Abstract
Industrial machine tool feed drives are predominantly controlled by cascade control due to their low tuning complexity and inherent robustness. However, the cascaded structure requires the inner cascades to have higher dynamics than the outer cascades, which limits the achievable dynamic accuracy. Direct control approaches, which substitute the position and velocity cascade, offer the potential to utilize the unused potential. A promising approach is model predictive control (MPC), which optimizes the manipulated variable with a plant model along a prediction horizon. However, model uncertainties between the nominal model and the real plant lead to tracking errors. Therefore, this paper presents, a linear MPC (LMPC) and an adaptive MPC (AMPC) with an additional integral action to robustly compensate for model mismatches. Both controllers use a compliant model, are real-time capable with a sample rate of \$\$\\2\\backslash,\backslashtextrm\kHz\\\$\$and consider state and input space constraints. The AMPC accounts for position-varying stiffness and friction. The controllers are experimentally compared with classical P-PI cascaded control on a ball screw drive. They show a tracking error reduction of \$\$\\37\\\backslash,\backslash\%\\\$\$(LMPC) and \$\$\\44\\\backslash,\backslash\%\\\$\$(AMPC) during a high speed motion profile and an increase in bandwidth of \$\$\\180\\\backslash,\backslash\%\\\$\$(LMPC) and \$\$\\184\\\backslash,\backslash\%\\\$\$(AMPC), resulting in significantly improved dynamic accuracy.
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