This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

https://www.itk.ntnu.no/fag/fordypning/TK16-filer/Samling1_MorariLee.pdf

Model predictive control: past, present and future

Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.

/pdf/carbon-capture-and-storage-ccs-the-way-forward-11rgsqyer0.pdf

Carbon capture and storage (CCS): the way forward

In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

Iterative Learning Control: Brief Survey and Categorization

Fossil fuel depletion, global warming, climate change, and steep hikes in the price of fuels are driving scientists to investigate on commercial and environmentally friendly fuels. The process of CO2 conversion to value-added products has been considered as a possible remedy to fulfill the requirements. The present review paper comprehensively discusses two different processes, namely hydrocarbon and methanol synthesis which are extensively used to convert CO2 to value-added products. Reaction mechanisms as well as the effects of catalyst, reactor type and operating conditions on product efficiency enhancement of each process are reviewed. Furthermore a brief overview on the reactor types as the most effective component of the theoretical and experimental reported results on the process improvement is given. All the information is tabulated in order to make the gathered information easily conclusive. Finally, by taking the available information into account the best reactor configuration which is adjustable to reaction mechanism is proposed.

Hydrogenation of CO2 to value-added products—A review and potential future developments

A quadratic-optimal iterative learning control (ILC) method has been designed and implemented on an experimental rapid thermal processing system used for fabricating 8-in silicon wafers. The controller was designed to control the wafer temperatures at three separate locations by manipulating the power inputs to three groups of tungsten-halogen lamps. The controller design was done based on a time-varying linear state-space model, which was identified using experimental input-output data obtained at two different temperatures. When initialized with the input profiles produced by multiloop PI controllers, the ILC controller was seen to be capable of improving the control performance significantly with repeating runs. In a series of experiments with wafers on which thermocouples are glued, the ILC controller, over the course of ten runs, gradually steered the wafer temperatures very close to the respective reference trajectories despite significant disturbances and model errors.

Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing

Kwang Soon Lee

Papers

Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing

Moving bed adsorption process with internal heat integration for carbon dioxide capture

Feedback-assisted iterative learning control based on an inverse process model

Iterative learning control of heat-up phase for a batch polymerization reactor☆

Model predictive control for nonlinear batch processes with asymptotically perfect tracking