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题名:
Helicopter Velocity Tracking Control by Adaptive Actor-Critic Reinforcement Method
作者: Hu Y(胡飏); Chen Y(陈洋); Han JD(韩建达); Wang YC(王越超); Qi JT(齐俊桐)
作者部门: 机器人学研究室
会议名称: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO)
会议日期: December 7-11, 2011
会议地点: Phuket, Thailand
会议主办者: IEEE Robotics and Automation Society
会议录: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO)
会议录出版者: IEEE Computer Society
会议录出版地: Piscataway, NJ
出版日期: 2011
页码: 1482-1486 1482-1486
收录类别: EI
EI收录号: 20122015013846
产权排序: 1
ISBN号: 978-1-4577-2136-6
关键词: Helicopters ; Learning ; Mathematical model ; Robots ; Training ; Vectors ; Vehicle dynamics
摘要: A robotic helicopter is an aircraft equipped with a sensing, computing, actuation, and communication infrastructure that allows it to execute a variety of tasks with autonomous mode. In this paper, we present an adaptive actor-critic reinforcement method to obtain near optimal controller for small autonomous helicopter. A network based on Q-value performs the critic and is trained by SARSA algorithm. A BP neural network, which is the actor network, generates control signal of helicopter dynamics. First, the proposed actor-critic reinforcement controller is introduced, then the algorithm is applied to an unmanned helicopter known as a highly nonlinear and complex system and the simulation results are presented.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8729
Appears in Collections:机器人学研究室_会议论文

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作者单位: 1.State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), 110016, Shenyang, China
2.Graduate School, CAS, 100039, Beijing, China
3.School of Information Science and Engineering, Wuhan University of Science and Technology, 430081, Wuhan, China

Recommended Citation:
Hu Y,Chen Y,Han JD,et al. Helicopter Velocity Tracking Control by Adaptive Actor-Critic Reinforcement Method[C]. 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO). Phuket, Thailand. December 7-11, 2011.Helicopter Velocity Tracking Control by Adaptive Actor-Critic Reinforcement Method.
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