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Neural controllers for nonlinear state feedback L2-gain control

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1 Author(s)
Ahmed, M.S. ; DaimlerChrysler Corp., Auburn Hill, MI, USA

Design of an L2-gain disturbance rejection neural controller for nonlinear systems is presented. The control input is generated from a radial basis network, which is trained offline such that a computed partial derivative of the network output satisfies a Hamilton-Jacobi inequality. Once the network is successfully trained for a given manifold in the state space, the closed-loop system ensures a finite gain between the system disturbance and the system input-output as long as the system states remain within the state manifold. The proposed method may also be applied to obtain an H controller

Published in:

Control Theory and Applications, IEE Proceedings -  (Volume:147 ,  Issue: 3 )