Adaptive Nonlinear Controller Design using Input Normalized Neural Networks

H. Lee, H. Bang, and C.-H. Hong (Korea)


Adaptive control, input normalized neural network, nonlin ear system.


An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The stability analysis of the closed loop system with the neural network so far have been rather complicated and difficult to understand, and sometimes un necessary assumptions are involved. As a new result, the input normalization technique of the neural networks en hanced the control frameworks and the unnecessary as sumptions for stability analysis are avoided by using the input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov sta bility theory. The proposed control law is demonstrated by the numerical simulations.

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