固定翼无人机编队分布式抗干扰复合学习协同控制OA

For fixed-wing unmanned aerial vehicle(UAV)formation flight systems subjected to internal uncertainties and time-varying external dynamic disturbances,a distributed trajectory tracking control protocol problem is investigated under a directed communication topology in this work.Firstly,for overcoming the strong nonlinearity and strong coupling characteristics of fixed-wing UAVs,the feedback linearization is employed to equivalently transform the fixed-wing UAV nonlinear model into an affine nonlinear system with strict-feedback form.Secondly,radial basis function neural net-works are utilized to online approximate internal uncertainties in the fixed-wing UAV formation flight system,and nonlinear disturbance observers are designed to online estimate the compounded distur-bances caused by external dynamic disturbances and neural network approximation errors.On this basis,state predictors are constructed based on the outputs of the neural networks and nonlinear dis-turbance observers,and the prediction errors of state predictors are taken as decision variables for the online updating of the neural networks and disturbance observers.It can further overcome the prob-lems of poor interpretability and low transparency arising from the black-box nature of neural network approximation.Moreover,a distributed anti-disturbance composite learning trajectory tracking coopera-tive control protocols is developed for the fixed-wing UAV formation flight system based on dynamic surface control and the framework of multi-agent consensus theory.The semi-globally uniformly ulti-mately bounded stability of the closed-loop formation system is rigorously proved based on Lyapunov stability theory.Finally,simulation experiments verify the feasibility and effectiveness of the proposed control protocol.The designed distributed anti-disturbance composite learning trajectory tracking coop-erative control protocols enable formation keeping of fixed-wing UAVs while achieving higher control accuracy and more continuous,smoother control signals than traditional backstepping control protocols.