Adaptive-neuron-based control for the yaw tracking of unmanned helicopter

Abstract

The yaw dynamics of helicopter involves input nonlinearity, time-varying parameters and the couplings between main and tail rotor. With respect to such complicated dynamics, the normal proportional integral differential (PID) control is difficult to realize good tracking performance while maintaining stability and robustness simultaneously. A valid control is proposed by integrating adaptive neuron into sliding mode control. The stability of the proposed method can beguaranteed by the sliding mode condition, and the time-varying parameters and other uncertainties in the helicopter dynamics can be rejected by the adaptive neuron to achieve small tracking error. The stability of the proposed algorithm is proved and the simulations results with respect to the dynamics identified from a real helicopter-on-arm testbed are presented. The simulation results are further compared with those obtained by normal PID control to demonstrate the improvements of the proposed algorithm.