Set-Membership Filtering Subject to Impulsive Measurement Outliers:A Recursive Algorithm

This paper is concerned with the set-membership filtering problem for a class of linear time-varying systems with norm-bounded noises and impulsive measurement outliers.A new representation is proposed to model the measurement outlier by an impulsive signal whose minimum interval length(i.e.,the minimum duration between two adjacent impulsive signals)and minimum norm(i.e.,the minimum of the norms of all impulsive signals)are larger than certain thresholds that are adjustable according to engineering practice.In order to guarantee satisfactory filtering performance,a so-called parameter-dependent set-membership filter is put forward that is capable of generating a time-varying ellipsoidal region containing the true system state.First,a novel outlier detection strategy is developed,based on a dedicatedly constructed input-output model,to examine whether the received measurement is corrupted by an outlier.Then,through the outcome of the outlier detection,the gain matrix of the desired filter and the corresponding ellipsoidal region are calculated by solving two recursive difference equations.Furthermore,the ultimate boundedness issue on the time-varying ellipsoidal region is thoroughly investigated.Finally,a simulation example is provided to demonstrate the effectiveness of our proposed parameter-dependent set-membership filtering strategy.

Flocking for swarm systems with fixed topology in a changing environment

This paper is mainly devoted to the flocking of a class of swarm with fixed topology in a changing environment. Firstly, the controller for each agent is proposed by employing the error terms between the state of the agent and the average state of its neighbors. Secondly, a sufficient condition for the swarm to achieve flocking is presented under assumptions that the gradient of the environment is bounded and the initial position graph is connected. Thirdly, as the environment is a plane, it is further proved that the velocity of each agent finally converges to the velocity of the swarm center although not one agent knows where the center of the group is. Finally, numerical examples are included to illustrate the obtained results.