A nonlinear observer for a general class of single-output nonlinear systems is proposed based on a generalized dynamic recurrent neural network (DRNN). The neural network (NN) weights in the observer are tuned online, with no off-line learning phase required. The observer stability and boundness of the state estimates and NN weights are proven. No exact knowledge of the nonlinear function in the observed system is required. Furthermore, no linearity with respect to the unknown system parameters is assumed. The proposed DRNN observer can be considered as a universal and reusable nonlinear observer because the same observer can be applied to any system in the class of nonlinear systems.
Proceedings of the 35th IEEE Decision and Control
Chaos, Erbium, Nonlinear systems
Abdallah, Chaouki T.; Young H. Kim; and Frank L. Lewis. "Nonlinear observer design using dynamic recurrent neural networks." Proceedings of the 35th IEEE Decision and Control (1996): 949-954. doi:10.1109/CDC.1996.574590.