Modern control system design is increasingly embracing data-driven methodologies, which bypass the traditional necessity for precise process models by utilising experimental input–output data. This ...

Data-driven control represents a paradigm shift in the design and implementation of controllers for both linear and nonlinear systems. Eschewing traditional reliance on first‐principles models, this ...

To govern AI safely and keep its speed advantage, enterprises must move from static, rule-based control systems to adaptive, ...

Data is one of organizations' most potent assets in an era of growing competition and artificial intelligence (AI) mandates. However, effectively managing and using it requires balancing strict ...

There is now broad consensus that data-driven decision-making is essential to success in today’s highly competitive manufacturing environment. Customers’ price ...

According to MarketsandMarkets™, the data center access control market is expected to grow from USD 1.55 billion in 2025 to ...

In the modelic control paradigm, the first step is to establish a dynamic model through system identification. This model offers a continuous but inaccurate description of state transition information ...

(A) 3D model of the manipulator structure, consisting of 3 continuum segments. The manipulator operates in the plane. (B) Close-up view of the revolute joint between adjacent disks. (C) Diagram ...

For today’s CISOs, the perimeter isn’t a firewall — it’s the data itself. Hybrid and multi-cloud architecture have created massive volumes of sensitive ...

AI can be added to legacy motion control systems in three phases with minimal disruption: data collection via edge gateways, non-interfering anomaly detection and supervisory control integration.