ARTIFICIAL INTELLIGENCE AND GRAPH THEORY TOOLS FOR DESCRIBING SWITCHED LINEAR CONTROL SYSTEMS

摘要

This paper develops a representation of multi-model based controllers using artificial intelligence techniques. These techniques will be graph theory, neural networks, genetic algorithms, and fuzzy logic. 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 multi-model controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multi-model based controllers. The obtained artificial intelligence-based multi-model controllers are compared with classic single model-based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multi-estimation based techniques. Furthermore, a method for synthesizing multi-model-based neural network controllers from already designed single model-based ones is presented, extending the applicability of this kind of technique to a more general type of controller. Also, some applications of genetic algorithms and fuzzy logic to multi-model controller design are proposed. In particular, the mutation operation from genetic algorithms inspires a robustness test, which consists of a random modification of the estimates which is used to select the one leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classic multi-model schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multi-estimation structures, which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design.