Finite-time trajectory tracking control of quadrotor UAV via adaptive RBF neural network with lumped uncertainties

Abstract

The trajectory tracking control of the quadrotor with model uncertainty and time-varying interference is studied. The RBF neural network is combined with the global fast terminal sliding mode (GFTSM) control method to converge tracking errors in finite time. To ensure the stability of the system, an adaptive law is designed to adjust the weight of the neural network by the Lyapunov method. The overall novelty of this paper is threefold, 1) Owing to the use of a global fast sliding mode surface, the proposed controller has no problem with slow convergence near the equilibrium point inherently existing in the terminal sliding mode control. 2) Benefiting from the novel equivalent control computation mechanism, the external disturbances and the upper bound of the disturbance are estimated by the proposed controller, and the unexpected chattering phenomenon is significantly attenuated. 3) The stability and finite-time convergence of the overall closed-loop system are strictly proven. The simulation results indicated that the proposed method achieves faster response speed and smoother control effect than traditional GFTSM.