In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented by a set of convex constraints involving only the frequency response data of the plant model and the parameters of a fixed structure controller. The new convex constraints, are then integrated into a recently developed data-driven robust control framework that can consider other control performance and robustness specifications. The proposed method is applied to the current controller design in traction systems. According to the field standards, the real part of the input admittance of the converters should be positive for a specific range of frequencies. The existing methods in the literature are based on the passivity approaches using a parametric model of the system and usually require a disturbance observer and additional sensors. In the proposed method, only the measurement data is needed for controller design and there is no requirement of additional sensors that reduces the costs and increases the reliability. The effectiveness of the proposed method is validated through numerical simulation including switching converters. The results show that the proposed controller provides required positive-realness as well as good performance in tracking and disturbance rejection.