al. [73] Obtaining the implicit desired control input (IDCI), and use of Learning from adaptive NN-based control for a class of NNs to approximate it nonaffine nonlinear  ... 
doi:10.20517/ir.2021.19 fatcat:xwp7dc3j6rdrraumuc5xigzici

2019 Index IEEE Transactions on Industrial Informatics Vol. 15

2019 IEEE Transactions on Industrial Informatics  
., +, TII June 2019 3163-3173 Constrained Sampled-Data ARC for a Class of Cascaded Nonlinear Systems With Applications to Motor-Servo Systems.  ...  ., +, Fully Integrated Open Solution for the Remote Operation of Pilot Plants. Constrained Sampled-Data ARC for a Class of Cascaded Nonlinear Systems With Applications to Motor-Servo Systems.  ... 
doi:10.1109/tii.2020.2968165 fatcat:utk3ywxc6zgbdbfsys5f4otv7u

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
GBSSL Method and Proposing a Fault Correction System; TII Aug. 2020 5300-5308  ...  Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K., 1625-1634 Jiang, X.  ...  ., +, TII Jan. 2020 319-327 Self-Evolving Neural Control for a Class of Nonlinear Discrete-Time Dynamic Systems With Unknown Dynamics and Unknown Disturbances.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i
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