High-precision control of a robotic arm using frequency-based data-driven methods

Next-generation motion control systems require fast and precise control. However, advanced control strategies often rely on complex and costly system models. Data-driven methods have been proposed to design high-performance controllers without requiring a parametric model of the system. In particular, methods using frequency response functions (FRFs) have been widely applied to mechatronic systems due to their good performance, and the industry’s familiarity with obtaining FRFs. This paper applies a recently developed method to design a controller for an industrial robotic arm with three translational degrees of freedom, using only the FRF of the robot around different operating points. Focused on motion control, the objective is to attain the desired reference tracking performance through the design of a linear-parameter-varying (LPV) two-degree-of-freedom (2DoF) controller. Performance is further improved by tuning an additional filter to compensate for inaccuracies in the transmission. • Frequency responses are a powerful tool to describe the local dynamics of a system. • Frequency responses can directly be used to design linear parameter-varying controllers. • We propose a new method to design such controllers using semi-definite programming. • Efficacy of the method is demonstrated on a real industrial robotic arm.