A framework for on-line learning of plant models and control policies for restructurable control

In this paper a learning framework to deal with restructurable control of a single-output dynamic plant is proposed. The central concept used to represent the restructurable behavior of the plant, and subsequently for the design of the framework, is the behavioral graph. The nodes of this graph correspond to possible local behaviors of the system while its edges model the switching scheme of the plant among its local behaviors. In the definition of this concept, general dynamical system theory is used. The framework is able to learn the dynamics (models) of a reconfigurable system, select appropriate models, and ultimately control the plant according to given specifications. The framework design borrows concepts and techniques from the active fields of adaptive and learning control. The underlying ideas and the software prototype implementing the framework design are tested through a series of simulated experiments. The simulations demonstrate the feasibility of the approach for controlling plants with unexpectedly and structurally changing behaviors in moderately noisy environments. They also identify a number of constraints that have to be satisfied for successful operation of the framework. This paper also discusses further validation of the approach, real-time application issues, and potential enhancements of the framework's functionality.<
>