Fictitious Reference Iterative Tuning for Internal Model Controller

Internal model controller (which is abbreviated as IMC in the following ) is widely well-known as one of the easily-understandable controllers from the practical points of view. The reason for this is that IMC has a simple structure which works to decrease the error between the output of the actual plant and the output generated by the internal model included in the controller. In the case in which the internal model completely reflects the dynamics of the actual plant, implementing the model of a plant to IMC yields the desired tracking property. Conversely, in the case in which we do not know a mathematical model of a plant, the achievement of the desired output by some sort of method based on the direct use of the data enables us to identify the plant as the internal model in IMC. Moreover, since the data has a fruitful information of the plant, the direct utilization of the data yields more desirable IMC controller in order to achieve a given specification with reflecting the actual dynamics of the plant. From these ideas, we propose the data-driven approach to IMC with Fictitious Reference Iterative Tuning (which is abbreviated as FRIT) as one of the controller design methods without using a mathematical model of a plant. We show that the minimization of the cost function in FRIT for IMC directly leads to both the optimal controller for achievement of a desired response and a mathematical model reflecting the dynamics of the actual plant, as will become apparent afterwards.