Research On State Fusion Estimation Of Cyber-physical System Based On Event-triggered

With the continuous progress of information technology,Cyber-Physical Systems(CPS)play important roles in human life and social production.Through organic integration and deep cooperation with computer,network communication technology and control technology,CPS can perform perception,dynamic control,management and comprehensive management of major engineering information systems.CPS is closely related to modern society and has been applied in multiple fields such as energy,transportation,industrial manufacturing,medical health,etc.With the increase of engineering needs,the system scale gradually expands and becomes more and more complex.In practical systems,unstable communication environment and limited sensor resources often lead to measurement data loss,sensor failure,time delay and other problems.How to ensure efficient and secure state estimation of CPS under measurement deviation,limited resource status and complex environment has become a research hotspot.Based on the hierarchical distributed fusion structure and event triggering mechanism,this paper studies the state estimation and fusion problem of CPS.The main content and innovation of this paper are as follows:For CPS with uncorrelated noise,in order to ensure stable operation of the system under measurement bias,a measurement event triggering mechanism is introduced for local nodes.It is composed of the squared distance between the current node measurement value and the current fused measurement value.The performance of the estimation is guaranteed using pseudo-measurements and variational Bayesian estimation.Based on this triggering mechanism,an optimal local multi-sensor measurement fusion algorithm is proposed,which can obtain the locally optimal estimated measurement values and transmit these fused measurement values to the fusion center for sequential state fusion estimation.For CPS with precision requirements for fusion estimation,an event-triggered mechanism based on the covariance trace of state estimation errors is proposed,and a distributed sequential weighted fusion algorithm is adopted to analyze the change in estimation accuracy of different nodes to the fusion center,and find the optimal sequential fusion order.Based on this,in order to better utilize energy resources,a random event energy scheduling problem is proposed,re-scheduling the system’s energy.Assuming that each event follows a Poisson process,two algorithms are proposed to obtain the Pareto optimal solution of the planning problem by introducing multi-objective nonlinear programming to solve nonlinear and energy problems.Finally,some practical simulations are conducted to verify our results,and the advantages of state estimation under this scheduling algorithm are analyzed.Considering complex information systems with missing measurement data and multiplicative noise,a fusion estimation algorithm based on measurement event triggering mechanism is proposed.By de-randomizing the original discrete-time system with stochastic noise,and further proposing a hierarchical fusion algorithm based on measurement event triggering mechanism for the model.Finally,the effectiveness of the algorithm is verified by experimental simulation.