Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for...

Area of Science:

• Electrical Engineering
• Control Systems
• Power Electronics

Background:

• Conventional finite control set model predictive torque control (MPTC) uses weighting factors in its cost function to balance multiple control objectives.
• Selecting appropriate weighting factors is a significant challenge, and improper choices can degrade controller performance in electric drives and power converters.

Purpose of the Study:

• To propose a novel and effective method for tuning weighting factors in MPTC using the Multi-Criteria-Decision-Making (MCDM) Entropy technique.
• To address the challenge of optimizing the relative importance of diverse control objectives within the MPTC cost function.

Main Methods:

• The proposed method utilizes the Entropy technique, a Multi-Criteria-Decision-Making (MCDM) approach, for quantitative assessment and online tuning of weighting factors.
• It involves forming a dataset of control objectives (electromagnetic torque and stator flux magnitude), normalizing the objective matrix, and applying the entropy technique to derive optimal weights.
• Experimental validation was performed using a dSpace dS1104 controller for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive.

Main Results:

• The proposed Entropy-based method demonstrated improved dynamic response in the induction motor drive under varying operating conditions compared to conventional MPTC and other MCDM techniques.
• Significant reductions were achieved: 28% in computational burden and 38% in total harmonic distortion.

Conclusions:

• The Entropy method offers an adaptive and quantitative approach for tuning weighting factors in MPTC, enhancing controller performance.
• This technique effectively optimizes control objectives, leading to better dynamic response, reduced computational load, and lower total harmonic distortion in electric drives.