Study On Optimized State Estimation Of Time-Varying Complex Networks Subject To Information Scheduling Under Unreliable Communication

With the rapid development of communication technology,the complex networks(CNs)have been widely used in many fields,such as transportation networks,smart grid,social networks and so on.In order to deeply explore the dynamic behavior of CNs,the design of state estimation algorithms for CNs has gained extensive attention.Due to the uncertain network transmission environment,it is usually necessary to consider the unreliable communication situation(incomplete observation,information scheduling strategies and so on),which will bring certain difficulties to the analysis and synthesis of CNs.At present,there are relatively few results on the variance-constrained state estimation for time-varying CNs(TVCNs)under unreliable communication.Consequently,this thesis aims to fully examine the impact from information scheduling strategies and incomplete observation onto the analyses of TVCNs.Some new distrubuted optimized state estimation methods and the evaluation criteria of algorithm performance for TVCNs are proposed under unreliable communication,which can provide effective methods to solve the problems of optimized state estimation for TVCNs.Specifically,this thesis will be studied from the following aspects:1.Based on the round-robin protocol schedule,the optimized state estimation problem is studied for stochastic inner coupled CNs with random switching nonlinearities.A Bernoulli random variable is used to describe the phenomenon of random switching way between two nonlinear functions.The random coupling phenomenon is described by the random variables with specific probability distributation.The round-robin protocol schedule is introduced to assign data transmission rights thereby avoiding the occurrence of channel congestion and a new compensation-based time-varying recursive estimator is constructed.The covariance of the state estimation error and its upper bound are given by means of stochastic analysis as well as matrix theory and the estimator gain is designed in the minimum mean-square error sense.The corresponding theoretical proofs are presented which include the boundedness of obtained upper bound and the monotonicity of the trace of such upper bound with respect to the coupling strength.2.Based on the event-triggered schedule with exponential-type,the optimized state estimation problem is solved for stochastic uncertain outer coupled CNs with fading measurements.For each network node,the data fading model is given.A random sequence with known statistical properties and bounded uncertain parameters are introduced to characterize the random uncertain coupling between the network nodes.An event-triggered scheduling scheme is employed to regulate data transmission rights thereby avoiding unnecessary real-time data transmission.The covariance of the estimation error and its upper bound equation are established based on the matrix theory.The estimator gain is designed in the sense of minimum mean-square error.A theoretical proof is given to clarify the monotonicity of the trace of the upper bound with respect to the fading probability.3.Based on the dynamic event-triggered schedule,the optimized state estimation issue is investigated for time-varying output coupled CNs with measurement outliers.Different from the event-triggered scheduling strategy mentioned before,a more general triggered criterion is described based on a dynamic variable driven by a dynamical equation so as to regulate the frequency of data transmission.Considering the situation with measurement outliers,a saturation function is introduced to constrain the innovation equation to reduce the impact from outliers onto the estimation accuracy.A new two-step state estimation equation with innovation constraints is established and the form of the covariance upper bound of state estimation error is derived.The estimator gain matrix is parameterized by minimizing the variance-constrained index,and a new distributed optimized estimation algorithm is developed.Finally,a criterion is presented to ensure the boundedness of the developed optimized state estimation algorithm.4.Based on the amplify-and-forward relay schedule,the optimized state estimation approach is proposed for TVCNs with data missing and uniform quantization.In the node-to-node channel,the uniform quantization phenomenon is considered.Considering the case of limited network transmission distance,the amplify-and-forward relay schedule is employed to extend the transmission range of data so as to guarantee that the remote data transmission task can be fulfilled successfully.A new two-step state estimation equation is constructed based on the accessible observational information and the recursive equation concerning on the covariance upper bound of estimation error is established.In order to minimize the variance-constrained index,the estimator gain is designed and the optimized state estimation algorithm is given.Finally,a rigorous theoretical proof is given to analyze the monotonicity of the trace of such upper bound with respect to the missing probability.5.Based on the stochastic communication schedule,the optimized state estimation problem is discussed for time-varying periodic coupled CNs under the influence of probability quantization.The periodic switching topologies of CNs are described by period-dependent time-varying functions.The phenomenon of probability quantization is considered in the process of data transmission.The random communication scheduling model is introduced so as to reduce the amount of real-time data transmission and a new state estimator with periodic coupling structure is constructed.The covariance upper bound of the estimation error is obtained with the help of matrix inequalities.The estimator gain is parameterized to ensure that the trace of the obtained upper bound is minimized and a sufficient condition is given based on certain conditions to ensure the boundedness of the developed optimized estimation strategy.6.Based on the coding-decoding communication schedule,the state estimation approach is provided for time-varying state-saturated CNs subject to bit rate constraints and random coupling parameters.A random matrix with known probability information is adopted to describe the random varying topologies of CNs.Considering the communication limitation and network security,the coding-decoding communication scheduling strategy under the bit rate constraints is adopted to process the transmitted data.The covariance of estimation error based on the definition and its upper bound are presented,and the estimator gain is designed in the sense of minimum mean-square error.In addition,combined with the state-saturated constraint,a sufficient condition regarding the boundedness of the obtained upper bound is given.Finally,the proposed optimized state estimation strategy based on the coding-decoding schedule subject to bit rate constraints is applied to solve the tracking and positioning problem of mobile robot to verify the effectiveness and practicability of the designed estimation scheme.