Force-sensorless damping control for damping systems using MR dampers

Recently, magneto-rheological (MR) fluid dampers are semi-active control devices that have begun to receive more attention. This paper presents a novel force-sensorless control method for a damping system using a MR damper. The control method is constructed by two models designed based on fuzzy-neural technique, a nonlinear black-box model (BBM) and an inverse black-box model (IBBM). By employing a fuzzy mapping system optimized by neural network technique including back-propagation and gradient descent method, the BBM model can estimate directly the damper characteristics. The inverse model, IBBM with self-learning ability, was then derived based on the BBM with the optimized parameters and neural network technique. Consequently, the designed BBM and IBBM models can be used as a `virtual´ force sensor and an adaptive force controller, respectively, to perform a closed-loop feedback control for any damping system which uses the corresponding MR fluid damper. Effectiveness of the proposed models for modeling as well as the force-sensorless damping control technique has been clearly verified through simulations and real-time experiments on two vibrating systems employing the same MR fluid damper series.