Neural network emulation of inverse dynamics for a magnetorheological damper

The dynamic behavior of a magnetorheological (MR) damper is well portrayed using a Bouc-Wen hysteresis model. This model estimates damper forces based on the inputs of displacement, velocity, and voltage. In some control applications, it is necessary to command the damper so that it produces desirable control forces calculated based on some optimal control algorithms. In such cases, it is beneficial to develop an inverse dynamic model that estimates the required voltage to be input to the damper so that a desirable damper force can be produced. In this study, we explore such a possibility via the neural network (NN) technique. Recurrent NN models will be constructed to emulate the inverse dynamics of the MR damper. To illustrate the use of these NN models, two control applications will be studied: one is the optimal prediction control of a single-degree-of-freedom system and the other is the linear quadratic regulator control of a multiple-degree-of-freedom system. Numerical results indicate that, using the recurrent NNT models, the MR damper force can be commanded to follow closely the desirable optimal control force