Nonlinear transition controller design using neural networks tested on a polymerization reactor

We propose a complete scheme for a nonlinear controller design to steer the process between different operating points. The main features of the controller are that it includes a state observer and dynamic state feedback. The process disturbances are considered in the design as well. The controller is designed for a wide range of nonlinear process dynamics where the performance of a linear controller dramatically deteriorates. The state observer includes a nonlinear state space simulation model of the process being estimated from measured input/output data. The state vector is partitioned into two parts which reflect our a priori knowledge about the process dynamics. The design algorithm is verified using a simulation model of a fluidized bed polymerization reactor. No assumptions are made in the design that would be violated in application of the controller for a real process