Modelling flexible robot dynamics using discrete-time dynamic recurrent neural networks

Abstract The identification of a general class of multi-input multi-output (MIMO) discrete-time nonlinear systems expressed in state space form is studied in this paper using dynamic recurrent neural networks (DRNNs). A discrete-time DRNN, which is represented by a set of parameterized nonlinear difference equations and has a universal approximation capability, is proposed for modelling unknown discrete-time nonlinear systems. A dynamic back propagation learning algorithm which carries out the modelling task using the input-output data is discussed. The proposed scheme is used for modelling the nonlinear dynamics of a single-link robot with a flexible joint.