Adaptive Neural Network Control for Nonlinear Systems with Unknown Hysteresis via Hinfty Approaches

In this paper, in order to design control scheme to mitigate the effects of unknown hysteresis, a class of novel hysteresis models are proposed. We superpose a finite of many different deadband width backlash models, which represented as a dynamics to mimic hysteresis in actuator. With the model proposed, a single hidden layer neural network (NN-based) adaptive control scheme for nonlinear systems with unknown hysteresis nonlinearity is developed. The control scheme adopts the design method of pseudo-control. For the nonlinear dynamic systems, with time-varying external disturbance and strong nonlinearity and large uncertainty of unknown hysteresis, which output is not available, we adopt H^infin optimal control techniques. Our result indicates that arbitrarily small attenuation level can be achieved via the proposed adaptive neural networks control algorithm if a weighting factor of control variable is adequately chosen. The effectiveness of the proposed control scheme is illustrated through simulation