In this paper,the flight formation control problem of a group of quadrotor unmanned aerial vehicles(UAVs) with parametric uncertainties and external disturbances is studied.Unitquaternions are used to represent the attitudes of the quadrotor UAVs.Separating the model into a translational subsystem and a rotational subsystem,an intermediary control input is introduced to track a desired velocity and extract desired orientations.Then considering the internal parametric uncertainties and external disturbances of the quadrotor UAVs,the priori-bounded intermediary adaptive control input is designed for velocity tracking and formation keeping,by which the bounded control thrust and the desired orientation can be extracted.Thereafter,an adaptive control torque input is designed for the rotational subsystem to track the desired orientation.With the proposed control scheme,the desired velocity is tracked and a desired formation shape is built up.Global stability of the closed-loop system is proven via Lyapunov-based stability analysis.Numerical simulation results are presented to illustrate the effectiveness of the proposed control scheme.
This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
