Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

Wei, Yao; Young, Héctor; Wang, Fengxiang; Rodríguez, José

doi:10.1109/tie.2022.3210563

Cited by 35 publications

(7 citation statements)

References 31 publications

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“…To address the parameter issue related to the conventional MPCC, the ultra-local model is applied to replace the actual mathematical model [15]. The modeling part is calculated with the input and output in real time, which is expressed as .…”

Section: Ultra-local Modelmentioning

confidence: 99%

“…Existing methods are categorized into three main groups according to the extent to which they are model-free, namely, totally model-free, using an ultra-local model, and prediction correction [14]. The first method is carried out by using the input and output information of systems without any model for prediction [15,16]. The second method uses a model with one or more uncertain terms that should be estimated continually via the input and output data of systems [17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Model-Free Predictive Current Control of Five-Phase PMSM Drives

Huang,

2023

Electronics

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Model predictive control is highly dependent on accurate models and the parameters of electric motor drives. Multiphase permanent magnet synchronous motors (PMSMs) contain nonlinear parameters and mutual cross-coupling dynamics, resulting in challenges in modeling and parameter acquisition. To lessen the parameter dependence of current predictions, a model-free predictive current control (MFPCC) strategy based on an ultra-local model and motor outputs is proposed for five-phase PMSM drives. The ultra-local model is constructed according to the differential equation of current. The inherent relation between the parameters in the predictive current model and the ultra-local model is analyzed in detail. The unknowns of the ultra-local model are estimated using the motor current and voltage at different time instants without requiring motor parameters or observers. Moreover, space vector modulation technology is employed to minimize the voltage tracking error. Finally, simulations and experiments are conducted to verify the effectiveness of the MFPCC with space vector modulation. The results confirm that the proposed method can effectively eliminate the impact of motor parameters and improve steady-state performance. Moreover, this control strategy demonstrates good robustness against load variations.

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Section: Ultra-local Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Huang,

2023

Electronics

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“…[8], a DTC method has been applied to minimise the torque ripple and iron loss of the BLDC motor. Another direct switching strategy is the finite-control-set model predictive control (FCS-MPC), which has become a mainstream control method in the last decade due to its outstanding features such as fast dynamics, easy implementation, and multi-variable control ability [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Smooth commutation of brushless DC motor by model‐free predictive current control without back‐EMF estimation

Moradpour

Tavakoli

2023

IET Electric Power Appl

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This study presents a new predictive control approach for brushless DC (BLDC) motor to smooth its commutation process. The proposed predictive control method is applied to the BLDC motor's equivalent DC current. Therefore, this method utilises only one prediction equation, which means less calculation than the standard predictive control schemes. Here, the equivalent DC current equation is defined according to the model‐free control theory to remove the back‐EMF voltage estimation from the control system. In consequence, the proposed predictive DC current control (DC‐PCC) has a smooth commutation. Because of applying the model‐free theory, both commutation and non‐commutation periods of the BLDC motor have similar dynamic equations. Hence, one simple prediction equation can be utilised for both periods. Besides, the proposed method has less computational burden due to using only one prediction equation and removing the back‐EMF estimation process. To validate the proposed model‐free DC‐PCC, experimental tests are performed, and the results are discussed in this research.

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“…Commonly used robust schemes can be divided into prediction model improvement, parameter identification, and observer-based estimation compensation schemes [39]. Predictive model improvement solutions such as driver solutions require a large amount of data training and have high data quality requirements [40][41][42][43]. Parameter identification often requires the identification of multiple parameters, which increases the complexity of the system [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Improved model predictive control of permanent magnet synchronous motor based on lumped disturbance compensation

Huangfu,

Cen,

Zhang

2023

Preprint

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This paper proposes an improved finite control set model predictive current control (FCS-MPC) for permanent magnet synchronous motor (PMSM), including the use of a sliding mode observer for full electromagnetic parameter disturbance compensation and duty cycle control. First, a PMSM prediction model containing lumped disturbances was established. Second, a sliding mode observer is designed to estimate the lumped perturbation and compensate it to the reference voltage. The reference voltage is obtained based on the deadbeat principle and requires further constraints to obtain the optimal basic voltage vector. Then, a voltage-based cost function is constructed to calculate the duration of the non-zero optimal voltage vector and output the amplitude of the duty cycle adjustment voltage. Finally, experiments verify the effectiveness of the proposed FCS-MPC in improving robustness and steady-state performance.

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Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

Cited by 35 publications

References 31 publications

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Model-Free Predictive Current Control of Five-Phase PMSM Drives

Smooth commutation of brushless DC motor by model‐free predictive current control without back‐EMF estimation

Improved model predictive control of permanent magnet synchronous motor based on lumped disturbance compensation

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