Neural observer by coordinate transformation

Nonlinear control affine systems with maximum relative degree and a class of nonlinear differential equations can be transformed into a state representation known as the normal form. Based on the normal form an observer is designed using neural networks as approximation of nonlinear terms in the normal form and the observer gain is determined with a previous result in a triangular form.