Data-driven control of nonlinear dynamic systems has emerged as a transformative paradigm in control engineering, leveraging empirical data rather than detailed first-principles models. By embedding ...

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 ...

Identify and address the critical questions that consistently impact automation project outcomes across diverse industries like food processing, pharmaceuticals and transportation. Learn technical ...

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.

How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems. While large language models are useful for chatbots, Q&A ...