Error self-recurrent neural networks for control of fast time-varying nonlinear systems

Neural networks and neural network based sliding mode controller are proposed. The neural networks are error self-recurrent neural networks which use a recursive least squares method for fast online learning. The proposed neural networks converge considerably faster than the backpropagation algorithm and have advantages of being less affected by the poor initial weights and learning rate. The controller for a car suspension system is designed according to the sliding mode technique based on on the proposed neural networks. In order to adapt the sliding mode control method each frame distance between ground and vehicle body is estimated and the controller is designed according to estimated neural model.