Data-driven prediction-control system modeling for magnetorheological damping force

As a new type of intelligent damper, the magnetorheological damper has been widely used in robot, automobile NVH, and intelligent structure. However, for the intelligent response control from the structural excitation, it is the challenge to realize the intelligent control of the magnetorheological damping system. In this paper, the prediction-control mechanism of the magnetorheological damping system is modeled by a data-driven method, such as neural network and classification algorithm. The NARX (Nonlinear autoregressive with external input) neural network is used to predict the desired damping force required for the structural system in the forward direction, and the decision tree classification algorithm is used to reversely-control the desired current of the magnetorheological damping system in instant response to the structural system’s damping force requirement. The analysis results show that the prediction-control method is feasible to realize the intelligent control of the damper based on the state data of the damped system, which provides a new idea for the intelligent control of the magnetorheological damper system.