Simplified Model Predictive Torque Control for Permanent Magnet Synchronous Motor in Static Coordinate System

Due to the application of only one voltage vector in a control period, conventional model predictive torque control (MPTC) suffers from relatively large steady‐state ripples. The‐state‐of‐the‐art double‐vector‐based MPTC (DVMPTC) was proposed to improve steady‐state performance. However, its duty cycle optimization method significantly affects the control performances. Thus, MPTC with hybrid mode duty cycle optimization is proposed, which realizes better performance than the universal DVMPTC, and obtains the appropriate average switching frequency, especially at high‐speed domain. The proposed method utilises the universal DVMPTC method, while it equips duty cycle optimization with discrete space vector modulation (DSVM) scheme as the additional constraint for further reducing the reference voltage tracking error that exists in DVMPTC. What's more, rotation module and pre‐selection strategies are involved in simplifying the design of vector selection methods. Comparative simulations and experiments verify the effectiveness of the proposed method.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

This paper proposes an improved model predictive torque control (MPTC) with duty ratio optimization for three-phase AC permanent magnet synchronous motor (PMSM) drives. By predicting the evolution of variables concerned using system model, the best voltage vector satisfying the torque and flux command can be selected. As a result, MPC has a fast dynamic response and good steady-state response. Despite the merits above, the traditional MPC still presents some torque and flux ripples due to the use of single voltage vector during the whole control period. To further improve the steady state performance of conventional MPTC, this paper introduces the concept of duty cycle optimization in MPTC by dividing the control period into two intervals. The non-zero vector obtained from conventional MPTC is applied for a fraction of control period based on the principle of torque ripple minimization, while the rest of time is allocated for a null vector. Simulation results are presented to validate the effectiveness of this method.

The conventional model predictive torque control (MPTC) for induction motor drives evaluates the electromagnetic torque and stator flux linkage in the cost function. However, in the speed outer loop design, the torque reference in the cost function generally utilizes a classic proportional-integral (PI) controller, which is not only dependent on motor parameters but also sensitive to the change of the load torque. In this article, an adaptive sliding-mode-control-based MPTC (ASMC-MPTC) method is proposed to improve the robustness of the finite control set model predictive control (FCS-MPC). First, the influence of the mismatched parameters for FCS-MPTC is introduced and the shortcoming of the PI-based MPTC (PI-MPTC) is analyzed. Then, a sliding-mode-control-based MPTC (SMC-MPTC) is studied, in which the exponential reaching law is introduced to control the trajectories of the state variables. To further improve the control performance of SMC-MPTC, the exponential reaching law is optimized to adjust the switching gain adaptively, thus the ASMC-MPTC is proposed. The PI-MPTC, SMC-MPTC, and ASMC-MPTC are compared theoretically and experimentally, and the results show that the proposed ASMC-MPTC has the best disturbance rejection ability and robustness against motor parameters variation and load torque change.

This article proposes a new model predictive torque control (MPTC) for induction motors to improve its robustness against parameter mismatching when current model (CM) for flux estimation is employed. The main advantages of the proposed method are the improved range of stable operation during the occurrence of mutual inductance Lm, mismatched value, lower computational burden, and reduced switching losses. The improvement in robustness is achieved via adaptation of direct torque control (DTC) features thus eliminating any additional parameter estimation mechanism. Reference transformation is used to remove torque error from the cost function to achieve weighting-factor-free MPTC formulation. Then, based on the estimated and reference stator flux vector positions, it is established that flux positional error is directly proportional to the torque error. The positional error is then employed to reduce the admissible voltage vectors, which decreases parameter dependence of MPTC. The simulation results are compared to two other well-established MPTC techniques and based on the results, it is concluded that the proposed MPTC without parameter estimation and compensation technique have a wider tolerability of 56% for Lm mismatching.

In this study, the model predictive control (MPC) method was used within the scope of the control of the permanent magnet synchronous motor (PMSM). The strongest aspect of the MPC, the ability to control multiple components with a single function, is also one of the most difficult parts of its design. The fact that each component of the function has different effects requires assigning different weight coefficients to these components. In this study, the Bees Algorithm (BA) is used to determine the weights. Using the multi-objective function in BA, it has been tried to determine the weights that reduce the current values together with the speed error. Three different PI controllers have been designed to compare the MPC method. The coefficients of one of these are tuned with BA. Good Gain Method and Tyreus-Luyben Method were used in the other two. As a result of experimental studies, it has been observed that MPC can control PMSM more smoothly and accurately than PI controllers, with weights optimized with BA. With MPC, PMSM has been controlled with 15% settling time than other controllers and also with no overshoot.

Vector control has been widely used to manipulate the permanent magnet synchronous motor (PMSM), in which the traditional double proportional integral (PI) controllers have commonly been utilized. However, it is difficult to set the suitable parameters of PI and maintain the PMSM performance under the different operating regimes. In order to solve these problems, a double closed-loop model predictive control (MPC) strategy based on the vector control has been presented for PMSM in this paper. Firstly, a simplified discrete model of PMSM is established in the d-q coordinate system without considering the interference. Then, the MPC is developed to substitute the PI controller in the speed-loop and the q-axis current-loop, respectively, and a double closed-loop MPC control system is formed. Finally, some case studies are carried out. The simulation and experimental results demonstrate that the presented MPC strategy improves the dynamic and steady-state performance of PMSM.

Conventional model predictive control for permanent magnet synchronous motor (PMSM) drives often introduces a weighting factor for lowering the switching frequency, which unfortunately leads to the tedious tuning works on weighting factors. Moreover, the computational burden is quite heavy since all the voltage candidates have to be enumerated. In this article, a cascaded band based model predictive current control is proposed, which can reduce the switching frequency without the use of weighting factor as well as alleviate the computational burden. In this method, the latest applied voltage vector will be adopted to predict the current error prior to the iterative prediction process. Next, a band is defined to compare with the precalculated current error to determine whether it is to be maintained. As a result, the iterative cost function minimization turns to be an event triggered process. In addition, the hard switch constraints for the candidate voltage vectors are developed to combine with the cascaded band, which can effectively reduce the iterations and avoid the suboptimum. The effectiveness of the proposed method is verified on a PMSM platform fed by three-level T-type inverter.

Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the non-convex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC.

As one of the most important power equipment in industrial society, permanent magnet synchronous motor has a large number of uses in many fields. The traditional three-phase permanent magnet synchronous motor using vector control technology cannot meet the industrial high precision control requirements. So, the motor is analysed, the permanent magnet motor control model is constructed, and the chicken cluster algorithm is used to optimize the vector control parameters of the permanent magnet synchronous motor. Considering that the traditional cluster model faces the problem of local optimal solution, hypercube sampling and particle swarm model are used to optimize the chicken flock model, and an improved chicken flock motor control model is constructed to achieve efficient control of the motor. In the test of Rastrigin function, the particle swarm model, the traditional flock model and the improved flock model converge after 91, 72 and 20 iterations, respectively, and the best objective function values of 3.91e-4, 9.27e-5 and 1.79e-5 are obtained for the three models. The improved flock model has better convergence efficiency and convergence accuracy. In the test of different motor control methods, the overall fluctuation of the improved flock model is smaller, and the stability is improved by 31.65% compared with the traditional vector control, and 11.35% compared with the traditional flock model. The research content has important reference value for intelligent control of motors.

This paper presents a simplified model predictive control (MPC) method for current control of a twelve-phase permanent magnet synchronous motor (PMSM). For the twelve-phase PMSM driven by a two-level converter, the total number of switching states is 4096. Even only considering the 24 largest voltage vectors, the computational burden is still heavy. In this paper, the 24 largest voltage vectors are divided into 6 sectors evenly. Assuming that predictive currents are equal to reference ones, then reference voltage vectors can be calculated and located in 6 sectors. Therefore, the cost function only need to evaluate 1 zero and 4 selected active switching states. The proposed method can considerably alleviate the computational cost and simultaneously ensure control performance.

The combination of direct torque control and permanent magnet synchronous motors (PMSMs) provides a highly dynamic drive. In this paper direct torque control method of PMSM is merged with a sensorless control algorithm and a robust flux observer is proposed which results in a high performance highly reliable drive. First a high frequency signal injection method within DTC algorithm is introduced to estimate the rotor position of PMSM. Then the method is modified in order to estimate stator dynamic inductances. The stator inductances are then used in a current model flux observer.

To reduce the computational burden of a conventional model predictive torque controller (MPTC), a reference voltage vector based MPTC strategy is proposed. The reference voltage vector is obtained from the reference stator flux vector and the reference torque. According to the location of the reference voltage vector, a first optimal vector can be determined in a quite straightforward way, improving the system dynamic performance. Furthermore, in order to decrease the torque and flux ripple, a root mean square (RMS) based solution is employed to generate the reference voltage vector and calculate the duty ratio. This method aims at minimizing the RMS error of flux and torque during the whole control period. Then, the steady state performance is improved. Besides, since the new cost function contains only the reference voltage vector, the weighting factor in conventional MPTC is eliminated. In addition, to keep a balance between the steady state performance and switching frequency, the candidates for the second optimal vector are restricted to a certain scope. Simulations were carried out and the results verified the validation of the proposed MPTC strategy.