Next-generation motion control systems require fast and precise control, but advanced control strategies often rely on complex and expensive models. This paper proposes a data-driven approach to tune controllers for a joint used in a robotic arm. This is achieved by using the frequency response at different operating points and designing a low-bandwidth controller robust to multimodel uncertainties, not exciting the nonlinear modes of the system. Tracking performance is improved by tuning an appropriate feedforward controller, and additional constraints are derived to guarantee the stability of this filter. The proposed approach is applied to a joint in an industrial robotic arm.