Robust adaptive control of linearizable nonlinear single input systems

Abstract

In this paper a nonlinear robust adaptive control algorithm is designed and analyzed for a class of single-input nonlinear systems with unknown nonlinearities. The controller guarantees closed loop semiglobal stability and convergence of the tracking error to a small residual set. The region of attraction for semiglobal stability depends on the number of nodes and weights in the single layer neural network used to estimate the unknown plant nonlinearities. The size of the residual set depends on design parameters and can be calculated apriori. One example is used to demonstrate the performance and properties of the proposed scheme. Copyright © 2002 IFAC.