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 ...
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 ...
Streamline Control and Snowflake deliver a unified data foundation that helps energy organizations modernize faster and ...
During Machine Design’s Motion Systems Takeover Week (Oct. 20–24, 2025), we explored how the fusion of mechanical motion and data-driven control is reshaping high-precision applications across ...
How finance and operations leaders can take back control of their telecom spending by using a data-driven approach ...
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How Governance-by-Design Frameworks Are Reshaping Responsible AI in Enterprise Systems As artificial intelligence cont ...
Modern warfare demands seamless coordination and interoperability among multinational forces for effective partnerships. Logistics, often referred to as the lifeblood of military operations, is a ...
The car industry is evolving with the integration of agentic artificial intelligence (AI) in intelligent vehicles, revolutionizing the car manufacturing process through data-driven design and ...
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