SOURCE-LINKED INTELLIGENCE

Robust Design of Trustworthy Neural Network Controllers

CORDIS · observation · Publication date unknown

formal guarantees, ensuring the robust stability and safety of neural feedback loops (NFLs). It combines model-based and data-driven approaches to address complexity, uncertainty and data scarcity in artificial intelligence (AI)-based control. The project focuses on one of the main barriers to applying AI in high-risk domains: the lack of certified trustworthiness. Although NN controllers demonstrate strong performance in simulations and prototypes, their use in fields such as aerospace, healthcare, robotics and critical infrastructure remains constrained. Without methods that provide the level of reliability and transparency required by regulators, adoption in these areas remains limited. RDTNNC aims to close this gap between empirical capability and verifiable trust. To achieve this, the project advances scalable verification methods based on integral quadratic constraints and sum-of-squares programming, integrating them with Lyapunov-constrained training to embed robustness and stability in controller design. Data-efficient strategies using meta-learning and few-shot adaptation ai

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR