Neural‐hybrid control of systems with sandwiched dead‐zones

The control of systems that have sandwiched nonsmooth nonlinearities, such as a dead-zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead-zone. This neural-hybrid controller consists of an inner loop discrete-time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead-zone, and an outer-loop continuous-time feedback control law for achieving desired output tracking. The dead-zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead-zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead-zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural-hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.

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