<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/2A208D2B-04FF-40C6-A214-E392F4DCD968" ns1:id="2A208D2B-04FF-40C6-A214-E392F4DCD968"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/0C251E29-95E1-4F50-8829-122F06018B38" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/1258F43B-5B4E-4F0E-8032-7CA02B0FB869" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/1258F43B-5B4E-4F0E-8032-7CA02B0FB869" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-07-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/7D47F707-12F1-45CA-868C-0F75EED839FA" ns1:rel="FUND" ns1:start="2023-04-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10064303</ns2:identifier></ns2:identifiers><ns2:title>Trustworthy AI for Industrial Process Control</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Luffy AI is developing a novel Adaptive Neural Network systems targeted at embedded robotic and industrial control. Our approach overcomes the &amp;quot;reality gap&amp;quot; that limits the application of the current generation of neural network systems in these sectors.

In order to achieve wide acceptance within process industries, the developed technology must be proved safe, and a large number of stakeholders must understand how the AI technology impacts them and the process that is being controlled. The challenge is to cover needs of all stakeholders from the human operating the process to board room decision makers.

In this grant project we undertake a feasibility study of applying Adaptive Neural Network controller into different processes used in the Foundation Industries. As an output, the project delivers releases a white paper discussing the potential applications of neural network controllers within the foundation industries, and the steps required to ensure trusted and responsible deployment. The project would culminate in formation of a consortium that would aim to deploy Adaptive Neural Network controller to a selected industrial process.</ns2:abstractText></ns2:project>