Identification of plant inverse dynamics using neural networks

This article investigates the approximation of the inverse dynamics of unknown plants using a new type of recurrent backpropagation neural network. The network has two input elements when modelling a single-output plant, one to receive the plant output and the other, an error input to compensate for modelling uncertainties. The network has feedback connections from its output, hidden, and input layers to its “state” layer and self-connections within the “state” layer. The essential point of the proposed approach is to make use of the direct inverse learning scheme to achieve simple and accurate inverse system identification even in the presence of noise. This approach can easily be extended to the area of on-line adaptive control which is briefly introduced. Simulation results are given to illustrate the usefulness of the method for the simpler case of controlling time-invariant plants.