In high-precision measurement systems, the feedforward controller is designed to compensate for all known disturbances to a system, e.g., a desired position to be tracked. To compensate for all known disturbances to a system the feedforward controller is designed as an inverse model of the system. The performance that can be achieved by exploiting a feedforward controller hinges upon the quality of this model. Hard-to-model dynamics, such as nonlinear friction effects, are often neglected and therefore not compensated for.
The aim is to compensate for these hard-to-model dynamic effects using a novel feedforward controller that exploits a neural network in addition to a physics-based model, also known as physics-guided neural networks (PGNN). The addition of the neural network enables unprecedented performance levels in positioning accuracies as is demonstrated on an industrial linear stage in a comparison between a conventional feedforward strategy and the PGNN-based feedforward controller. The findings, along with the improvement factor achieved in this comparison, were presented by Nard.
Wednesday 27th September, the last day of the DSPE Conference 2023, our colleague Nard Strijbosch presented how IBS Precision Engineering is rethinking system control with Physics guided neural networks for feedforward control.