Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization-based tuning method for a hierarchical contour control system, significantly improving positioning accuracy, traversal speed, and vibration suppression in machining applications.

Contribution

It presents a sample-efficient Gaussian process-based tuning algorithm for hierarchical contour control, optimizing multiple parameters for better productivity.

Findings

Optimized parameters improve traversal speed and accuracy.

Vibration suppression is enhanced through parameter tuning.

The Bayesian approach reduces tuning effort and improves system performance.

Abstract

Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the increase of productivity in positioning systems. The high-level controller pre-optimizes the input to a low-level cascade controller, using a contouring predictive control approach. This control structure requires tuning of multiple parameters. We propose a sample-efficient tuning algorithm, where the performance metrics associated with the full geometry traversal are modelled as Gaussian processes and used to form the global cost and the constraints in a constrained Bayesian optimization algorithm. This approach enables the trade-off between fast traversal, high tracking accuracy, and suppression of vibrations in the system. The performance improvement is evaluated numerically when tuning different combinations of parameters. We demonstrate that tuning the parameters of the MPC contour-controller achieves the best performance in terms of time, tracking accuracy, and minimization of the vibrations in the system.