ESO and RBFNN-based Adaptive Prescribed Performance Control for Morphing Aircraft

In this paper, a radial basis function neural network (RBFNN) and extended state observer (ESO) based adaptive control method is proposed to achieve velocity and altitude tracking control of the morphing aircraft. Different from the existing results in which the nominal dynamic model of the morphing aircraft is used to design the ESO, the RBFNN is adopted in the design of the ESO and the coupling estimation error is also considered in this paper. Besides, to improve the performance of the altitude tracking control of the morphing aircraft, the prescribed performance control (PPC) technique is introduced in the controller design. Simulation results show the stability and effectiveness of the proposed method.