Adaptive feedforward control for dynamically substructured systems based on neural network compensation

The potential applications of dynamically substructured systems (DSS) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, a feedforward adaptive controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensation strategy.