Search

Iterative learning control (ILC) can yield superior performance for repetitive tasks while only requiring approximate models, making this control strategy very appealing for industry. However, applying it to non-linear systems involves solving of optimization problems, which limits the industrial uptake, especially for learning online to compensate for variations throughout the system’s lifetime. Industry tackles this by designing simple rule-based learning controllers. However, these are often designed in an ad-hoc manner, which potentially limits performance. In this paper, we will couple a low-dimensional parametrized learning control algorithm with a generic signal parametrization method on the basis of machine learning, and specifically using autoencoders. This will allow high control performance, while limiting implementational complexity and maintaining interpretability, paving the way for a higher industrial uptake of learning control for non-linear systems. We will illustrate the parametrized approach in simulation on a non-linear slider-crank system, and provide an example of using the learning approach to perform a tracking task for this system.

In this paper the challenge of improving throughput in additive manufacturing and other laser-based manufacturing processes is addressed. It is the main obstacle, for industries using these processes, to market position themselves with regards to conventional material processing technologies. A key component affecting throughput, besides the material alteration process itself, is the laser scanner controlling the laser spot position.In this paper, a new model-based methodology to improve the performance of such laser scanners is proposed. This methodology consists of two main steps: (1) identification of a high-fidelity model of the laser scanner quantifying its dynamics (2) design of a model-based feedforward controller improving performance beyond conventional control. This methodology is implemented and validated using an industrial setup equipped with an open controller for additive manufacturing machines, an industrial laser scanner and a camera based monitoring system that allows an accurate evaluation of the scanner positioning. The validation on the setup showed improvement in performance up to 49% compared to conventional control of the laser scanner.

The ongoing need for miniaturization and an increase of throughput in IC-manufacturing is obstructed by performance limitations in motion control of nano-positioning wafer stages. These limitations are imposed by flexible dynamical behavior, associated with structural deformations of the nano-positioning stages. The aim of this research is to investigate limits on achievable performance in a conventional control configuration and to mitigate these limits through the use of additional actuators and sensors. To this end, a systematic framework for control design using additional actuators and sensors in the generalized plant configuration is presented, which leads to a well-posed H ∞ -control optimization problem that extends conventional design approaches in a natural way and exploits physical insight to address structural deformations in weighting filter design. Through an experimental confrontation of the design framework with a prototype next-generation nano-positioning motion system, successful performance enhancement beyond the conventional limits is demonstrated.