Model bank based intelligent control

This paper deals with an intelligent system approach to the problem of control of plants with large parameters variations and multiple operating modes. It is based on the concept of a dynamic model bank - a long-term memory working in conjunction with a recursive learning algorithm that online estimates plant dynamics. The role of the model bank is to accumulate these models that successfully approximate the plant and to further use them to improve the performance of an indirect adaptive control algorithm. The models contained in the bank are used to periodically initialize a recursive least-square estimation procedure in the cases when it cannot provide a satisfactory approximation of the plant. An OWA aggregation operator that is dependent on the performance of individual models is applied to infer the initializing model parameters. The bank is continually updated by summarizing the parameters of the estimated models without requirement for off-line identification.