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 ...

Quick diagnostic sprints deliver measurable results in weeks, not years, helping manufacturers prove AI value before ...

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 ...

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.

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 ...

We have considerable expertise in MPC as a powerful tool for providing optimal control in dynamic environments, ensuring real-time performance and adaptability. Our work includes developing predictive ...

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 ...

In today's fast-paced digital economy, corporate decision-making is undergoing a seismic shift. The traditional reliance on historical data, experience and intuition is giving way to AI-powered ...

The future of automation and artificial intelligence in warfare begins with structured, interoperable data. As a staff officer, the greatest obstacle I’ve observed to achieving truly data-driven ...