Neural Network Adaptive Controller Design for Air-Ground Heterogeneous Formation

The air-ground heterogeneous multi-intelligent body formation cooperative control problem is addressed by proposing an adaptive controller heterogeneous multi-intelligent body formation cooperative control method based on radial basis function (RBF). The heterogeneous intelligent body formation, consisting mainly of unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV), is analyzed. Firstly, the equivalent transformations of the nonlinear dynamics models of UAVs and UGVs are derived, and a unified formation control model with acceleration as the control input is established. Secondly, the error model between the leader (UAV) and the follower (UGV) is established by adopting the leader-follower method. An adaptive controller with radial basis function (RBF) is designed to adjust the network weights online by sigmoid function and tanh function, ensuring that the tracking error of the formation converges to zero quickly. Finally, MATLAB simulations demonstrate that the joint formation of UAVs and unmanned vehicles can form the desired formation rapidly and achieve formation consistency.