Minimizing Position Error with Learning-Based Control Algorithms

Share With Your Colleagues

At micron- to nanometer-level motion performance and better, the world’s most advanced machines can be compromised by numerous effects and conditions including engineering tolerances, environmental operation conditions, and part/process/load dynamics. Engineers employ sophisticated tools to optimize motion system performance in a static environment before machine commissioning. In the field, these machines have a long life expectancy and their operators demand consistent, repeatable performance. Over the course of a machine’s life from design and commissioning to field installation and prolonged operation, performance can be variable. A machine that once met motion performance specifications might later in life fail acceptance test procedures.

To maximize and maintain the highest level of motion performance, ACS Motion Control has designed LearningBoost, a new servo control algorithm. This learning-based algorithm is used as a powerful tool during machine calibration, and autonomously online during machine process execution, to ensure continuously maximized performance.

“Reach previously unachievable levels of motion accuracy and throughput using learning-based servo control algorithms”

LearningBoost is a state-of-the-art control algorithm that autonomously compensates and optimizes servo performance to address disturbances both inside and outside servo bandwidth. Controls engineers will use LearningBoost to solve a range of issues that produce undesirable motion behavior, including but not limited to friction effects, motor temperature variability, mechanical component degradation, oscillations, resonances, cogging and more. Previously unachievable levels of motion system accuracy and throughput can be reached with LearningBoost.

Featuring a variety of powerful tools, LearningBoost’s operation modes and flexible user implementation will enhance motion performance across many different advanced industries and applications with high-precision motion control requirements.

Off-line learning operation mode, designed for use during machine calibration, allows control engineers to manipulate and tailor learned compensations, helping address unique application requirements. Learned compensation corrections are applied until nominal performance levels are reached.

Dynamic online operation mode, intended for in-process execution, is designed for continual learning and disturbance compensation corrections. Following the completion of a recipe, compensation corrections are applied, improving subsequent recipe performance.

Motion recipes that range from fractions of a second to 30 minutes will achieve optimal performance after a few motion recipe executions. Stored on an ACS Motion Controller, users can create and save up to 100 different motion recipes, eliminating the need to re-learn recipes after machine power-up and making them simple to deploy during serial machine production.

LearningBoost is an algorithm supported on motion controllers in the SPiiPlus series. To learn more about LearningBoost, contact [email protected].
To evaluate LearningBoost as an original equipment machine manufacturer, register for the OEM Motion System Evaluation Program.

Ideal Applications

Metrology Applications
Semiconductor Metrology
Flat Panel Display Processing
MicroLED Processing
Biomedical Inspection
Industrial roll-to-roll or raster scan printing

About Us

Since 1985, original equipment manufacturers in semiconductor, electronics assembly, laser processing, flat panel display, biomedical, and other high-tech industries have trusted ACS Motion Control to improve machine performance, enhance design flexibility, accelerate application development and minimize time to market.

ACS Motion Control Customer Promise Statement

We are always ready to rise to the challenge to deliver smart, best-performing motion control solutions for the most demanding applications, developed and supported in partnership with you and in line with your needs.