Iterative learning control with applications in energy generation, lasers and health care

Abstract

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

Stimulating Intrinsic Motivation in Millennial Students: A New Generation, a New Approach

“…Training through simulation exercises offers pragmatic opportunities to build competence (Khalaila, ; Gillispie, ) and confidence (Barrett et al, ) in an interactive manner (Desy et al, ; Pandey et al, ) and within a risk‐free environment. The modalities that can be employed include, but are not limited to, screen‐based computer simulators (Abdel Meguid and Khalil, ; Havyer et al, ), standardized patients (Marks, ), complex task trainers (Rogers and Tutty, ; Christensen et al, ), and realistic patient simulators (Alcamo et al, ; Hill et al, ). These tools can be easily adapted to represent a very wide variety of client conditions (Miller, ; Kusurkar, ) that can be t ailored to different levels of difficulty ( Strombach et al, ; Raza et al, ) .…”

“…Having grown up in well‐organized settings (Bakon et al, ; Chen et al, ), millennial students value structure in their curriculum (Rogers and Tutty, ; Tai et al, ). In addition, well‐formulated teaching sessions enhance learning in students by creating a state of self‐determined motivation (Dassel et al, ; Christensen et al, ), and supporting many of the other strategies suggested above (Ottenberg et al, ; Penny, ).…”

“…Hence, test‐enhanced learning practices should emphasize no‐stakes or low‐stakes scenarios (Karpicke and Bauernschmidt, ; Strombach et al, ; Raza et al, ), where the primary purpose is to enhance recall to promote student learning (Sluka et al, ; van Gessel et al, ). In addition, such test‐enhanced learning practices should specifically align with predetermined course objectives to offer students opportunities to practice important skills (Rogers and Tutty, ; Schubert, ; Yan et al, ), analyze complex situations (Elmore et al, ; Woolley and Fishbach, ), and synthesize different points of view (Sinclair et al, ; de Bruin et al, ).…”

“…This review is based on a scrutiny of scientific publications that have been largely derived from the data generated from surveys of student personal satisfaction (Kong et al, ; Zielezinski and Karlowski, ; Strombach et al, ) and preference (Nuffer et al, ; Eidson et al, ; Khalil et al, ). Since most of the reported studies are usually from one or two similar institutions (Kong et al, ; Streilein et al, ), the opinions that have been espoused are at times based on the observations (Jacobs et al, ; Pape et al, ) and perceptions (Lopes et al, ; Rogers and Tutty, ; Mor et al, ) of researchers of isolated student populations. Thus, the published literature may not be accurately reflective of the extraordinary diversity of the present‐day student population (Bradley et al, ; Strombach et al, ; Brass et al, ; Mabel and Britton, ).…”

“…The current population of health care professionals is unique in that it comprises of numerous generations (Cook and Artino, 2016;Everson et al, 2018), which at times struggle to interact productively with each other (Tai et al, 2016;Rothmaler et al, 2017;Mor et al, 2018). This exemplifies the issues related to generational differences that scientists and educators have anticipated for decades (Khalaila, 2015;Rogers and Tutty, 2016;Gauer and Jackson, 2018).…”