Finite Control Set Model Predictive Control (FCS-MPC) calculates torque and flux tracking errors via a cost function that is used for selecting the optimal vector. Compared with Field Oriented Control(FOC), FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulse-width modulation (PWM) and inner proportion-integration (PI) controllers. However, the weighting factor for modifying torque and flux terms must be tuned in accordance with varying operating conditions; this is an area in which further research is needed. In this paper, a Parallel Predictive Torque Control (PPTC) with adaptive constraints is proposed as a solution for this problem. The PPTC method optimizes torque and flux terms simultaneously, and switching-state candidates are then selected in an adaptive mechanism. The key feature is that torque and flux tracking errors are constrained within the optimized boundaries. The proposed PPTC is compared with the state-of-the-art PTC method. Both simulation and experimental results confirm that the proposed method, which has no weighting factor, achieves an even better dynamic performance and robustness than the conventional PTC.

This paper proposes a computationally efficient predictive current control (PCC) combined with an extension of finite set using current derivative projection, to solve the challenges experienced by finite control set PCC (FCS-PCC) i.e., unsatisfied steady-state performance and high computational burden. Owing to that only one switching sequence is utilized in the entire sampling period, FCS-PCC is inevitably penalized by the high torque and current deviations. More specifically, FCS-PCC suffers from the high computational burden caused by the exhaustive search in the optimization stage. To tackle the aforementioned issues, a reformulated objective function using current derivative projection with least-squares (LS) optimization in PCC is presented in this work. Firstly, PCC is geometrically described as a quadratic programming problem. To minimize the deviation between the selected and desired current derivative, the objective function is rearranged as the quadratic Euclidean norm of the derivative deviation. The exhaustive search in the optimization stage is avoided by a preselection principle. Based on the above, the optimal stator current derivatives in the consecutive sampling intervals combined with their duty cycles are optimized by the LS method. The effectiveness of the proposed method is verified by the experimental results based on a 2.2 kW IM drive platform.

This paper presents a sliding-mode (SM) model reference adaptive system (MRAS) observer for predictive torque control (PTC) in the induction machine drives. The PTC scheme is one of the most promising control strategy for electrical drives. However, its performance is constrained due to the accuracy of parameter and speed estimation. The proposed SM MRAS is employed to estimate rotor speed for PTC. The dominant feature of the methodology is that the adjustable model is modified by a sliding manifold in accordance with the error signal. Compared with conventional MRAS, the proposed method is more robust and easily tuned. To enhance robustness of the PTC scheme to the parameter variation, the extended rotor resistance compensation is employed in the estimator. In this paper, the experimental results confirm that the proposed method achieves fast dynamic performance and strong robustness.

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