Neural networks based robust forwarding control of nonlinear systems

Abstract

In this work, an attractive alternative of robust backstepping approach for a reference tracking problem is developed. We extend robust forwarding design strategy to a class of nonlinear systems with unknown functions. In particular, using radial basis function neural networks (RBFNNs), uncertainties and derivatives of mappings in the recursive computation are estimated with any arbitrarily small approximation error. Two salient features include: first, nonlinear model is not required to be linearly parametrizable; second, at each step, the problem of “explosion of terms” is tackled by applying neural networks instead of filters. The boundedness of all signals is proven and the tracking error is ensured to converge to a small neighborhood of zero. Simulation results and comparisons with robust backstepping are given to demonstrate tracking performances through a case study on flexible joint robots.