Neural identification and indirect control of a nonlinear mechanical oscilatory plant

A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of nonlinear oscillatory mechanical plant. The first MRTNN module identified the exponential part of the unknown plant and the second one - the oscillatory part of the plant. The plant has been controlled by an adaptive sliding mode control system with integral term. The RTNN controller used the estimated parameters and states to suppress the plant oscillations and the static plant output control error is reduced by an I-term added to the control.