As today’s industrial processes become more complex, controllers used
in drivetrains for vehicles, machines, robots, process facilities, and other
physical dynamic systems face increasing challenges with respect to e.g.
efficiency and quality. In an industry 4.0 setting, a higher level of
adaptivity and automation is required. Meanwhile, artificial intelligence
(AI) is a promising enabling technology. However, examples wherein AI
techniques such as reinforcement learning (RL) are directly in control of
(high) power (up to kW) and (highly) dynamic (down to (m)s)) physical
systems to improve energy efficiency and performance remain very
limited.
Going beyond the fixed but safe structure of classical controllers and
embracing the RL framework provides the ability to learn and adapt.
While doing so, expensive trials and unsafe experimentation on real
systems as is common in RL need to be avoided. We therefore propose a
fundamentally new approach residing at the intersection of classical
control and RL (CTRLxAI). Besides offering increased efficiency and
performance (thrust) of the adaptive and autonomous controllers; we
will strengthen the trustworthiness (trust) in terms of sample-efficiency,
robustness, safety and explainability; critical capabilities for industrial
widespread adoption. As such, we will realise our vision CTRLxAI=T
(H)RUST.
CTRLxAI will focus profoundly on pioneering concepts going beyond the
scientific state of the art tackling relevant challenges inspired by the
companies in the industrial advisory board. To enable this future
utilisation the pioneering CTRLxAI results will be validated up to TRL4-5.
Utilisation of the project results by Flemish companies will enable them
to increase their competitiveness as well as lower i.a. their production
footprint, lowering CO2 and other greenhouse gas emissions per capita
contributing to SDG13.