摘要
针对存在含未知输入和过程,测量噪声相关的随机系统,设计了随机系统的事件驱动状态估计器。利用随机稳定性理论,通过借助随机Laypunov函数推导了事件触发的状态估计器增益;以此使相应的状态估计误差均方收敛,并利用估计器的输出误差对相应的未知输入进行抑制。借助近似二次性能指标设计了相应的事件驱动传输策略,保证了估计误差、数据通信速率和传感器电池寿命得到一个很好的平衡。最后,利用数值仿真的实例进行了验证。结果表明,所设计的事件驱动状态估计器能有效地估计系统的状态,并延长约45%的传感器电池使用寿命,这也说明本研究所提出的算法在一定程度上降低了网络资源的使用率。
In this paper,an event-driven state estimator is designed aiming at the stochastic system which contains unknown inputs and processes,and the correlated measurement noise.Firstly,the event-triggered state estimator’s gain is deduced by using the random stability theory and Lyapunov’s function,and based on which the corresponding state estimation errors are calculated in mean square convergence.Secondly the corresponding unknown inputs are inhibited by using output errors of the estimator.In addition,the corresponding event-driven transmission strategy is designed by using quadratic performance index which guarantees a good balance between estimation errors,data transmission rate and the sensor’s battery life.Finally,numerical simulation instance verified that the designed event-driven state estimator can estimate the system’s state effectively and extend the sensor’s battery life by about 45%.And the proposed algorithm was proved that the utilization of network resource was reduced to some degree.
作者
牛超
李云骥
NIU Chao;LI Yunji(Department of Mechanical Engineering,Henan Mechanical and Electrical Vocational College, Zhengzhou 451191,China;Engineering Research Center of Internet of Things Technology Applications,Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《河南农业大学学报》
CAS
CSCD
北大核心
2018年第5期749-754,共6页
Journal of Henan Agricultural University
基金
河南省科技攻关项目(162102210320)
关键词
事件驱动
状态估计
未知输入
噪声
event-triggered
state estimator
unknown input
correlated noises
