An investigation into the use of neural networks for the semi-active control of a magnetorheologically damped vehicle suspension

Abstract Neural networks are highly useful for the modelling and control of magnetorheological (MR) dampers. A damper controller based on a recurrent neural network (RNN) of the inverse dynamics of an MR damper potentially offers significant advantages over conventional controllers in terms of reliability and cost through the minimal use of sensors. This paper introduces a neural-network-based MR damper controller for use in conjunction with the system controller of a semi-active vehicle suspension. A mathematical model of a semi-active quarter-vehicle suspension using an MR damper is derived. Control performance criteria are evaluated in the time and frequency domains in order to quantify the suspension effectiveness under bump and random road disturbance. Studies using the modified Bouc—Wen model for the MR damper as well as an actual damper fitted in a hardware-in-the-loop simulation (HILS) both showed that the inverse RNN damper controller potentially offers a significantly superior ride comfort and vehicle stability over a conventional MR damper controller based on continuous-state control. The neural network controller produces a smoother and lower input voltage to the MR damper coil, ensuring extended damper life and lower power requirement respectively. Further studies performed using an RNN model of the forward dynamics of the MR damper showed that it is a reliable substitute for HILS for validating multi-damper control applications.