Adaptive control of discrete-time nonlinear systems using recurrent neural networks

A learning and adaptive control scheme for a general class of unknown MIMO discrete-time nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented. A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm. Based on the dynamic neural model, an extension of the concept of the input-output linearisation of discrete-time nonlinear systems is used to synthesise a control technique for model reference control purposes. A dynamic learning control architecture is developed with simultaneous online identification and control. The potentials of the proposed methods are demonstrated by simulation studies.