System identification and state estimation for magnetorheological dampers

The magnetorheological (MR) damper is a promising device enabling control in suspension systems. The MR damper has a variable and non-linear damping force which depends on the input current, and where the nonlinearity stems from the MR damper’s hysteric behaviours. The force can be modelled using models such as the Bingham model and the Bouc-Wen model, each varying in complexity and accuracy. There are a variety of methods in the literature that are used to identify parameters, such as non-deterministic approaches like machine learning (ML) algorithms, or deterministic approaches like Kalman estimation strategies. This paper uses a combination of ML and the unscented Kalman filter (UKF) for parameter and state estimation on the popular Bouc-Wen model in a forced response dynamic setup. In addition, fine-tuning of individual parameters using an augmented state vector approach is explored.