Dimensionless Two-Vector Model Predictive Current -Torque Control of Induction motor drives

Model predictive controls (MPCs) have gained significant attention in induction motor drives due to their simplicity, non-linear control capabilities, and multi-objective control. However, conventional MPCs often face performance issues, such as high starting torque and torque ripples in model predictive current control (MPCC), and slow dynamic response and high current ripples in model predictive torque control (MPTC). This paper proposes a novel model predictive current torque control (MPCTC) for induction motor drives, combining the strengths of MPCC and MPTC by evaluating two separate cost functions for torque and current to generate an optimal switching vector. The fuzzy decision-making (FDM) principle is employed to evaluate these cost functions in a dimensionless manner, eliminating the need for weighting factors. Additionally, to enhance steady-state performance, an additional voltage vector is generated based on the current cost function and applied alongside the first vector generated using FDM, with appropriate duty cycle control. The effectiveness of the proposed MPCTC is validated through simulation and quantitative analysis, demonstrating superior performance in balancing dynamic and steady-state responses, as well as torque and current performance, compared to conventional MPCC and MPTC.