Adaptive feedforward control of cyclic movements using artificial neural networks

An adaptive neural network control system has been designed for the purpose of controlling cyclic movements of nonlinear dynamic systems with input time delays (as found in functional neuromuscular stimulation). The adaptive feedforward (FF) controller is implemented as a two-stage neural network. The first stage, the pattern generator (PG), generates a cyclic pattern of activity. The signals from the PG are adaptively filtered by the second stage, the pattern shaper (PS). This stage uses modifications to standard artificial neural network learning algorithms to adapt its filter properties. The control system is evaluated in computer simulation on a musculoskeletal model which consists of two muscles acting on a swinging pendulum. The control system provides automated customization of the FF controller parameters for a given musculoskeletal system as well as online adaptation of the FF controller parameters to account for changes in the musculoskeletal system.<>