Artificial intelligence and graph theory tools for synthesizing switched discrete adaptive controllers for linear time invariant plants

This paper develops a representation of multi-model based controllers using graph theory and artificial intelligence techniques. These techniques are neural networks and genetic algorithms. Thus, graph theory is used to describe in a formal and concise way the switching mechanism between the various plant parameterizations of the switched system. Moreover, the interpretation of multimodel controllers in an artificial intelligence frame allows the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence-based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms to multimodel controller design are proposed