An integrated hybrid approach to the design of high-performance intelligent controllers

This paper presents a hybrid approach to the development of high-performance real-time intelligent and adaptive controllers for nonlinear plants. Several paradigms derived from cognitive sciences ore considered and analyzed in this work, such as neural networks, fuzzy inference systems, genetic algorithms, etc. Although most of these paradigms are widely known and have been used extensively in the field of automatic control since several years, the novelty of the proposed approach resides in their tight integration and its capability of allowing a hybrid design. The different control strategies have also been integrated with the theory of finite state automata, in such a way that an automaton tracks the different plant states and selects accordingly one out of a given number of controller characteristics, each one being designed in a hybrid manner. State transitions can also be triggered by fuzzy and neural signals. Finally, two practical examples of the proposed hybrid approach are analyzed.