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

#energy #predictive-control #motor-drives

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.

Joseph O. Akinwumi of University of Alberta

Biography:

Joseph O. Akinwumi received the B.Sc. degree in Electrical and Electronic Engineering from Adeleke University, Nigeria. He later received the M.Sc. degree in Advanced Electrical and Electronic Engineering  from the University of Leicester, United Kingdom. He is currently pursuing the Ph.D. degree at the University of Alberta, Canada under the supervision of Prof Yunwei (Ryan) Li. His research focuses on control of electric drives and power electronic systems.

Email:

Address:9120 116 St NW, , Edmonton, Alberta, Canada, T6G 2V4