Finite-state predictive torque control (FS-PTC) is computationally expensive, since it uses all voltage vectors (VVs) available from a power converter for prediction and actuation. The computational burden is rapidly increased with the number of VVs and objectives to be controlled. Moreover, designing a cost function with more than two control objectives is a complex task. This paper proposes a simplified algorithm based on a new direct torque control (DTC) switching table to reduce the number of VVs to be predicted and objectives to be controlled. The new switching table also assists to reduce average switching frequency and its variation range. As a result, the cost function is simplified by not requiring to include the frequency term. Experimental results show that the average execution time and the average switching frequency for the proposed algorithm are greatly reduced without affecting the torque and flux performances achieved in the conventional FS-PTC.

A sensorless finite-state predictive torque control (FS-PTC) strategy uses stator current, estimated stator and rotor flux, and estimated rotor speed to predict stator flux and torque. Direct application of measured stator currents and using a noisy estimated speed in the prediction model degrade the steady-state performance in terms of higher current total harmonic distortion (THD), torque ripple, and flux ripple, particularly at low speeds. This paper proposes an extended Kalman filter (EKF)-based, which is a promising state observer, improved prediction model of sensorless FS-PTC for induction motor drives. The EKF has been used to estimate rotor speed, rotor/stator flux, and stator currents accurately. The estimated stator currents, instead of measured currents, are fed back to the prediction model, and thus, small stator current THD is confirmed. Depending on the commanded speed, either the rotor current model or the open-loop stator voltage model is proposed for the EKF to achieve better performance in a wide speed range, including the field-weakening region. The proposed control system has been verified experimentally, and excellent torque and flux responses, robustness, and stable operation at lower and higher speeds have been achieved.

Finite-state predictive torque control (FS-PTC) of an induction motor (IM) drive has been widely investigated for two-level voltage source inverter recently. This control method suffers from high and variable switching frequencies in a wide range, due to the limited number of available voltage vectors of the power converter. In order to take advantage of multilevel inverter drives which offer the benefits of low harmonic distortion of the stator currents, torque ripple, and low switching frequency, this paper proposes the integration of the FS-PTC with a three-level neutral-point clamped (3L-NPC) inverter driven IM drive. The drawback inherited from the topology of 3L-NPC voltage source inverter (VSI), such as neutral-point voltage, is easily handled by treating it as a variable to the cost function. Similarly, apart from the inverter topology itself, the average switching frequency is reduced further, and is maintained almost constant over a wide speed range. The effectiveness of the proposed FS-PTC in terms of torque and flux responses, capacitor voltage balancing, and low average switching frequency is validated through experimental results.

A finite-state predictive torque control system for a speed-sensorless induction motor drive supplied from a three-level neutral-point clamped inverter is proposed. For sensorless operation, the controller requires estimated speed and rotor/stator flux. In this study, the rotor speed and the rotor flux are estimated accurately by using an extended Kalman filter. Due to the large number of available voltage vectors, the control algorithm for a multilevel inverter-fed drive is computationally expensive. As a consequence, the controller requires longer execution time that yields worse torque, flux, and speed responses, especially at low-speed. In order to reduce the computational burden, a reduced number of voltage vectors for prediction and optimization in the control algorithm is proposed in this paper. The sign of the stator flux deviation and the position of the stator flux are predicted to lessen the number of voltage vectors tested. Experimental results illustrate that the proposed encoderless strategy can estimate the speed accurately over a wide speed range including field-weakening region while maintaining robustness and excellent torque and flux responses.

Computational burden is a major hurdle for practical implementation of finite-state predictive torque control (FS-PTC) of motor drive fed by a multi-level inverter. One of the reasons of computational complexity is that all voltage vectors are evaluated for prediction and actuation. This paper proposes a reduced number of voltage vectors for the prediction and actuation, which are called prediction vectors in FS-PTC. The performance is investigated for a three-level neutral-point clamped (3L-NPC) inverter fed motor drive in terms of torque and flux response, stator current total harmonic distortion (THD), robustness, average switching frequency, and neutralpoint voltage variation. The number of prediction vectors is reduced based on the position of stator flux and the deviation in stator flux from its reference. Experimental results confirm that the computational burden could be reduced by 38%, while the dynamic performance is comparable to the conventional all voltage vectors based FS-PTC.