Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity

Abstract

In this paper, an adaptive neural tracking controller with prescribed performance is developed for near space vehicles (NSVs) with unknown parametric uncertainties, external disturbances and input nonlinearities including input saturation and dead-zone. By placing the right inverse of the dead-zone before the input nonlinearity, the serial input nonlinearity consisting of input saturation and dead-zone can be regarded as an equivalent input saturation which is solved by a common constrained control method. To guarantee the prescribed performance of the closed-loop system including the transient and steady-state performance, the constrained prescribed performance is changed into unconstrained transformation error using error transformed technology. Meanwhile, the radial basis function neural networks (RBFNNs) are adopted to tackle the system uncertainties. Then, using the auxiliary system and backstepping technology, an adaptive tracking control scheme is proposed, and all the closed-loop system signals are proved to be bounded. Finally, extensive simulations are given for the attitude motion of the NSV to illustrate the effectiveness of the developed adaptive neural control scheme.