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 ...

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 ...

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

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

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 ...

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 ...

Machine Design’s Motion Systems Takeover Week (Oct. 20–24, 2025) explored how the fusion of mechanical motion and data-driven control is reshaping high-precision applications across industries, from ...