Adaptive nonlinear control system design for helicopter robust command augmentation

In this paper, the design and evaluation of a helicopter trajectory tracking controller are presented. The control algorithm is implemented using the feedback linearization technique and the two time-scale separation architecture. In addition, an on-line adaptive architecture that employs a Sigma-Pi neural network, which is simple in its structure so that it is easily applicable to on-line adaptation, compensating the model inversion error caused by the deficiency of full knowledge of helicopter dynamics is applied to augment the attitude control system. Trajectory tracking performance of the control system is evaluated using a generic helicopter model simulation program. It is shown that the on-line neural network in an adaptive control architecture is very effective in dealing with the performance degradation problem of the trajectory tracking control caused by insufficient information of dynamics.