Recurrent-Neural-Network-Based Rate-Dependent Hysteresis Modeling and Feedforward Torque Control of the Magnetorheological Clutch
Guangzeng Chen, Yunjiang Lou
Abstract
The complicated rate-dependent hysteresis due to the input current and rotation speed of the magnetorheological clutch (MRC) greatly hinders its applications in robotics. In this article, it is first proved that the Preisach model is a special diagonal recurrent neural network (dRNN) with a binary step activation function. Motivated by this, the condition and the internal knowledge of the classical dRNN in modeling the rate-dependent hysteresis are investigated and validated. To guarantee that such a condition holds in dRNN training, a new loss function is proposed. The coupled rate-dependent hysteresis of an MRC prototype is, then, precisely modeled by the dRNN with the current input and speed input. By employing the trained dRNN model, the MRC torque is controlled using the feedforward scheme with no external torque sensors. Taking the advantage of being free-of-parameter tuning for the classical inverse multiplicative structure control (IMSC), a dRNN-based IMSC is proposed by finding the best feasible neuron and solving a nonlinear function. Various experiments validate much better control accuracy and higher bandwidth of the IMSC over the model-based proportional–integral–derivative control.