Neural-hybrid control of systems with sandwiched dead-zones

An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for 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 the 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 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