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Deadbeat model predictive control is known as a precise control method in motor control applications. Recently, finite set deadbeat control is applied to power electronics applications. A voltage‐based cost function is used to select the closest voltage vector to the reference vector by examining all possible states in the cost function. This method needs a high amount of computation when it is applied to multilevel inverters or virtual voltage vectors due to a considerable increase in the number of candidates. Some studies have been done to reduce the number of candidate voltage vectors and computation burden. The methodology in these methods is based on finding the adjacent area and applying the vertex vectors to the cost function. So, these strategies are multi‐step and complex. The hexagonal zoning deadbeat model predictive control is proposed and introduced. The basic idea of the proposed method is that each zone should contain only one vector. In this way, the area identification is equal to finding the optimum voltage selection. Hexagonal zoning is used, which simplifies the process of identifying the area. A simple area identification method is developed based on the reference voltage phase angle. The proposed method can be applied to different inverters with different spatial vector diagrams. Furthermore, the synthesis of the virtual vector is manipulated to reduce the torque and flux ripple. The proposed method is experimentally evaluated by applications to the virtual voltage switching of a two‐level inverter for the induction motor drive in this research. [ABSTRACT FROM AUTHOR]

A sampling error‐based finite‐set predictive current control (FS‐PCC) is proposed in this article for the open‐end winding induction motor (OEWIM) drive. The proposed scheme controls the zero‐sequence current (ZSC) alongside the stator currents. In a model‐free approach, this method predicts the future of ZSC and stator current components by the stator current and voltage sampling errors. In this way, the parameters of the OEWIM are not utilised in the prediction algorithm of the FS‐PCC. So, the proposed method is robust against the variation of the parameter. Moreover, this article presents a simple vector selection technique for the FS‐PCC of the OEWIM. The proposed technique has two cost functions and a simple algebraic equation to put the voltage vectors (VVs) in the prediction algorithm. The first cost function uses VVs that do not have the zero‐sequence voltage component. Then, the algebraic equation determines VVs that must be utilised in the second cost function. Finally, the optimum VV is selected by the second cost function. In the proposed scheme, the prediction algorithm is iterated 14 times instead of 27 iterations of the conventional predictive algorithm. So, besides establishing a novel model‐free prediction algorithm, the proposed method has almost 50% fewer calculations. The validity of the proposed sampling error‐based FS‐PCC and the simplified vector selection technique has been verified through experimental tests. [ABSTRACT FROM AUTHOR]

The parameter estimators and the disturbance observers are two widely used methods for the robustness improvement of the model predictive control schemes. This article presents a hybrid solution to improve the robustness of predictive torque control (PTC) for induction motor (IM) drive. A novel integral sliding mode observer (ISMO)-based ultralocal model and an adaptive observer are combined in the proposed method to establish a robust prediction model for the PTC. The stator current prediction model of the conventional PTC contains different parameters and variables of the IM that increase the sensitivity of the method. The proposed method solves this problem by replacing the conventional stator current prediction model with the ISMO-based ultralocal model, which does not require the IM’s parameters. On the other hand, the stator flux prediction model of the PTC just depends on the stator resistance. So, an adaptive Luenberger observer is utilized to cancel the effect of resistance variation from the stator flux prediction model. The proposed ISMO and the Luenberger observer are constructed based on the Lyapunov theory to guarantee the stability of the proposed control method. The experimental validation of the proposed method is performed. Also, the robustness of this method has been validated experimentally. [ABSTRACT FROM AUTHOR]

In power electronics applications, finite set model predictive control (FS-MPC) has proven to be a viable strategy. However, due to the high processing power required, using this technology in multilevel converters is difficult. This strategy, which is based on predicting the behavior of the system for all conceivable states, has an issue with a numerous of possible switching states. A recent and useful strategy for dealing with the problem is the limiting of calculations based on triangle regions. Despite its success, this method has several limitations, including the computation required to locate the right triangle and the boundary modes. In this research, the vertical areas are used for the limiting of calculations. Not only determining the right zone is an easy task with this strategy, but the number of possible candidates is also reduced to two. Furthermore, the boundary mode will not occur. In the proposed method, two key advantages can be seen in the discussion of reduction of calculations: (1) new zoning, which eliminates the calculations related to the slope of the lines. (2) The number of options placed in the cost function has been reduced to 2 candidates. Simulations are used to validate the approach, which is applied to a three-level neutral point clamped (NPC) inverter. [ABSTRACT FROM AUTHOR]

The extended state observer (ESO) has been found as an efficient solution for the model sensitivity in the model-predictive control (MPC). Generally, the ESO is used for disturbance rejection when the voltage reference is the output of the control law. The observed disturbance is subtracted from the control law. Thus, using disturbance rejection for finite-set MPC is a challenge when it is utilized by a cost function consisting of the errors of the torque and the flux. In this research, a modified disturbance rejection method is used in a feedforward shape to improve the robustness of the finite-set model-predictive torque control. In this regard, the motor parameters have not appeared in the torque prediction algorithm. Only a rough approximation of the motor parameters is needed to design the ESO, which means that the proposed method has a very low dependence on the motor parameters. Besides, a thorough tuning guideline is proposed to tune the local parameters based on the convergence analysis of the ESO by using the self-stable region approach and the Lyapunov function. The performance of the proposed MPC scheme is evaluated through simulations and experimental tests. The results of the proposed method are compared with the classic MPC results. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) methods without using a weighting factor is being investigated as the state of the art in the power electronics application. In this article, a very simple and fast technique is proposed for making the decision among the possible options. The proposed method is an amendment on the sequential predictive control. The amendment is based on understanding the importance of the torque and the flux criteria in each control interval. The criterion that results in less cross-error will be used for voltage vectors (VVs) selection. Therefore, the high sequence may switch between the torque and the flux errors in every control interval. With this method, the best option can be found without using the weighting factor. The main advantage is avoiding the selection of an option with a big cross-error (definitely nonoptimum option). While that selection is possible by the fixed sequence method. Although, simplicity and fast computation will also be achieved. The proposed method is verified by experimental tests in different operating conditions. [ABSTRACT FROM AUTHOR]

The loss model of an induction motor based on the electric circuit is known as the more comfortable model to be identified compared to the magnetic model. This model is commonly used for field-oriented control rather than direct torque control (DTC). In the recent research for applying the electric loss model for flux optimization, the core loss resistance current is ignored, since it will reduce the robustness and accuracy of the model. In this article, it is analytically proved that considering that current will reduce the sensitivity significantly. Thus, a robust loss model based on a complete electric circuit is proposed for flux optimization in predictive torque and flux control. The predictive method had been chosen because of the fast dynamic response. In order to avoid computation increase, the predictive direct voltage control is used. The validity of the proposed method is studied via experimental results. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) has been accepted as an effective and feasible method in variable-speed drive applications. Considering the discrete nature of the converters and using a cost function for state selection reduce the computational burden despite of having a precise control. In this paper, FCS-MPC has been used to develop a novel method for induction motor control. Contrarily to conventional direct torque and flux control, in the proposed method, the torque and the angle between stator current and rotor flux are controlled. The fluxes are not directly controlled. This method increases the efficiency of the motor in light loads without using online optimum flux calculation. The angle control has become feasible by using FCS-MPC without imposing time-consuming calculations. Closed-loop prediction model is used to increase the precision of the angle prediction. A novel and simple gain calculation is also proposed. The proposed method has been evaluated by both simulations and experiments. [ABSTRACT FROM PUBLISHER]

Voltage model observer is a simple and economical technique for flux estimation in induction motor sensorless drives. However, it shows poor performance in low-speed regions. Therefore, in most cases, the use of this observer is limited. On the other hand, using a simple but accurate estimator is important when the control method is sophisticated and requires heavy computation. This issue will be important in predictive control more than the other methods because the accuracy of the prediction is dependent on the flux estimation. In this paper, a modified closed-loop technique based on voltage model observer is proposed for flux estimation. The feedback loop is supported by the proposed model reference adaptive system direct flux magnitude estimation technique. The dependence of the feedback loop on the stator resistance is eliminated. Therefore, the drift error will be avoided. This will allow the method to withstand the high stator resistance error even at low speeds. Also, a new Lyapunov-based technique for the stator resistance estimation via reduced-order model is proposed. By using the proposed observer, the predictive direct voltage control technique is used as the control method in order to achieve a control method that requires low computation. The proposed method is validated through the experimental results. [ABSTRACT FROM AUTHOR]

This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise. [ABSTRACT FROM AUTHOR]

The finite control state predictive torque control (FCS-PTC) method is known to produce a fast response. However, when compared with the direct torque control (DTC) method, which is inherently encoderless, the FCS-PTC method has a disadvantage because of its speed dependence. An encoderless FCS-PTC method that is based on a revised prediction model for an induction machine is proposed and experimentally verified in this paper. To observe the unmeasured variables, an encoderless full-order sliding-mode observer (EFOSMO) is applied. Neither the revised prediction model nor the EFOSMO uses the estimated rotor speed value. The disturbance injected by process of speed estimation in the usual encoderless predictive methods is removed from the system. The experimental results show that the proposed algorithm can work well and achieve good performance at a very wide range of speeds. [ABSTRACT FROM PUBLISHER]

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, FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulsewidth modulation and inner proportion-integration 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 predefined 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 initial boundaries. The proposed PPTC is compared with the state-of-the-art predictive torque control (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. [ABSTRACT FROM AUTHOR]