A control of MR damper using feed-forward neural network without force sensor

This paper proposes an inverse model feed-forward neural network (FNN) that does not require any force sensor to control magneto-rheological (MR) dampers. The system is designed by using time-histories of displacement and velocity in combination with desired force to predict voltage input to control MR damper. Unlike conventional MR damper controller, the proposed system does not require force inputs, providing more economical control system. Additional dead zone filter is also introduced here to reduce errors at near zero state of velocity. Using training and validation data sets generated by a modified Bouc-Wen model, the results of the proposed system with and without dead zone filter are also presented.