Data-driven high-performance motion system optimization in practice

Manufacturing productivity demands positioning systems that achieve both high speed and micrometer-level accuracy. Existing approaches rely on time-intensive manual tuning or optimize controller layers independently. We extend prior work on data-driven controller tuning to demonstrate that jointly optimizing Model Predictive Contouring Control (MPCC) planner parameters and low-level controller gains using constrained Bayesian optimization improves performance beyond sequential or isolated tuning strategies. The approach models system performance metrics such as traversal time, tracking accuracy, and vibration levels over complete geometric trajectories as joint Gaussian processes, enabling sample-efficient exploration of the combined parameter space while respecting physical constraints. Numerical results show that joint optimization achieves 8–23% improvement in traversal time and 2.5 - 5 × reduction in maximum contour errors compared to optimizing either layer independently. Experimental validation on precision motion hardware demonstrates that MPCC parameter optimization alone (with pre-tuned low-level gains) achieves 15% improved maximal tracking error at a 6% faster traversal time. The framework is system-agnostic and requires no hardware modifications.