Time-varying group formation tracking control for multi-agent systems using distributed multi-sensor multi-target filtering with intermittent observations

Abstract

Time-varying group formation tracking control problems for multi-agent systems are investigated based on distributed multi-sensor multi-target filtering with intermittent observations. First, in order to estimate the states of multiple targets under the phenomenon of intermittent observations accurately, a distributed multi-sensor multi-target filtering algorithm is proposed based on cubature Kalman filtering. Second, a time-varying group formation tracking protocol is designed for multi-agent systems by using the state estimations obtained from the filtering algorithm and the neighboring interaction. The protocol enables multi-agent systems to form time-varying subformations and track multiple targets in the same subgroups, respectively. Third, the boundedness of the error covariance matrices is proved under the condition that the observation probability is higher than the minimum threshold. Then the estimation errors of the filtering algorithm are proved to be stochastically bounded by introducing a stochastic process. Furthermore, the boundedness of the group formation tracking errors is proved. Finally, a numerical example is used to verify the performance of the proposed algorithm and protocol.