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论文:2018,Vol:36,Issue(2):315-322 |
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引用本文： |
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高昂, 胡延苏, 李立欣, 段渭军, 张会生. 基于神经网络的云机器人服务质量控制方法研究[J]. 西北工业大学学报 |
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Gao Ang, Hu Yansu, Li Lixin, Duan Weijun, Zhang Huisheng. A BP Network Control Approach for QoS-Aware MAC in Cloud Robotics[J]. Northwestern polytechnical university |
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| 基于神经网络的云机器人服务质量控制方法研究 |
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| 高昂1, 胡延苏2, 李立欣1, 段渭军1, 张会生1 |
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1. 西北工业大学 电子信息学院, 陕西 西安 710072;
2. 长安大学 电子与控制工程学院, 陕西 西安 710064 |
| 摘要: |
| 云机器人通过动态"卸载"任务到云端高效处理，极大提高了节点的智能水平。然而，由于云端应用的实时性差异和负载的不可预知，对网络传输的服务质量（quality of service，QoS）需求不尽相同。从控制角度研究网络传输的服务质量问题，提出并实现了一种基于BP神经网络的双闭环接入控制方法（BPFD-MAC），在最大化能量利用率的同时，实现绝对服务质量和相对服务质量保证。通过反馈控制结构，将绝对QoS约束和相对QoS约束解耦为2个独立闭环：活动时间闭环根据高优先级的延迟控制节点活动时间，满足绝对约束；退避窗口闭环根据不同优先级的延迟比，调整退避时间的初始上限，保持相对延迟比例关系恒定，满足相对约束。并采用BP神经网络方法进行参数自适应校正和控制器设计。最后，基于ZigBit 900的硬件实验表明，相对于FD-MAC，BPFD-MAC不仅能够在负载动态变化时提供绝对和相对QoS保证，并且在网络高负载下，具有更高的吞吐量和能量利用率；在网络低负载下，具有更低的能耗。 |
| 关键词:
云机器人
服务质量
接入控制
BP神经网络
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| A BP Network Control Approach for QoS-Aware MAC in Cloud Robotics |
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| Gao Ang1, Hu Yansu2, Li Lixin1, Duan Weijun1, Zhang Huisheng1 |
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1. School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Electronic & Control Engineering, Chang'an University, Xi'an 710064, China |
| Abstract: |
| The basic idea of Cloud Robotics is dynamically uploading the compute-intensive applications to the cloud, which greatly enhances the intelligence of robots for the high processing and parallel ability of cloud. However, for the nature of uncertainty of mobility, different kinds of applications on robot may have different Quality of service (QoS). The paper proposes a BP network for QoS-aware MAC(BPFD-MAC) in Cloud Robotics form a view control theory, which can support both absolute and relative QoS guarantees while the energy saving. The hard and soft QoS constraints are de-coupled by normalized into a two-level cascade feedback loop. The former is Active Time Loop (AT-Loop) to enforce the absolute QoS guarantee for real-time application and the later is Contention Window Loop (CW-Loop) to enforce the relative QoS guarantee for Best Effort traffics. Finally, the Back-propagating (BP) neuron network based PID is used for self-tuning parameters and controller design. The hardware experiments demonstrate the feasibility of BPFD-MAC. Comparing with FD-MAC, BPFD-MAC has new feature of absolute QoS support and further developed two advantages:In the condition of heavy loads, BPFD have about 18% great throughput and 14% great power efficient; and in light load, BPFD have lower total energy consumption. |
| Key words:
cloud robotics
quality of service
MAC
back-propagating neuron network
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| 收稿日期: 2017-04-01
修回日期:
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| DOI: |
| 基金项目: 中国博士后科学基金（2017M623243）、陕西省自然科学基金（2016JM6062）、上海航天科技创新基金（SAST2016034）与中央高校基础研究基金（3102017ZY029）资助 |
| 通讯作者:
Email： |
| 作者简介: 高昂(1984-),西北工业大学副教授,主要从事网络服务质量保证及网络化控制研究。
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