State Estimation Of Nonlinear Discrete Time-varying Dynamic Systems And Its Application

Since the 21 st century,computer network has been developed rapidly.With the improvement of science and technology,computer network is becoming more and more complex and widely used.However,it is worth mentioning that due to the limitation of network factors,the data will be affected in the transmission process to some extent,and the data usually obtained will become incomplete.Therefore,how to use the available measurement data to effectively estimate the internal system has become a big problem facing human beings.In this paper,a new robust state estimation method based on nonlinear measurement output data is proposed,and on this basis,the algorithm is further applied to the state estimation of complex networks.The specific work is as follows:1.The state estimation problem of discrete time-varying systems with random nonlinear and nonlinear measurement outputs is studied.The nonlinearity that happens randomly here is represented by a random sequence that obeys the Bernoulli distribution.In addition,a nonlinear function satisfying lipschitz condition is introduced to characterize the nonlinearity of the measured output.Using the known measurement output,a new robust state estimator is designed,Furthermore,the upper bound is optimized to obtain the explicit expression of the estimated gain.Finally,the practicability of the algorithm is illustrated by MATLAB simulation.2.Applying the research ideas of the previous part,the state estimation problem of discrete time-varying complex networks with measurement loss is solved.Based on the idea of the previous part,and the probability of data loss,a state estimator is designed.The explicit expression of state estimation gain is obtained by Riccati-like difference equation.On this basis,a sufficient condition is given that the mean square exponential of state estimation error is bounded.Finally,an example is given to illustrate the effectiveness of the proposed algorithm.