Modeling And Fusion Estimation For Networked Control Systems

With the rapid development of network communication technology and computertechnology, a large number of networked control systems come into being. Networktransmission is widely used in various aspects of national defence and nationaleconomy such as military affairs, industry, medical science, traffic, aerospace and otherareas. However, in networked control systems, time delays, packet dropouts andmultirate sampling are unavoidable due to the limited carrying capacity,communication bandwidth of the network, the shelter and interference factors. Intraditional control systems, all the issues do not exist, which results that the estimationstrategies based on the conventional estimation theory can not be directly applied tothe networked control system. For the complicated structure and above special issuesof the networked control system, we must explore the estimation strategies that aresuitable to the network environment. Under this background, based on the projectiontheory and the optimal fusion algorithms in the linear minimum variance sense, thispaper is concerned with the above problems and investigates the modeling and thefusion estimation problem for networked control systems. The main contents andinnovations are as follows:For multi-sensor linear discrete time-varying stochastic systems with measurementpacket dropouts, the distributed and centralized information fusion estimatorsincluding filter, predictor and smoother are presented. The cross-covariance matricesbetween any two local estimation errors are derived. For linear time-invariant systems,a sufficient condition for the existence of the steady-state estimators is given. Theexistence for the steady-state solution of the cross-covariance matrices between anytwo local estimation errors is proven. Further, the centralized optimal and steady-state information fusion estimators are proposed for multi-sensor linear discretetime-varying stochastic systems with measurement and control packet dropouts.For multi-sensor linear discrete time-varying stochastic systems with randommultiplicative noises uncertainties and measurement packet dropouts, the local filtersfor single sensor system, distributed and centralized fusion estimators are designedrespectively. A sufficient condition for the existence of the steady-state estimators isgiven. Furthermore, the centralized optimal and steady-state information fusionestimators are obtained for multi-sensor discrete time-varying stochastic systems withrandom multiplicative noises uncertainties and uncertain observations. Compared tothe robust filter of the existing reference, our filter does not need to select artificiallythe coefficient matrices and variance matrices of the anti-interference noises. Also, theproposed filter is optimal in the linear minimum variance sense.The unified measurement model is established to describe the three stochasticphenomena by using three Bernoulli distributed random variables for linear discretetime-varying stochastic systems with multiple sensors subject to random sensor delays,multiple packet dropouts and uncertain observations. Based on the establishedmeasurement model, the linear optimal estimators are presented for a single sensorsystem by using the augmentation approach of the existing references. Compared tothe estimators of the existing literatures, the estimation accuracy is improved underlarge delay rate since the correlation of the augmented process noise at the same andadjacent moments is taken into account. However, the obtained filter is complicatedand unsuitable for real-time application due to the existence of the colored noises.Hence, a new augmentation approach is given. Based on it, the new local linearoptimal estimators are designed. The proposed estimators have the simpler forms andthe lower computational cost compared to the above estimators since a lowerdimension parameterized systems is constructed and the colored noise is avoided.Moreover, we generalize the results to the case of multi-sensor systems and the distributed and centralized information fusion estimators are given based on the simpleaugmentation approach.By establishing the state space model at the measurement sampling points of theith sensor, the local state filters are given at the state update points for multiratemulti-sensor linear discrete time stochastic systems, where the state has the highestupdate rate and different sensors have different lower measurement sampling rates. Inaddition, the distributed suboptimal fusion filter at the state update points is proposedby applying the suboptimal weighted fusion algorithm and covariance intersectionfusion algorithm. Compared to the state augmentation approach, the computationalcost is obviously reduced. For multirate multi-sensor linear discrete stochastic systemwith different packet dropout rates, the fusion estimation problem for multirate systemsis transformed into the equivalent fusion estimation problem for a single rate system byintroducing the pseudo measurement sequences. Further, based on the realization of themeasurement packet dropouts, the distributed optimal fusion filter is proposed. Thecross-covariance matrices between any two local estimation errors are derived.Compared to the non-state augmentation approach, the estimation accuracy issignificantly improved since all the measurement information is used at each stateupdate point.Throughout this paper, the MATLAB software is used to do the simulationresearch. The simulation studies show the feasibility and effectiveness of the proposedalgorithms by comparing to the existing results.