Real-Time Predictive Control for Precision Machining

TL;DR

This paper compares two optimization-based predictive control methods for precision machining, focusing on high-speed, accurate positioning, and real-time implementation, evaluated through simulations on challenging geometries.

Contribution

It introduces and compares two novel predictive control approaches tailored for high-performance, real-time machining applications, emphasizing different error definitions and system simplifications.

Findings

Both approaches achieve high-speed and accurate positioning.

The local error approach enables real-time optimization with fewer states.

Performance varies with different quadratic programming solvers.

Abstract

Precise positioning and fast traversal times are crucial in achieving high productivity and scale in machining. This paper compares two optimization-based predictive control approaches that achieve high performance. In the first approach, the contour error is defined using the global position, the position on the path is inferred through a virtual path parameter, and the cost function combines the corresponding states and inputs to achieve a trade-off between high speed and positioning accuracy. The second approach is based on a local definition of both the error and the progress along the path, and results in a system with a reduced number of states and inputs that enables real-time optimization. Terminal and trust region constraints are required to achieve precise tracking of geometries where a fast or instantaneous change in direction is present. The performance of both approaches using different quadratic programming solvers is evaluated in simulations for geometries that are challenging in machine tools applications.