39 research outputs found
Memory-element based hysteresis:Identification and compensation of a piezoelectric actuator
Hysteresis phenomena can significantly deteriorate the performance when performing servo tasks with piezoelectric actuators. The aim of this brief is to model this nonlinear hysteresis effect and use this model to develop a feedforward controller that compensates for the hysteretic behavior. Exploiting the dual-pair concept, a connection is established between hysteresis models and general memory (MEM) elements examplified by the Ramberg–Osgood model. This facilitates both a straightforward identification procedure of a hysteresis model and a feedforward controller design. Both the identification procedure and the feedforward controller are implemented on a piezoelectric actuator indicating a performance improvement by a factor 3.5
Commutation-Angle Iterative Learning Control for Intermittent Data: Enhancing Piezo-Stepper Actuator Waveforms
Piezo-stepper actuators are used in many nanopositioning systems due to their
high resolution, high stiffness, fast response, and the ability to position a
mover over an infinite stroke by means of motion reminiscent of walking. The
aim of this paper is to develop a control approach for attenuating disturbances
that are caused by the walking motion and are therefore repeating in the
commutation-angle domain. A new iterative learning control approach is
developed for the commutation-angle domain, that addresses the
iteration-varying and non-equidistant sampling that occurs when the
piezo-stepper actuator is driven at varying drive frequencies by parameterizing
the input and error signals. Experimental validation of the framework on a
piezo-stepper actuator leads to significant performance improvements.Comment: 6 pages, 8 figures, 21st IFAC World Congress 202
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
Iterative learning control for intermittently sampled data: Monotonic convergence, design, and applications
The standard assumption that a measurement signal is available at each sample in iterative learning control (ILC) is not always justified, e.g., when exploiting time-stamped data from incremental encoders or in systems with data dropouts. The aim of this paper is to develop a computationally tractable ILC framework that is capable of exploiting intermittent data while maintaining favourable properties, including monotonic convergence. A controllability and observability analysis of the intermittent ILC framework leads to appropriate monotonic convergence conditions which allow for missing data. These conditions lead to a new explicit ILC controller design independent of the sampling instances, which is reminiscent of gradient-descent ILC. The approach is demonstrated on both an intuitive example and a practically relevant example which exploits time-varying timestamped data from an incremental encoder