Iterative neural network adaptive robust control of a maglev planar motor with uncertainty compensation ability

Abstract

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM.