An adaptive controller for a class of nonlinear plants based on neural networks and convex parameterization

The problem of control of a class of nonlinear plants has been addressed by using neural networks together with sliding mode control. We revisit this problem in this paper and propose an adaptive controller based on convex/concave parameterization for adjusting the weights that occur nonlinearly. By using the algorithms that have been proposed previously for adaptive control for nonlinearly parameterized problems, it is shown that global boundedness of all signals can be achieved together with a reduced tracking error. Simulation studies of an aircraft landing problem validate the proposed controller and that it contributes to improved learning of the underlying nonlinearity.