Control method of dynamic inversion with neural network used for MAV

The accurate mathematic model of micro air vehicle (MAV) can hardly be gotten. The use of control method of pure dynamic inversion is limited. The traditional control methods, such as proportion-integral-derivative (PID) etc., cannot satisfy all the requirements. Aiming at this condition, a new control method of dynamic inversion with neural network was studied and some models of neural network used in dynamic inversion were analyzed. Multilayer feed-forward neural network with integrators was chosen to train the dynamic inversion model and the according compensated error of the model. The simulator was established by combining NNCTRL20 and NNSYSID20 in MATLAB. Rudder and elevator were used to control the swerve; elevator was differential and could associate with the rudder. A new flight control system of the MAV was composed to two PID controllers, an approximate dynamic inversion block, and an adaptive on-line neural-network compensator. The system was driven by the outputs of a reference model block. Simulation results demonstrate that the control method has strong robustness, stability and capability of following commands. Compared with PID, this control method adapts more to attitudes control of the MAV.