As an efficient control strategy, model predictive current control (MPCC) has rapid response and simple calculation. This article proposes an improved MPCC scheme for permanent-magnet synchronous hub motor (PMSHM) drives. The mentioned control scheme uses the parameter values at the last moment to obtain the back electromotive force (EMF) and utilizes the obtained back EMF to obtain the predicted current value at the next moment. In the actual application of the motor, to enhance the robustness of the control system, a sliding mode controller is used to replace the conventional proportional-integral (PI) speed loop, and a finite position phase-locked loop based on the dichotomy is added to achieve sensorless speed control and provide an accurate rotor position angle. To improve the steady-state performance, the method of duty cycle is introduced, and the null vector and the actual vector are used together in the same control cycle. The simulation and experimental results both show the effectiveness of the proposed MPCC scheme, and the steady-state performance of MPCC is greatly improved compared with traditional MPCC.

Speed Sensorless Model Predictive Current Control Based on Finite Position Set for PMSHM Drives

Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview

This article represents modified model reference adaptive system (MRAS) method for speed sensorless control of interior permanent magnet synchronous motor (IPMSM), which can estimate rotor speed and position information. To suppress the adverse effect of parameter variation on control performance, the stator resistance and the permanent magnet flux linkage are estimated and continuously updated in reference and adjustable model. Then, proportional-integral (PI) controller parameters of speed adaptive law obtained by MRAS are optimized by gray wolf optimization (GWO) algorithm. To get the smallest possible speed following error and reference current error, the objective function is designed with discretized rotor speed error and current error as variables. The simulation results show the better performance of rotor speed estimation is obtained with GWO algorithm. 

Speed Sensorless Control for IPMSMs Using a Modified MRAS With Gray Wolf Optimization Algorithm

Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted. 

To simplify the tuning process and enhance the parameter robustness of the model predictive control (MPC) for grid-connected inverters, an improved phase-locked loop (PLL) and a new inductance online identification method are proposed in this article. First, an improved finite control set-based PLL is presented, which removes the PI controller, simplifying the tuning process. Next, a new inductance online identification method based on a model reference adaptive system is presented, which takes the actual grid voltage and the estimated one as the reference model and adjustable model, respectively. Here, a sliding mode observer (SMO) is proposed to estimate the grid voltage, and the inductance adaptive law is deduced by exploring the effects of the inductance on the estimated grid voltage. Furthermore, the sensitivity of the proposed SMO to frequency deviation is also analyzed, which shows that a proper sliding mode gain can be selected to reduce the effects of frequency deviation. Thus, a fixed grid frequency of 50 Hz can be used in the proposed SMO, indicating that it is not required to calculate the actual grid frequency using differential operation any more. Finally, experimental studies verify the validity of the proposed strategies.

An Inductance Online Identification-Based Model Predictive Control Method for Grid-Connected Inverters With an Improved Phase-Locked Loop

This article presents a sensorless control for the permanent-magnet linear synchronous motor (PMLSM) under the energy-feeding mode in vehicle regenerative suspension. The proposed method is based on a multiple-iterative search algorithm to obtain a limited quantity of mover position signals (electrical angle) precisely, which are utilized to observe the $d$ -axis back electromotive force (EMF). Then, the optimal mover position signal selected from the estimated mover position is restrained by the cost function applied in the multiple-iteration search algorithm. The accuracy of the mover position signal increases geometrically in the effect of an increase in the iterations. The proposed search algorithm is superior to the existing search algorithm in the terms of the computational burden. Different from the conventional phase-locked loop (PLL), the new strategy removes the need for a proportional–integral controller. The proposed control method and the conventional control method are experimentally tested under different conditions. Results prove that the proposed PLL has the effectiveness to realize the estimated parameters corresponding to the actual parameters.

Multiple-Iteration Search Sensorless Control for Linear Motor in Vehicle Regenerative Suspension

As the core mechanism of active suspension, the control strategy of linear motor actuator is the key factor to affect ride comfort. This paper presents a model predictive thrust force control (MPTFC) method with switching frequency term in the evaluation function for linear motor applied in the control system of vehicle active suspension. MPTFC selects seven voltage vectors that the inverter produces into the motor predictive model. The motor operating status parameters of the control cycle are obtained with the effect of different voltage vectors. Based on the evaluation function to select the optimal voltage vector, the optimal vector is used for motor in the next control period. The sky-hook controller of active suspension system and the model predictive thrust controller of linear motor are consisted of the control system of the active suspension. A two-degree-of-freedom quarter-car model with active suspension system was established. The MPTFC and the direct thrust force control (DTFC) for linear motor are experimentally tested by dSPACE. Results prove that MPTFC has faster dynamic response of speed, less thrust force and stator flux linkage ripple, which results in the desired electromagnetic force according to the vibration of the bodywork.

Shaohua Wang

Papers

Multiple-Iteration Search Sensorless Control for Linear Motor in Vehicle Regenerative Suspension

Model Predictive Thrust Force Control for Linear Motor Actuator used in Active Suspension