Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

Distribution network state estimation based on attention-enhanced recurrent neural network pseudo-measurement modeling

Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.

On-orbit target tracking and inspection by satellite formation

A new type of estimator is developed for the satellite formation to track and inspect on-orbit targets. The follower satellite in the formation works without relative sensors, and its target pointing commands are derived based on relative orbital dynamics. The centralized estimator based on truth measurement is designed, however, this estimator is proved unstable because of the lack of necessary measurement information. After that, an alternative estimator based on pseudo measurement is designed, and its observability and controllability are analyzed to qualitatively evaluate the convergence performance. Finally, an on-orbit target inspection scenario is numerically simulated to verify the performance of the estimator based on pseudo measurement.

Existence of μ-Pseudo Almost Periodic Solutions to Some Classes of Nonautonomous Partial Evolution Equations

In this paper, we use a recent works [5], where the authors provide a new ap-proach for pseudo almost periodic solution under the measure theory, under Acquistpace-Terreni conditions, we make extensive use of interpolation spaces and exponential di-chotomy techniques to obtain the existence of μ-pseudo almost periodic solutions tosome classes of nonautonomous partial evolution equations.