Abstract. This paper considers the problem of designing a controller for an unknown plant based on input/output measurements. The new design method we propose is direct (no model identification of the plant is needed) and can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the case of restricted complexity controller design, the achieved controller is a good approximation of the restricted complexity global optimal controller. A simulation example shows the effectiveness of the method.

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The Virtual Reference Feedback Tuning (VRFT) is a data based method for the design of feedback controllers. In the original formulation, the VRFT method gives a solution to the one degree of freedom model-reference control problem in which the objective is to shape the input-output transfer function of the control system. In this paper, the extension of the method to the design of two degree of freedom controllers is presented and discussed.

Iterative Feedback Tuning (IFT) is a widely used procedure for controller tuning. It is a sequence of iteratively performed special experiments on the plant interlaced with periods of data collection under normal operating conditions. In this paper we derive the asymptotic convergence rate of IFT for disturbance rejection, which is one of the main fields of application.

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