Neural network-based adaptive tracking control for nonlinearly parameterized systems with unknown input nonlinearities

This paper presents tracking control problem of the unmatched uncertain nonlinearly parameterized systems (NLP-systems) with unknown input nonlinearities. Two kinds of nonlinearities existing in the control input are discussed, which are non-symmetric dead-zone input and continuous nonlinearly input. The smooth controller is proposed in either of these two cases by effectively integrating adaptive backstepping technique and neural networks. Some assumptions, in which the parameters with respect to the input nonlinearities are available in advance in previous works, are removed by adaptive strategy. The researches also take the arbitrary unmatched uncertainties and nonlinear parameterization into account without imposing any condition on the system. It is shown that the closed-loop tracking error converges to a small neighborhood of zero. Finally, numerical examples are initially bench tested to show the effectiveness of the proposed results.