Physics-Guided Neural Networks for Inversion-based Feedforward Control applied to Linear Motors

Ever-increasing throughput specifications in semiconductor manufacturing require operating high-precision mechatronics, such as linear motors, at higher accelerations. In turn this creates higher nonlinear parasitic forces that cannot be handled by industrial feedforward controllers. Motivated by this problem, in this paper we develop a general framework for inversion-based feedforward controller design using physics-guided neural networks (PGNNs). In contrast with black-box neural networks, the developed PGNNs embed prior physical knowledge in the input and hidden layers, which results in improved training convergence and learning of underlying physical laws. The PGNN inversion-based feedforward control framework is validated in simulation on an industrial linear motor, for which it achieves a mean average tracking error twenty times smaller than mass-acceleration feedforward in simulation.Comment: Submitted to 2021 IEEE Conference on Control Technology and Application

Physics-guided neural networks for feedforward control: From consistent identification to feedforward controller design

Model-based feedforward control improves tracking performance of motion systems, provided that the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics-guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes the use of a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller for high-precision motion control. The developed methodology is validated on a real-life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification

Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feedforward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics-guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean-absolute tracking error

Physics-guided neural networks for feedforward control with input-to-state stability guarantees

Currently, there is an increasing interest in merging physics-based methods and artificial intelligence to push performance of feedforward controllers for high-precision mechatronics beyond what is achievable with linear feedforward control. In this paper, we develop a systematic design procedure for feedforward control using physics-guided neural networks (PGNNs) that can handle nonlinear and unknown dynamics. PGNNs effectively merge physics-based and NN-based models, and thereby result in nonlinear feedforward controllers with higher performance and the same reliability as classical, linear feedforward controllers. In particular, conditions are presented to validate (after training) and impose (before training) input-to-state stability (ISS) of PGNN feedforward controllers. The developed PGNN feedforward control framework is validated on a real-life, high-precision industrial linear motor used in lithography machines, where it reaches a factor 2 improvement with respect to conventional mass-friction feedforward

Generalization of ILC for fixed order reference trajectories using interpolation

The increasing demands for motion accuracy in high–precision mechatronics call for intelligent solutions to feedforward controller design. Iterative learning control (ILC) produces data–driven feedforward signals that give high accuracy for repeating references. However, the ILC feedforward input requires time consuming re–learning for each variation of the reference. In order to circumvent the re–learning process, this paper presents a feedforward controller design that can handle fixed order references. First, we assume that ILC is used to obtain feedforward signals for a finite number of repeating references, and that these references can be split into sections that admit a polynomial parameterization. Then, we show that a new feedforward input can be calculated from the existing ILC signals for any polynomial reference spanned by parameter–wise linear combinations of the learned references. Effectiveness of the method is shown in simulation of a coreless linear motor