First results on the application of the Fynesse control architecture
A system is presented that learns to control a priori unknown nonlinear dynamical systems and enables the user to interpret, examine and correct the control strategy in every stage of learning. The application of dynamic programming methods to train a neural network for such control purposes was already quite successful. In order to shorten the long training process the system should allow the incorporation of a priori knowledge about the control strategy in terms of classical linear controllers, fuzzy control rules etc. As a solution, we propose the hybrid control architecture Fynesse: the control strategy is represented by a fuzzy relation that can be interpreted and contain a priori knowledge, whereas the more complex part of learning is solved by a neural network as before. This article reports results on the application of the Fynesse controller to a chemical plant.