Semi-active control system for magneto-rheological damper based on the identification model with fuzzy neural network

Semi-active suspensions that dissipate energy through controllable dampers have been used in trains, tractors and off-road vehicles in the last decades. Magneto-rheological (MR) fluids have been widely applied as a new material in the field of vibration control. Magneto-rheological (MR) damper is an extremely ideal semi-active control device compared with traditional semi-active damping control device for its superiorities of rapid response (in millisecond), far less response time than sampling time, and almost no time lag caused by the control devices. However, its complicated dynamic hysteresis characteristics vary with the currents imposed on it, resulting in the difficulty in establishing the mathematical model which can truly describe its dynamic behavior. Besides, an effective and precision mathematical model can be of help for constructing the semi-active control law. In this paper, the system identification method based on the theory of fuzzy neural network (FNN) is employed to identify the mathematical model that can accurately reflect the dynamic hysteresis characteristics of magneto-rheological (MR) damper. Under the semi-active control law, the structure on the damper can be stabilized at the fastest rate with an optimal current calculated by the identified mathematical model imposed on magneto-rheological (MR) damper. Consequently, vibration reduction can be effectively realized.