Robust Adaptive Neural Network Control for Strict

Authors: Yansheng Yang Tieshan Li Xiaofeng Wang

Publish Date: 2006/5/28

Volume: , Issue: , Pages: 888-897

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Abstract

A novel robust adaptive neural network control RANNC is proposed for a class of strictfeedback nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities The synthesis of RANNC is developed by use of the inputtostate stability ISS the backstepping technique and generalized small gain approach The key feature of RANNC is that the order of its dynamic compensator is only identical to the order n of controlled system such that it can reduce the computation load and makes particularly suitable for parallel processing In addition the possible controller singularity problem can be removed elegantly Finally simulation results are presented to validate the effectiveness of the RANNC algorithm

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