A Novel Finite-Control-Set Model Predictive Current Control for Five-Phase PM Motor With Continued Modulation

Zhao, Wenxiang; Tao, Tao; Zhu, Jihong; Tan, Huajun; Du, Yuxuan

doi:10.1109/tpel.2019.2954285

Cited by 46 publications

(10 citation statements)

References 27 publications

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“…In [10], an improved five-level bidirectional converter based on FCS-MPC was presented, which consisted in using the discrete time nature of the improved five-level bidirectional converter to define its state in each sampling interval. In response to the problem of the conventional five-phase FCS-MPC suffers from heavy computational burden, a novel FCS Model Predictive Current Control (MPCC) with continued modulation was proposed in [11]. In [12], a simple Model Predictive Direct Power Control (MP-DPC) of single-phase pulse width-modulated rectifiers with constant switching frequency using modulation function optimization was proposed.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Model Predictive Control Method for Five-Level Active NPC Inverter

Zhan

Xia

et al. 2020

IEEE Access

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Finite Control Set Model Predictive Control (FCS-MPC) can improve the control performance of Five-level Active NPC (5L-ANPC) inverter effectively. However, with the increasing of the levels of inverter, the traditional FCS-MPC method has many problems, such as large amount of rolling optimization calculation and difficulty in weight coefficient design. In order to solve those problems, an Optimal Model Predictive Control (O-MPC) method is proposed. First, an optimal control method based on level jump limitation is proposed to reduce the number of switch states. Second, in order to solve the conflict problem of multiple control objectives and reduce the design difficulty of weight factors, a satisfactory optimal control method based on hierarchy is proposed. After the optimization, the calculation time of the controller is greatly reduced and the control performance of the multi-objective control system will be significantly improved. At last, the effectiveness and feasibility of the proposed O-MPC are validated by the experimental results.INDEX TERMS Five-level active NPC (5L-ANPC) inverter, an Optimal Model Predictive Control (O-MPC) method, switching states, satisfactory optimal control.

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Section: Introductionmentioning

confidence: 99%

An Optimal Model Predictive Control Method for Five-Level Active NPC Inverter

Zhan

Xia

et al. 2020

IEEE Access

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“…So far, the new DPPM based on tripartite predictions is established. It needs to be mentioned that in addition to Equation (14), the candidate voltage vector (10) and position expression (11) are parts of the model as well.…”

Section: ) New Dppm For Lsf Applicationsmentioning

confidence: 99%

“…In order to predict the future states of the currents using (14), as is shown in Figure 3, the input of the controller must contain the present currents i sM (k) and i sT (k), control voltages, synchronous speed ω e (k), slip speed △ω(k) and rotor speed ω r (k). Besides, because the real-time flux position needs to be used to calculate the M, T-axis control voltages, θ(k) should be the input of the controller definitely.…”

Section: ) Input and Output Of Controllermentioning

confidence: 99%

“…Step 2) Synchronous speed and position observation: Use the measured states and (14) to observe the slip speed △ω(k) and synchronous peed ω e (k).…”

Section: ) Implementation Proceduresmentioning

confidence: 99%

“…Finite control set (FCS) model predictive control (MPC), which is endowed with remarkable simplicity and strong constraint capability, has been widely used in different electric powertrains [11][12][13][14]. Without adopting any modulators for pulse width modulation (PWM) signal generation, FCS-MPC directly uses the output of the controller to determine the optimal switching state [15].…”

Section: Introductionmentioning

confidence: 99%

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Low-Switching-Loss Finite Control Set Model Predictive Current Control for IMs Considering Rotor-Related Inductance Mismatch

Tan

et al. 2020

IEEE Access

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This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the lowswitching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations. INDEX TERMS Induction motor, finite control set model predictive control, sliding mode observer, parameter mismatch, low switching frequency.

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