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 ...

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

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 research team has developed a novel method for estimating the predictability of complex dynamical systems. Their work, "Time-lagged recurrence: A data-driven method to estimate the predictability of ...

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 ...

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

Manufacturers are moving beyond audits and checklists by using data-driven safety platforms to improve compliance and proactively manage workplace risk.

You often hear entrepreneurs say, “We don’t know what we don’t know,” when talking about deficiencies in data gathering. But when you have data in silos, it’s more a case of “We don’t know what we DO ...