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Vision-based control in the open racing car simulator with deep and reinforcement learning 期刊论文
Journal of Ambient Intelligence and Humanized Computing, 2019, 页码: doi={10.1007/s12652-019-01503-y}
作者:  Yuanheng Zhu;  Dongbin Zhao
Adobe PDF(2210Kb)  |  收藏  |  浏览/下载：51/12  |  提交时间：2023/04/26
Control of a Two-wheeled Machine with Two-directions Handling Mechanism Using PID and PD-FLC Algorithms 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 511-533
作者:  Khaled M. Goher;  Sulaiman O. Fadlallah
浏览  |  Adobe PDF(2563Kb)  |  收藏  |  浏览/下载：131/44  |  提交时间：2021/02/22
Two-wheeled inverted pendulum (IP) with two direction handling  Lagrangian formulation  proportional-integral-derivative (PID)  fuzzy logic control (FLC)  under-actuated systems.  
Synthesizing Robot Programs with Interactive Tutor Mode 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 462-474
作者:  Hao Li;  Yu-Ping Wang;  Tai-Jiang Mu
Adobe PDF(1023Kb)  |  收藏  |  浏览/下载：124/50  |  提交时间：2021/02/22
Human-robot interaction  semantic parsing  program synthesis  intelligent robotic systems  natural language understanding.  
Neural Adaptive Event-Triggered Control for Nonlinear Uncertain Stochastic Systems With Unknown Hysteresis 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 11, 页码: 3300-3312
作者:  Wang, Jianhui;  Liu, Zhi;  Zhang, Yun;  Chen, C. L. Philip
收藏  |  浏览/下载：263/0  |  提交时间：2020/03/30
Actuators  Hysteresis  Nonlinear systems  Artificial neural networks  Adaptive systems  Stochastic systems  System performance  Actuator failure  adaptive control  event-triggered  neural networks (NNs)  stochastic nonlinear systems  unknown direction hysteresis  
基于深度哈希学习的视觉检索研究 学位论文
, 中国科学院自动化研究所: 中国科学院大学, 2019
作者:  王运波
Adobe PDF(6243Kb)  |  收藏  |  浏览/下载：311/2  |  提交时间：2020/01/16
哈希学习，视觉检索  
Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 4, 页码: 1076-1087
作者:  Niu, Ben;  Wang, Ding;  Alotaibi, Naif D.;  Alsaadi, Fuad E.
收藏  |  浏览/下载：241/0  |  提交时间：2019/12/16
Adaptive tracking control  average dwell time (ADT)  neural networks (NNs)  nonstrict-feedback structure  stochastic nonlinear systems  switched nonlinear systems  
Adaptive Neural Network Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 12, 页码: 9458-9466
作者:  Liu, Yan-Jun;  Zeng, Qiang;  Tong, Shaocheng;  Chen, C. L. Philip;  Liu, Lei
收藏  |  浏览/下载：282/0  |  提交时间：2019/12/16
Active suspension systems (ASSs)  adaptive control  barrier Lyapunov function (BLF)  neural networks (NNs)  
Output Tracking Control Based on Adaptive Dynamic Programming With Multistep Policy Evaluation 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2019, 卷号: 49, 期号: 10, 页码: 2155-2165
作者:  Luo, Biao;  Liu, Derong;  Huang, Tingwen;  Liu, Jiangjiang
收藏  |  浏览/下载：232/0  |  提交时间：2019/12/16
Adaptive dynamic programming (ADP)  Bellman equation  heuristic dynamic programming  neural networks (NNs)  output tracking control  
Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 卷号: 15, 期号: 9, 页码: 4969-4977
作者:  Wen, Guoxing;  Chen, C. L. Philip;  Ge, Shuzhi Sam;  Yang, Hongli;  Liu, Xiaoguang
收藏  |  浏览/下载：221/0  |  提交时间：2019/12/16
Lyapunov function  neural networks (NNs)  nonlinear systems  optimized tracking control  reinforcement learning (RL) of actor-critic architecture  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 1, 页码: 295-305
作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载：311/0  |  提交时间：2019/07/12
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning