The Study Of Limited Communications Networked System State Estimation

Networked state estimation system (NSES) is that the traditional state estimation system introduces network into the traditional point-to-point systems. NSES has many advantages, such as high flexibility,simple installation and maintenance, increase the reliability of the system and can realize the advantages of information resources sharing, therefore has received extensive attention in the field of control. However, due to the introduction of network, makes the analysis and design of the whole system becomes more complex, at the same time, it will inevitably cause communications limited, the network induced delay, packet loss and packet transmission, timing disorder and the variable sampling, these can reduce the performance of the system and even lead to instability of the system. In the NSES, it is very important that how to solve the problems in the situation of communications limited, the network induced delay and packet dropout.This paper is based on the traditional Kalman optimal state estimation algorithm, When the NSES in the situation of communications limited, network induced delay and packet dropout, we should how to solve the problem of network state estimation system. The problem is decomposed into a variety of situations to discuss and proposed the networked optimal state estimation algorithm of state compensation. The main research is as follows:Firstly, according to the degree of the communications limited, the problem can be divided into four cases. Detailing description the NSES in the case of only have the communications limited, in the case of the communications limited and packet dropout, in the case of the communications limited and network induced delay, and in the case of the communications limited and simultaneously exist the network induced delay and packet dropout, respectively, different system model is constructed, and the state of the compensation methods are given.Secondly, as for the four cases of communications limited, proposes the corresponding discrete stochastic linear system networked optimal state estimation algorithm, respectively. According to the linear minimum variance estimate criterion, using the linear minimum variance estimation Kalman optimal filtering principle, when the states of NSES are completely observable, deducing the optimal filtering basic equations. Established the sufficient and necessary conditions of consistent completely controllable and consistent completely observability to the certain linear systems and the stochastic linear system, respectively, and the stability of the networked state estimation is discussed.Thirdly, when the networked optimal state estimation algorithm is used in the practical application, due to the problem of choosing the initial value is not accurate or due to insufficient for the physical processes of the object model error, thus may lead to the problem of filtering divergence. This chapter for the communications limited, the filtering divergence problem solutions are given, and the corresponding attenuation memory filtering algorithm are presented.Finally, as to the networked optimal state estimation compensation methods and the methods to solve the problem of the state estimation filtering divergence, showing simulations to verify the correctness of the theoretical analysis and the real effectiveness of the method.