Hybrid neuro-fuzzy system for control of complex plants

Artificial neural networks (ANNs) and fuzzy systems (FS) are high parallel structures that consist of a large number of elementary nonlinear units (called neurons) fully interconnected. In this article we present a hardware implementation of ANNs combined with a DSP resulting in a powerful system used in control applications. The NN processes most of the control tasks, while the DSP performs signal pre-processing and learning algorithms. In some cases the DSP also tracks some discrete states of the plant by implementing a finite state automata and/or verifying plant safety boundaries operations. The tight link with a DSP allows the NN hardware to be very simple since several operations related with the NNs (learning, weights refresh, etc.) can be performed by the DSP. Moreover the system can implement intelligent control paradigms mixing neuro-fuzzy algorithms with finite state automata and/or digital control algorithms