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Adaptive control of discrete-time nonlinear systems using recurrent neural networks
1994
IEE Proceedings - Control Theory and Applications
A learning and adaptive control scheme for a general class of unknown MIMO discretetime 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
doi:10.1049/ip-cta:19949976
fatcat:nxismquxvjae3klqjgi7uogeti