Indirect inverse adaptive control based on neural networks using dynamic back propagation for nonlinear dynamic systems

In this paper, Dynamic Back Propagation (DBP) is reinvestigated in the context of indirect inverse control of unknown nonlinear dynamic systems using Artificial Neural Networks (ANNs). The overall control scheme includes two networks. The first is used as an approximating model of the unknown nonlinear dynamic behavior of the plant while the second is the controller. The trajectory error is backpropagated through the neural model using DBP algorithm to obtain the control input error. The controller, which is a recurrent ANN, is then adapted also using DBP algorithm. Simulation results obtained with both Standard Back Propagation (SBP) and DBP algorithms for a fourth order nonlinear system clearly show some advantage of DBP.