UAV formation control based on distributed Kalman model predictive control algorithm

To address the perturbation of formation of multiple unmanned aerial vehicles (UAVs) subject to external disturbances, an algorithm of distributed Kalman model predictive control is proposed in this paper to improve the accuracy of maintaining a formation in flight. A UAV two-order discrete-time system model was built before devising a Kalman prediction model based on the standard prediction model. The desired formation configuration and neighbor Kalman optimal state estimation were conducted to determine the reference state of UAVs. While taking into account the formation tracking error and input stability, a logarithmic barrier function was introduced in the design of the overall cost function to ensure flight safety. Meanwhile, information was exchanged with neighbors with the directed and time-invariant communication topological structure. With the Lyapunov stability theorem, sufficient conditions were defined for the asymptotic stability of the formation system. Simulation results revealed that the algorithm could effectively suppress the perturbation in the formation of UAVs arising from external disturbances, allowing the formation to cope with the conflicts between individual UAVs.