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.