Control-relevant neural networks for feedforward control with preview: Applied to an industrial flatbed printer

The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new criterion that emphasizes the closed-loop control objective,...

Cross-coupled iterative learning control: A computationally efficient approach applied to an industrial flatbed printer

Cross-coupled iterative learning control (ILC) can improve the contour tracking performance of manufacturing systems significantly. This paper aims to develop a framework for norm-optimal cross-coupled ILC that enables intuitive tuning of time- and iteration-varying weights of the exact contour error and its tangential counterpart. This leads...

SLIP-based Iterative Learning for Efficient and Compliant Locomotion of Articulated Soft Quadrupeds

Effectively controlling and exploiting the natural dynamics of Articulated Soft Robots for energy-efficient motions remains challenging. In literature, the problem is often split in two; in energy-efficient motion planning and structure-preserving control, where the focus is on one, and the other is largely disregarded. This work aims to unify...

Compensating commutation-angle domain disturbances with application to waveform optimization for piezo stepper actuators

Piezo stepper actuators are very promising for 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 enhance the waveforms for actuating piezo steppers, by actively compensating for...

Functional Iterative Learning Control With Full State Feedback Control of a Flexible Link Robot Actuated With a Stepper Motor

Advantages of lightweight design robot links include cost savings and reduced energy consumption. The flexibility introduced by the lightweight design can cause structural vibrations decreasing precision. To mitigate this, an advanced controller is proposed, consisting of Iterative Learning Control combined with Full State Feedback control. To...

Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation

Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both...

Non-Causal State Estimation for Improved State Tracking in Iterative Learning Control

State-tracking Iterative Learning Control (ILC) yields perfect state-tracking performance at each n sample instances for systems that perform repetitive tasks, where n stands for the order of the system. By achieving perfect state-tracking, oscillatory intersample behavior often encountered in output-tracking ILC has been mitigated. However,...

Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed...

State-tracking iterative learning control in frequency domain design for improved intersample behavior

Iterative learning control (ILC) yields perfect output-tracking performance at sampling instances for systems that perform repetitive tasks. The aim of this article is to develop a framework for a state-tracking ILC that mitigates oscillatory intersample behavior, which is often encountered in output tracking ILC. As a framework for the...

The generalization of feedforward control for a periodic motion system: A comparison of data-driven methods

A repetitive motion system supporting nano meter precision is positioned at high accelerations, which produces a force that disturbs the demanded accuracy requirements. Iterative learning control is used to learn optimal feedforward control signals for the attenuation this disturbance force. The iterative method comes with a limitation, as it...

Robust Reset Control using Adaptive / Iterative Learning Control

Reset controllers can outperform PID controllers and may introduce phase advantage compared to linear PID control. However, in general, reset controllers do not have the same steady state properties as linear controllers, like removing steady state errors. In case of model mismatches and disturbances, this may cause limit cycles (persisting...

Towards incremental kinesthetic teaching of bipedal walking

The large dimensionality of walking motions is a challenge for robot learning. The human seems designated to assist in this learning process, because of their aptness in walking. This paper presents a step in the investigation how a human can teach a robot a walking-like motion using incremental kinesthetic teaching. This approach lets the human...

Model-free motion control of positioning stage

Driven by the rise of market demand and development of technology, high-precision motion systems must meet the increasing accuracy requirement. Currently, High-precision positioning stage is widely used in many different application areas, such as hard disk drives, wafer steppers and electron and atomic force microscopes for nano-scale imaging....

Iterative Learning Control: Feasibility and Implementation for a Reticle Stage

ASML is always searching for improvements in reducing the servo error of both the Wafer Stage and the Reticle Stage of their lithography machines. Due to the repetitive motion and corresponding repetitive servo error of the Reticle Stage Short Stroke (RSSS), Iterative Learning Control (ILC) can be a powerful method to further reduce the servo...

Optomechatronics Design and Control for Confocal Laser Scanning Microscopy

Confocal laser scanning microscopy (CLSM) is considered as one of the major advancements in microscopy in the last century and is widely accepted as a 3D fluorescence imaging tool for biological studies. For the emerging biological questions CLSM requires fast imaging to detect rapid biological processes and aberration-corrected imaging to...

Batch-to-batch learning for model-based control of process systems with application to cooling crystallization

From an engineering perspective, the term process refers to a conversion of raw materials into intermediate or final products using chemical, physical, or biological operations. Industrial processes can be performed either in continuous or in batch mode. There exist for instance continuous and batch units for reaction, distillation, and...

Inkjet printhead performance enhancement by feedforward input design based on two-port modeling

Inkjet technology is an important key-technology from an industrial point of view. Its ability to deposit various types of materials on a substrate in certain patterns makes it a very versatile technology. Though the performance criteria imposed by today's applications are quite tight already, future performance requirements will be even more...