Unfalsified adaptive control strategy is a data-driven approach in robust adaptive control that selects the stabilizing controller from an the available control bank based on the input-output system’s data. Selection is done without activating the controllers by using the virtual reference signal and a cost function. Stability of the closed loop system is guaranteed. In this paper, inspired by the unfalsified control approach, the goal is to select a controller from the available pre-designed controller bank that has the highest level of disturbance attenuation. The selection is based on the system’s data by using a cost function without disturbance measurement. By introducing a new concept called virtual disturbance, the performance of controllers is evaluated without activation. The convergence of the algorithm and the disturbance attenuation level of the proposed method have been proven in a theorem. Simulation results show performance of the proposed method on a wind turbine for various wind speed disturbances.

Feedback control loops are hidden but ubiquitous mechanisms all around the modern world. The current control technology utilizes model-based advanced control systems design. The philosophical background thought behind such designs is an inductivism-based reductionist-mechanistic approach. Despite its outstanding problem-solving achievements, it is argued that a deduction-based philosophical approach in the form of data-driven control system design has emerged. Unfalsified control is a notable outcome of philosophical thinking and is briefly reviewed.