Frequency-domain-based nonlinear normalized iterative learning control for three-dimensional ball screw drive systems.

Iterative learning control (ILC) is a well-established method for achieving precise tracking in repetitive tasks. However, most ILC algorithms rely on a nominal plant model, making them susceptible to model mismatches. This paper introduces a novel normalization concept, developed from a frequency-domain perspective using a data-driven approach, thus eliminating the need for system model information.

Rapid identification of fruit length loci in cucumber (Cucumis sativus L.) using next-generation sequencing (NGS)-based QTL analysis.

The cucumber (Cucumis sativus L.) exhibits extensive variations in fruit size and shape. Fruit length is an important agronomic and domesticated trait controlled by quantitative trait loci (QTLs).