6 research outputs found
Learning for Advanced Motion Control
Iterative Learning Control (ILC) can achieve perfect tracking performance for
mechatronic systems. The aim of this paper is to present an ILC design tutorial
for industrial mechatronic systems. First, a preliminary analysis reveals the
potential performance improvement of ILC prior to its actual implementation.
Second, a frequency domain approach is presented, where fast learning is
achieved through noncausal model inversion, and safe and robust learning is
achieved by employing a contraction mapping theorem in conjunction with
nonparametric frequency response functions. The approach is demonstrated on a
desktop printer. Finally, a detailed analysis of industrial motion systems
leads to several shortcomings that obstruct the widespread implementation of
ILC algorithms. An overview of recently developed algorithms, including
extensions using machine learning algorithms, is outlined that are aimed to
facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced
Motion Control, 202
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
Kernel-based identification of non-causal systems with application to inverse model control
Models of inverse systems are commonly encountered in control, e.g., feedforward. The aim of this paper is to address several aspects in identification of inverse models, including model order selection and dealing with unstable inverse systems that originate from inverting non-minimum phase dynamics. A kernel-based regularization framework is developed for identification of non-causal systems. It is shown that ‘unstable’ models can be viewed as bounded, but non-causal, operators. As the main contribution, a range of the required kernels for non-causal systems is developed, including non-causal stable spline kernels. Benefits of the approach are confirmed in an example, including non-causal feedforward control for non-minimum phase systems
Kernel-based identification of non-causal systems with application to inverse model control
Models of inverse systems are commonly encountered in control, e.g., feedforward. The aim of this paper is to address several aspects in identification of inverse models, including model order selection and dealing with unstable inverse systems that originate from inverting non-minimum phase dynamics. A kernel-based regularization framework is developed for identification of non-causal systems. It is shown that ‘unstable’ models can be viewed as bounded, but non-causal, operators. As the main contribution, a range of the required kernels for non-causal systems is developed, including non-causal stable spline kernels. Benefits of the approach are confirmed in an example, including non-causal feedforward control for non-minimum phase systems