Consensus learning based coordinated formation control of multiple UAVs

This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles (UAVs) formation control within a cooperative consensus framework. The proposed strategy incorporates a reference command generation layer, which derives UAV attitude commands based on formation requirements, and a tracking control layer to ensure accurate execution. Collaborative variables, including trajectory position and flight speed, are defined using a three-dimensional track particle and autopilot model, enabling the development of a consensus-based formation control law. Desired attitude angles are computed through altitude-hold and coordinated-turn strategies. A sliding surface is designed based on reference models derived from flight quality metrics, while an adaptive controller compensates for aerodynamic model uncertainties. To enhance learning capabilities, a prediction error mechanism based on a series–parallel estimation model is introduced, enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system. This facilitates the design of a distributed composite learning law. Lyapunov stability analysis confirms the local exponential stability of the tracking error. The simulations of a twelve-UAV formation, along with comparative analysis of two algorithms, demonstrate the system's capability for formation maintenance and high-precision tracking control.