AI supported Motor Control Development

Welcome to our exhibition on the revolutionary impact of AI in motor control applications. The integration of AI in motor control not only reduces energy consumption but also enhances the robustness of control algorithms, allowing for adaptive operation. Furthermore, it accelerates software development, enabling faster innovation and implementation of advanced control strategies.

In a groundbreaking collaboration, MOTEON, Infineon and other partners have come together in the EU-wide ECOMAI project to harness the power of neural networks for control prediction, shifting from reactive to proactive control strategies. This approach addresses dynamic changes in pressure, temperature, and component lifetime, including factors like permanent magnetism, coil resistance, and inductivity, all of which significantly influence system performance and efficiency. By combining AI with traditional motor control algorithms, these variables are carefully considered in the design process, paving the way for more intelligent and responsive motor control systems.

In real-world applications, such as compressor systems for air suspension or sensor cleaning, periodic load changes can exert varying forces on the motor, either augmenting or opposing its rotation. By integrating torque patterns from components like flywheels, springs, or compressors into a simulation environment, a learning process is initiated to predict behavioral changes and evaluate performance and efficiency. This innovative approach empowers the utilization of pre-trained algorithms, such as load predictive control, in cost-effective embedded microcontrollers, leading to a more robust and energy-efficient solution for motor control applications.

The collaboration between MOTEON and Infineon represents a significant leap in the utilization of neural networks in motor control design, particularly for applications with variable mechanical loads. Join us as we explore the transformative potential of AI in motor control and its profound impact on system efficiency, performance, and adaptability.

Reference number: 16ME0569