**5. Conclusions**

In this chapter, physical phenomena of a MR fluid damper have been carefully investigated through both experimental data and modeling methodologies. Furthermore, a novel forcesensorless control method was proposed and successfully applied to the damping system using the MR fluid damper and without using any mechanical force sensor. The designed force control system is based on the optimized BBM model, functioning as the virtual force sensor, and the IBBM model, functioning as the self-learning force controller.

The two test rigs using the MR fluid damper have been fabricated in order to design the models as well as to evaluate the proposed controller. The BBM model is built as the simple direct modeling method and optimized by using neural network technique. The IBBM controller is the direct inverse model of the optimized BBM. In addition, the IBBM has the online self-learning ability by using neural network, consequently, improving the system performance. As the result, the optimized BBM and IBBM models have a strong ability to be applied to damping systems using the MR fluid damper as a virtual force sensor and a simple direct controller, respectively.

The modeling as well as control validation process has been done by both the simulations and real-time experiments. Based on the simulation and experimental results, it is clear that the optimized BBM can predict the force-displacement behavior of the MR damper with high precision while the IBBM can control the damping system to follow the desired damping force well. As a result, the proposed force-sensorless damping control methodology can become an effective and economical solution for practical MR fluid damper applications in the near future.
