Electric Motor Drives in Industry: Control Methods, challenges and the role of predictive control

Electric motor drives are widely used in industrial systems, electric vehicles, and renewable energy applications, where high efficiency, fast dynamic response, and robustness are important. Conventional control methods, such as field-oriented control (FOC), provide reliable performance but depend on accurate system models and careful tuning.
To address these limitations, Model Predictive Control (MPC) has been proposed as an alternative, offering improved dynamic performance and the ability to directly handle system constraints. However, model-based MPC is still sensitive to parameter mismatch and modelling errors, which can affect performance in practice.
To improve robustness, data-driven and model-free approaches have been explored, where system behaviour is estimated online from measured data rather than relying fully on predefined models. These methods aim to reduce dependence on parameter accuracy, although they introduce new challenges such as sensitivity to measurement noise and real-time implementation.
This presentation looks at different control strategies for electric drives, from classical methods to predictive and data-driven approaches, and discusses how they relate to practical industrial applications.
Co-sponsored by: Resilience and Clean Energy Systems (RCES)
Speaker(s): Joseph O. Akinwumi
Virtual: https://events.vtools.ieee.org/m/565078