Model predictive control for reference tracking on an industrial machine tool servo drive

Abstract—The benefits of model predictive control (MPC) have beenwell established; however its application to reference tracking on digital servo drives (DSDs), which typically have very fast up-date rates, is limited by the computational power of present-day processors. This paper presents a novel MPC formulation, which provides a mechanism to trade-off online computation effort with tracking performance, while maintaining stability. This is achieved by introducing a trajectory horizon, which is distinct from the pre-diction and control horizons typically encountered in MPC formu-lations. It is shown that increasing the trajectory horizon inher-ently leads to improved tracking; however larger horizon lengths also have the unwanted effect of increasing online computation. The proposed MPC formulation is compatible with recently devel-oped explicit MPC solutions, and hence the burden of online opti-mization is avoided. The new approach is successfully implemented on an industrial machine tool DSD, and in terms of tracking accu-racy, is shown to outperform the incumbent approach of cascaded PID control. Index Terms—Discrete-time systems, model predictive control (MPC), motion control, tracking systems, servomechanisms. I