Fixed-Time Adaptive Neural Tracking Control for a Helicopter-Like Twin Rotor MIMO System

In this paper, a fixed-time adaptive neural control (FTANC) for a helicopter-like Twin Rotor MIMO System (TRMS) is presented. The proposed controller has been developed to achieve finite-time convergence of the system dynamics independently of the initial conditions. The control of the TRMS system is a challenging problem due to the significant non-linearities and the cross-coupling between the main and tail rotors. In this study, we proposed an adaptive radial basis function neural networks (RBFNNs) to estimate all unknown nonlinear functions and disturbances. The RBFNN is combined with the backstepping technique to guarantee the trajectory tracking and the overall closed-loop stability. The effectiveness of the proposed control strategy is demonstrated through simulation tests in Matlab/Simulink software environment.