针对基于无线传感器网络构建的温室环境控制系统,为了减少无线网络固有的时变传输延时、丢包、网络拥塞等现象对控制性能的影响,该文从提高网络服务质量(quality of service,QoS)的角度出发,提出一种基于Takagi-Sugeno模糊控制器的QoS...针对基于无线传感器网络构建的温室环境控制系统,为了减少无线网络固有的时变传输延时、丢包、网络拥塞等现象对控制性能的影响,该文从提高网络服务质量(quality of service,QoS)的角度出发,提出一种基于Takagi-Sugeno模糊控制器的QoS管理策略。该QoS管理策略以截止期错失率作为QoS性能评价指标,针对传感器节点和执行器节点之间的数据传输,通过动态调整传感器节点的采样周期,使截止期错失率维持在设定水平,从而提高网络QoS。初步试验表明了该QoS管理策略的合理性、有效性和实用性。该QoS管理策略可以广泛应用于温室、农田、苗圃等区域。该研究为提高无线传感器网络在设施农业中的应用水平做出了有益探索。展开更多
Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidit...Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidity,light intensity,and CO 2 concentration.Due to the complex coupled correlations,it is a challenge to achieve coordination control of greenhouse environmental factors.This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints.In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm,case-based reasoning （CBR） is seamlessly incorporated into the Q-learning process.The experimental results demonstrate that this approach is practical,highly effective and efficient.展开更多
文摘针对基于无线传感器网络构建的温室环境控制系统,为了减少无线网络固有的时变传输延时、丢包、网络拥塞等现象对控制性能的影响,该文从提高网络服务质量(quality of service,QoS)的角度出发,提出一种基于Takagi-Sugeno模糊控制器的QoS管理策略。该QoS管理策略以截止期错失率作为QoS性能评价指标,针对传感器节点和执行器节点之间的数据传输,通过动态调整传感器节点的采样周期,使截止期错失率维持在设定水平,从而提高网络QoS。初步试验表明了该QoS管理策略的合理性、有效性和实用性。该QoS管理策略可以广泛应用于温室、农田、苗圃等区域。该研究为提高无线传感器网络在设施农业中的应用水平做出了有益探索。
基金supported by National Natural Science Foundationof China(No.60775014)
文摘Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidity,light intensity,and CO 2 concentration.Due to the complex coupled correlations,it is a challenge to achieve coordination control of greenhouse environmental factors.This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints.In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm,case-based reasoning （CBR） is seamlessly incorporated into the Q-learning process.The experimental results demonstrate that this approach is practical,highly effective and efficient.
