On Continuous-Set Model Predictive Control of Permanent Magnet Synchronous Machines

Hammoud, Issa; Hentzelt, Sebastian; Xu, Ke; Oehlschlaegel, Thimo; Abdelrahem, Mohamed; Hackl, Christoph M.; Kennel, Ralph

doi:10.1109/tpel.2022.3164968

Cited by 28 publications

(15 citation statements)

References 47 publications

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“…In this case, the predictive-repetitive controller is used to track an input reference signal, therefore, this reference signal should enter into the augmented state-space model computation through the output variables in the expanded state-space vector x(k i ), the reason is that the controller will take the states of the expanded model to zero, so when the state is the error between the reference and the output signal the stationary error zero is obtained. So, as (19) this vector includes the filtered state vector x s and the output variables y for each sampling time in the past.…”

Section: A Predictive-repetitive Controller (Prc) Design For the Dfigmentioning

confidence: 99%

“…There are countless predictive control techniques for DFIG reported in specialized journals, for instance, generalized predictive control (GPC), in this one, a transfer function is used as a system model to obtain the corresponding control law after minimization of a quadratic cost function [13,14]. MBPCs with nonlinear models [15,16], predictive control in continuous time [17][18][19] or the finite control set [2,20,21], and others [22], are interesting approaches of this kind of control theory for the control of DFIG. Of all the diversity of predictive controllers, the linear state-space model presents some advantages such as a low computational cost when it is compared with other nonlinear models, for instance, its ability to deal with coupling components and flux components in a direct way, among others [23,24].…”

Section: Introductionmentioning

confidence: 99%

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Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

Lunardi

Chaves

et al. 2022

Energies

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This paper shows an increase in the robustness of a DFIG-based wind energy system when a predictive-repetitive controller in the dq reference frame is used for the control of the rotor current. This designing approach proposes a model predictive control with a receding horizon technique that uses the frequency decomposition of the reference signal as a filter. Furthermore, an augmented space-state model from the original plant model is obtained, and its implementation is addressed from the perspective of normal operating conditions of DFIG in wind energy systems. Additionally, this repetitive controller increases the robustness of the predictive control when a mismatch in machine parameters is considered. Experimental results presented in this paper endorse the advantages of this controller.

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Section: A Predictive-repetitive Controller (Prc) Design For the Dfigmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

Lunardi

Chaves

et al. 2022

Energies

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“…Model predictive control (MPC) has advantages such as good control effect and high current tracking accuracy, and is widely used in the field of motor control [3, 4]. MPC can be divided into Continuous Control Set Model Predictive Control (CCS‐MPC) [5, 6] and Finite Control Set Model Predictive Control (FCS‐MPC) [7, 8]. Although CCS‐MPC has better robustness when parameters change, it has drawbacks such as high computational complexity and slow response [7].…”

Section: Introductionmentioning

confidence: 99%

“…Although CCS‐MPC has better robustness when parameters change, it has drawbacks such as high computational complexity and slow response [7]. CCS‐MPC requires SVPWM, which results in high switch losses [8]. FCS‐MPC has a faster response by minimizing the cost function to directly select the corresponding switch state, but the switching frequency is not fixed and the current ripple is larger [11].…”

Section: Introductionmentioning

confidence: 99%

Three‐vector model‐free predictive control for permanent magnet synchronous motor

Wen,

Zhang,

Zhang

2023

IET Power Electronics

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A three‐vector model‐free predictive control (TV‐MFPC) strategy is proposed to address the problem of model predictive control systems performance degradation caused by parameter mismatch and inverter dead‐zone. Firstly, an ultra‐local model is constructed for parameter mismatch and dead‐zone. Then, to improve the adaptive capability of the observer to parameter variations, sliding mode control is introduced, and an Improved Extended State Observer (IESO) is designed to estimate unknown disturbances in the ultra‐local model and provide feedforward compensation for the current loop. Finally, the proposed strategy is experimentally validated, and the results demonstrate that it reduces dependence on the model, suppresses the phase current distortion caused by dead‐zone, and improves the robustness of the control system.

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“…Traditional control strategies are difficult to achieve the stability of the drive system by directly controlling the switching sequence of the inverter to effectively control the front‐stage dc‐link voltage. The cost function of model predictive control (MPC) has the characteristics of flexibility and diversity, 15 and the direct control of the dc‐link voltage can be realized by constructing a predictive model 16 . In Smidl et al, 17 based on the idea of two‐stage predictive control, a linear quadratic cost function controller was proposed to control the damping of the filter and to optimize the operating characteristics of the PMSM through finite control set model predictive control (FCS‐MPC).…”

Section: Introductionmentioning

confidence: 99%

DC‐link voltage control of small capacitor PMSM drive system based on MPC algorithm

Song

Lin

2023

Circuit Theory & Apps

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SummaryThe permanent magnet synchronous motor (PMSM) drive system with small dc‐link capacitor will have the problem of unstable drive system due to the small damping of the LC filter and the negative impedance characteristics of the constant power load. The input LC filter can suppress interference and is essential for the normal operation of the drive, but it may not match the impedance of the post‐stage inverter, causing the drive system to become unstable. In this paper, by constructing the dc‐link voltage prediction model, combined with the PMSM current predictive control, the weighted cost function of the model predictive control (MPC) strategy is designed to realize the motor optimal control and the dc‐link voltage fluctuation suppression control. To avoid introducing additional sensors when constructing the dc‐link voltage model, a source state observer that can estimate the dc‐link voltage and input current is constructed. Simulation and experimental results are provided to verify the effectiveness of the proposed method.

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On Continuous-Set Model Predictive Control of Permanent Magnet Synchronous Machines

Cited by 28 publications

References 47 publications

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

Improvement of Robustness of MPC Adding Repetitive Behavior for the DFIG Current Control

Three‐vector model‐free predictive control for permanent magnet synchronous motor

DC‐link voltage control of small capacitor PMSM drive system based on MPC algorithm

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