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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  ...  ., +, TCYB March 2020 890-901 Event-Triggered Reinforcement Learning-Based Adaptive Tracking Control for Completely Unknown Continuous-Time Nonlinear Systems.  ...  ., +, TCYB Jan. 2020 201-210 Event-Triggered Reinforcement Learning-Based Adaptive Tracking Control for Completely Unknown Continuous-Time Nonlinear Systems.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a

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.  ...  ., +, TSMC July 2019 1435-1447 Event-Triggered Optimal Neuro-Controller Design With Reinforcement Learning for Unknown Nonlinear Systems.  ...  Open loop systems  ... 
doi:10.1109/tsmc.2019.2956665 fatcat:xhplbanlyne7nl7gp2pbrd62oi

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.  ...  ., +, TSMC Sept. 2020 3401-3411 Reinforcement Q-Learning Algorithm for H ∞ Tracking Control of Unknown Discrete-Time Linear Systems.  ...  ., +, TSMC March 2020 734-746 Reinforcement Q-Learning Algorithm for H ∞ Tracking Control of Unknown Discrete-Time Linear Systems.  ... 
doi:10.1109/tsmc.2021.3054492 fatcat:zartzom6xvdpbbnkcw7xnsbeqy

2019 Index IEEE Transactions on Automatic Control Vol. 64

2019 IEEE Transactions on Automatic Control  
., +, TAC Feb. 2019 640-653 Reinforcement Learning-Based Adaptive Optimal Exponential Tracking Control of Linear Systems With Unknown Dynamics.  ...  ., +, TAC Sept. 2019 3920-3927 Reinforcement Learning-Based Adaptive Optimal Exponential Tracking Control of Linear Systems With Unknown Dynamics.  ... 
doi:10.1109/tac.2020.2967132 fatcat:o2hd2t4jz5fbpkcemjt5aj7xrm

2020 Index IEEE Transactions on Automatic Control Vol. 65

2020 IEEE Transactions on Automatic Control  
., +, TAC July 2020 3150-3156 Posterior Sampling-Based Reinforcement Learning for Control of Unknown Linear Systems.  ...  ., +, TAC Jan. 2020 223-236 Posterior Sampling-Based Reinforcement Learning for Control of Unknown Linear Systems.  ...  Linear programming A Decentralized Event-Based Approach for Robust Model Predictive Control.  ... 
doi:10.1109/tac.2020.3046985 fatcat:hfiqhyr7sffqtewdmcwzsrugva

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.  ...  ., +,4933-4945 Reinforcement Learning-Based Nearly Optimal Control for Constrained-Input Partially Unknown Systems Using Differentiator.  ...  ., +, TNNLS Aug. 2020 2930-2941 Discrete time systems H 3 Static Output-Feedback Control Design for Discrete-Time Systems Using Reinforcement Learning.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems [article]

Joao Paulo Jansch-Porto, Bin Hu, Geir Dullerud
2020 arXiv   pre-print
In this paper, we introduce the problem of controlling unknown (discrete-time) MJLS as a new benchmark for policy-based reinforcement learning of Markov decision processes (MDPs) with mixed continuous/  ...  Our simulation results suggest that the natural gradient method can efficiently learn the optimal controller for MJLS with unknown dynamics.  ...  In this paper, our key point is that the problem of controlling unknown (discrete-time) Markov Jump Linear Systems (MJLS) (Costa c 2020 J.P. Jansch-Porto, B.  ... 
arXiv:2006.03116v2 fatcat:s22byk4rx5gtteyf4byfruevze

A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

Yiming Jiang, Chenguang Yang, Hongbin Ma
2016 Discrete Dynamics in Nature and Society  
Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed.  ...  In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions.  ...  [101] used the HDP algorithm to develop a NN-based optimal controller for unknown discrete-time nonlinear systems.  ... 
doi:10.1155/2016/7217364 fatcat:qkjxrfnvpja4zjzk2cgmg6x7tm

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 [141] , the authors present an algorithm for iterative adaptive dynamic pro-gramming (ADP) for problems of optimal tracking control on infinite horizon nonlinear system that are discrete-time.  ... 
arXiv:1810.01989v1 fatcat:a5ax66lsxbho3fuxuh55ypnm6m

Design and Comparison of Reinforcement-Learning-Based Time-Varying PID Controllers with Gain-Scheduled Actions

Yi-Liang Yeh, Po-Kai Yang
2021 Machines  
This paper presents innovative reinforcement learning methods for automatically tuning the parameters of a proportional integral derivative controller.  ...  Conventionally, the high dimension of the Q-table is a primary drawback when implementing a reinforcement learning algorithm.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/machines9120319 fatcat:jdpnaicfwjfdthosj5vvg4yll4

Learning Control in Robotics

Stefan Schaal, Christopher Atkeson
2010 IEEE robotics & automation magazine  
Keywords Robot learning, learning control, reinforcement learning, optimal control.  ...  research was supported in part by National Science Foundation grants ECS-0326095, EEC-0540865, and ECCS-0824077, IIS-0535282, CNS-0619937, IIS-0917318, CBET-0922784, EECS-0926052, the DARPA program on Learning  ...  Many different approaches have been suggested in the literature, for instance, based on splines [79] , hidden Markov models [80] , nonlinear attractor systems [76] , and other methods.  ... 
doi:10.1109/mra.2010.936957 fatcat:sg4pl7qbrnfyvc7stg5wslkyry

Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning

Pengzhan Chen, Zhiqiang He, Chuanxi Chen, Jiahong Xu
2018 Algorithms  
Jiang, Zhang, and Luo et al. [14] adopted a reinforcement learning method to realize an optimized tracking control of completely unknown nonlinear Markov jump systems.  ...  DDPG is a data-driven control method that can learn the mathematical model of the system according to the input and output data of the system and realize the optimal control of the system according to  ...  Control Scheme Based on Reinforcement Learning PID Parameter Tuning Method Based on Reinforcement Learning This study uses actor-critic to construct the framework of reinforcement learning agent 1,  ... 
doi:10.3390/a11050065 fatcat:zog7mfziznarvgrsvzon4j43ju

Deep Reinforcement Learning for Event-Triggered Control [article]

Dominik Baumann and Jia-Jie Zhu and Georg Martius and Sebastian Trimpe
2018 arXiv   pre-print
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.  ...  In particular, we demonstrate that it can, other than many model-based ETC designs, be straightforwardly applied to nonlinear systems.  ...  BACKGROUND We consider a nonlinear, discrete-time system disturbed by additive Gaussian noise, x k+1 = f (x k , u k ) + v k (1a) y k = x k + w k , (1b) with k the discrete-time index, x k , y k , v k ,  ... 
arXiv:1809.05152v1 fatcat:jbagfw2innedlp5br4hdbc5w4u

A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning

Mengqian Lin, Rui Zhao, Kapil Sharma
2022 Computational Intelligence and Neuroscience  
However, the existing music generation neural network methods have not yet solved the problems of discrete integrability brought by piano roll representation music data and the still-limited control domain  ...  In this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach.  ...  Conclusion In this paper, an event-triggered heuristic dynamic programming algorithm is proposed for solving the optimal control problem of discrete-time nonlinear systems with unknown models.  ... 
doi:10.1155/2022/4999447 pmid:35655518 pmcid:PMC9152395 fatcat:gszxjzbihvby3akb5lafsfbz2e

Conference Guide [Front matter]

2020 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)  
controller design for discrete-time Markov jump systems.  ...  This paper aims at using an off-policy integral reinforcement learning (IRL) algorithm to solve the linear quadratic tracking (LQT) control problem of completely unknown continuous-time systems, such that  ...  Security of consensus control is of key significance in multi-agent systems.  ... 
doi:10.1109/icarcv50220.2020.9305477 fatcat:4h7gpoj7ljgsrlkjoyw3qcfzxi
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