Dynamic neural network control of nonlinear-time varying systems: stability analysis, optimal network structure, and on-line adaptation

In this paper, dynamic neural network control (DNNC) is presented as a control strategy which uses a neural network to model the process and then applies the mathematical inversion of the process model to design the controller. DNNC falls into the large class of model predictive controllers, many of which are now widely used in industry. The objectives of this paper are: to study the stability properties of DNNC; and to study the on-line adaptation properties of DNNC. In this study four basic assumptions are made. The process is modeled with continuous time functions. If the process is nonstationary, it is also necessary to assure that changes will be continuous and smooth over small intervals of time, i.e. that process parameters change in a continuous manner. The time between model adaptation is small relative to time scale of parameter changes. Process parameters will not change at once, but only a small subset of them