A recurrent neural network based MPC for a hybrid neuroprosthesis system

Control input sequence in a hybrid neuroprosthesis that combines functional electrical stimulation (FES) and an electric motor can be optimized by a model based optimization method, like model predictive control (MPC). However, because the human muscle model is highly nonlinear, time-varying, and contains unmeasurable state variables, it is often difficult to identify the model. Therefore, a three-layer recurrence neural network (RNN) is developed in this paper, in which backpropagation through time (BPTT) is used as training technique and the internal states are used to represent the unmeasurable states. This structure shows the potential to approximate the model of the hybrid neuroprosthesis system. After the NN model is obtained, an adaptive model predictive control is used to simulate regulation and tracking tasks to test the performance of the NN training and the MPC method.