Traditional motor control methods, heavily reliant on precise mathematical models, often struggle in complex and varying operational conditions. Model-Free Predictive Control is a technology that acts like a "smart brain" for motor drives. By leveraging real-time data to predict future motor behavior, MFPC makes optimal control decisions without the need for detailed system models. This approach not only streamlines design but also excels in multi-objective optimization, offering unprecedented flexibility and robustness to motor drives.
The study, led by Yongchang Zhang, analyzed the characteristics and latest developments of four MFPCs:
I. Prediction Error Correction-Based : It maintains the ideal system model and uses estimation and compensation to handle errors, improving parameter robustness. However, nominal parameters of the controlled plant and suitable error gains are needed.
II. Current Difference-Based : It builds a current differential look-up table from sampled data to predict future currents. However, data stagnation may occur if certain voltage vectors are unused for long periods.
III. Ultra-Local Model-Based : This MFPC constructs an ultra-local model capturing uncertainties and nonlinearities, estimating unknowns in real-time for prediction. It excels with unknown or changing parameters but demands high computational power and prior knowledge.
IV. Black-Box Model-Based : Treating the motor as a black box, it builds predictive models solely from input-output data using advanced system identification, achieving high precision without an accurate system model. However, it also requires significant computation.
【 MFPC in Action 】
Model predictive control gains industrial traction for its simplicity and speed, evident in ABB's inverters and automotive R&D by Volvo and Tesla. MFPC expands this impact further and addresses challenges in sectors like cybersecurity, power grids, and aerospace. It also optimizes permanent magnet motor-driven appliances, showcasing its broad real-world applicability.
Future outlook
【Challenges and the Road Ahead 】
Despite its advancements, MFPC faces challenges such as sampling delays and dead-time effects that may affect prediction accuracy. Achieving high-precision control at low switching frequencies also remains a hurdle. However, with ongoing research and technological progress, these obstacles are expected to be overcome.
Looking ahead, MFPC holds immense potential for wider adoption across various industries. From intelligent manufacturing equipment to advanced rail transportation systems, and from high-power variable frequency drives to home appliance frequency conversion, MFPC promises smarter, more efficient motor control solutions.
The complete study is accessible Via DOI: 10.30941/CESTEMS.2025.00002
CES Transactions on Electrical Machines and Systems
Experimental study
Not applicable
Model-Free Predictive Control of Motor Drives: A Review
29-Jan-2025