A Cost-Effective and Flexible Decision-Making Method for Multi-Objective Finite Control Set Model Predictive Control

Multi-objective finite control set-model predictive control (FCS-MPC) plays a prominent role in controlling modern power converters and electric drives. However, selecting weighting factors (WFs) remains challenging, particularly when considering additional control objectives (ACOs). To address this challenge, this paper proposes a straightforward, cost-effective, and flexible decision-making (DM) method for multi-objective FCS-MPC strategies. The proposed hierarchical structure DM method utilises the Euclidean norm to individually evaluate the main and additional control performances. Because it allows for the expansion of the number of hierarchical stages and does not require a sorting algorithm, it stands out as a flexible and technically effective solution among existing DM techniques for the given problem. To demonstrate its effectiveness, it is applied to the model predictive torque control (MPTC) strategy to drive a permanent magnet synchronous motor. The MPTC with the proposed DM method is tested under various operating conditions and compared with the conventional MPTC. The experimental results confirm the ability of the proposed DM technique to handle ACOs in conjunction with the main control objectives and system constraints, while also demonstrating increased flexibility, enhanced control performance, and reduced computational demand.