Nonlinear Identification of a Magneto-Rheological Damper Based on Dynamic Neural Networks

Semi-active control of dynamic response of civil structures with magneto-rheological (MR) fluid dampers has emerged as a novel revolutionary technology in recent years for designing smart structures. A small-scale MR damper model with the valve mode mechanism has been examined in this research using dynamic recurrent neural network modeling approach to reproduce its hysteretic nonlinear behavior. Modified Bouc-Wen model based on nonlinear differential equations has not only been employed as the reference model to provide a comprehensive training data for the neural network but also for comparison purposes. A novel frequency and amplitude varying displacement input signal (modulated chirp signal) associated with a random supply voltage has been introduced for persistent excitation of the damper in such a way to cover almost all of its operating conditions. Finally a series of validation tests were conducted on the proposed model which proved the appropriate performance of the model in terms of accuracy and capability for realization.