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A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the current control steady‐state error caused by sampling delay, parameters mismatch of direct‐drive permanent magnet synchronous motor (PMSM). This paper proposed a method of dual model predictive control (MPC) of direct‐drive PMSM based on sliding mode disturbance observer (SMDO). Deadbeat predictive current control (DPCC) is used for current loop, a SMDO is proposed to observe the system disturbance, the estimation is compensated to DPCC by feedback to decrease the current control steady‐state error. For speed loop, MPC with an internal model control observer (IMCO) can observe and compensate the load disturbance feedforward. The experimental results show that, the dynamic response speed of the proposed strategy is fast, the speed recovery time after sudden load torque is short, and the anti‐load disturbance performance and current tracking performance are effectively improved. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The highway cycle is an important consideration in the EV's new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM's efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed. [ABSTRACT FROM AUTHOR]

In order to optimize the control performance of permanent magnet synchronous motor (PMSM) servo system, an improved model predictive control (MPC) scheme based on neural network is investigated in this paper. First, the dynamic characteristics of the PMSM are approximated by echo state network (ESN) to predict the future speed. Particle swarm optimization (PSO) is used to train ESN output weights to solve the problem that instability of output weights caused by pseudo-inverse matrix in ESN weight solving algorithm, called PSO-ESN, which enhances the stability and the accuracy of ESN speed prediction. That provides future plant output for control optimization of the predictive control. Furthermore, in order to reduce the computational cost and improve the response performance of the controller, a fast gradient method (GM) is applied to minimize the quadratic performance index and solve the optimal control input sequences. The simulation results under three different working conditions show that the PSO-ESNMPC controller designed in this paper reduces the overshoot by 5.87% and the rise time by 0.036 s compared with the reference controllers and has better robustness under parameter changes and load disturbances. [ABSTRACT FROM AUTHOR]

In this paper, an extended-horizon predictive torque control (PTC) method is proposed with computationally efficient implementation. Predictive control with extended horizon brings important advantages compared to single-horizon methods, such as better steady-state performance, reduced current THD, and lower ripples in torque and stator flux. However, the computational burden is a serious obstacle because the required calculations rise exponentially with the extension of the prediction horizon. This is due to a high number of candidate voltage vectors and also predicting the values of several machine variables for these vectors. Different approaches are employed in this work to make the control method computationally tractable. A voltage vector reduction technique is utilised that significantly decreases the total number of enumerated vectors. Moreover, the stator current prediction is eliminated in the proposed method, based on the inherent features of the PMSM. The proposed method is experimentally implemented and its good performance and advantages are verified. [ABSTRACT FROM AUTHOR]

Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy electric vehicles and in other fields because of their simple structure, light weight, small size, and high power density. With the continuous advancement of production technology, the requirements of accuracy, rapidity, and stability in permanent magnet synchronous motor systems have gradually increased. Among many advanced control technologies, this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion. This strategy involves the construction of a reference stator flux linkage vector based on the analytical relationship between electromagnetic torque, reference stator flux linkage amplitude, and rotor flux linkage and the transfer of the separate control of electromagnetic torque and flux linkage amplitude into flux linkage vector control. At the same time, the optimal duty cycle corresponding to the two adjacent extended voltage vectors and the zero vector is calculated according to geometric relationships so as to realize the three voltage vector duty cycle optimization control. Experimental results show the effectiveness and superiority of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper investigates minimization of torque ripples in permanent magnet synchronous motor (PMSM) drive and presents the design and implementation of a proportional-plus-integral resonant (PI-RES) controller-based model predictive control (MPC) of PMSM drive. In MPC, the machine model is used to predict the possible actions of control variables in the time domain and the control operation is then selected based on the optimization of the objective function. In the proposed technique, an objective function is defined by the error between reference current and predicted currents in d–q axis. The objective function is minimized using the MATLAB/Simulink minimum function block for each voltage vector for each sampling interval and provides optimal switching states to control VSI. The PI-RES controller is designed by connecting a variable frequency resonant controller in parallel with a conventional proportional-plus-integral (PI) controller; and is implemented to produce the reference current for MPC of PMSM. The resonant controller generates the compensating torque current, while the PI controller delivers the reference current and establishes the reference torque current. A comparative study of the MPC-based PMSM drive using the classical PI controller and PI-RES controller has been done and the results show the ripples in motor torque are 27.2% and 9.1%, respectively. Simulation analysis shows THD of stator currents are 4.52% & 3.85% at 11 Nm load and 6.53% & 4.4% at load of 5.5 Nm using PI and PI-RES controllers, respectively. Experimental analysis shows that THD of stator currents are 3.10% & 2.29% at 11 Nm load and 8.13% & 5.73% at a load of 5.5 Nm using PI and PI-RES controllers, respectively. The proposed technique shows superior performance over the classical PI controller-based MPC of PMSM drive. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification. [ABSTRACT FROM AUTHOR]

Finite control set model predictive current control (FCS-MPCC) has attracted the attention of a large number of researchers due to its intuitive idea, fast dynamic response and control objective flexibility. However, FCS-MPCC has a high steady-state current ripple. Three-vector-based model predictive current control (TV-MPCC) is an effective method to improve steady-state performance. However, it results in high motor parameter dependency. In this paper, the effect of motor parameters mismatch in FCS-MPCC has been analyzed. The control performance of FCS-MPCC is impacted when the motor parameters are mismatched. Then based on the disturbance estimation technique, a disturbance feedforward compensation based generalized proportional integral observer is used to reduce the disturbances and improves the robustness performance of the system. Experimental results show that the proposed method effectively improve the robustness of the system. [ABSTRACT FROM AUTHOR]

Duty cycle model predictive torque control (Duty-MPTC) only uses zero vector as the second vector to form vector combinations, which restricts its torque control performance. This paper proposes a modified double vectors model predictive torque control (DVMPTC) by reasonably selecting active vector instead of zero vector. By analyzing the regulation of torque and flux of permanent magnet synchronous motor (PMSM), the torque and flux difference parameters are obtained, and then a second vector selection table is established to determine the best vector combination for torque ripple suppression. Based on the torque and flux difference parameters, the duty cycle calculation method is proposed to reduce the calculation burden of the control system. In order to verify the effectiveness of the proposed method, experiments with a 6-kW PMSM are carried out. Compared to the Duty-MPTC, the proposed DVMPTC method can significantly reduce the torque ripples with good dynamic performance and relatively low average switching frequency. [ABSTRACT FROM AUTHOR]

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]