Neural Network Based Control of Mode-Switch Processes

In ‘mode-switch processes’, the dynamics are fairly the same in one mode of operation, but are truly different in another one. For such processes, this paper presents a ‘supervisory system’ which is based on the storage and retrieval of controllers designed at various modes. Modes are recognized by a neural network based decision-rule which rewards and penalizes the active controller on the basis of its closed-loop performance. This approach has been tested in a chemical reactor simulation where the objective is to reject disturbances. However, the concept can be applied to other processes as well.