Computationally efficient neural predictive control based on a feedforward architecture

A new strategy for integrating system identification and predictive control is proposed. A novel feedforward neural-network architecture is developed to model the system. The network structure is designed so that the nonlinearity can be mapped onto a linear time-varying term. The linear time-varying model is augmented with a Kalman filter to provide disturbance rejection and compensation for model uncertainty. The structure of the model developed lends itself naturally to a neural predictive control formulation. The computational requirements of this strategy are significantly lower than those using the nonlinear neural network, with comparable control performance, as illustrated on a challenging nonlinear chemical reactor and a multivariable process, each with both nonminimum and minimum phase behavior.