Abstract System inversion is at the basis of many feedforward and learning control
algorithms. The aim of this paper is to analyze several of these approaches in view of their …

Learning from past data enables substantial performance improvement for systems that
perform repeating tasks. Achieving high accuracy and fast convergence in the presence of …

Learning control enables significant performance improvement for systems that perform
repeating tasks. Achieving high tracking performance by utilizing past error data typically …

The piezoelectric actuator is indispensable for driving the micro-manipulator. In this paper, a
simplified interval type-2 (IT2) fuzzy system is proposed for hysteresis modelling and …

The filtered basis functions (FBF) approach is gaining interest for feedforward tracking
control of linear, especially, nonminimum phase systems. It expresses the control input to the …

Model-Free Inversion-based Iterative Control (MFIIC) enables tracking performance
improvement of systems that perform repeating tasks without using a model of the system …

For an accurate and precise periodic scanning motion of a galvanometer scanner, this paper
presents iterative learning control (ILC) that is designed and implemented in the frequency …

In many engineering applications, mechanical contact leads to unwanted dynamic
phenomena, such as excitation of high frequency modes. To investigate the induced …

This paper proposes an iterative learning control (ILC) for a micro stereo lithography (MSL)
setup to enhance both the speed and the quality of 3D printing. The MSL setup is built based …

For highly precise motion of a galvanometer scanner that tracks a periodic motion reference,
learning control significantly decreases the tracking error. To achieve higher quality motion …