Assessing Finite-Control-Set Model Predictive Control: A Comparison with a Linear Current Controller in Two-Level Voltage Source Inverters

Young, Héctor; Pérez, Marcelo A.; Rodríguez, José; Abu‐Rub, Haitham

doi:10.1109/mie.2013.2294870

Cited by 196 publications

(107 citation statements)

References 26 publications

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“…The steady-state performance offered by the classical and predictive control techniques is almost similar. However, the latter approach provides a superior and dynamic performance because of the elimination of internal current loops and modulators [25], [28], [34], [41]. The comprehensive comparison and analysis between the classical and the proposed two-step FCS-MPC are not covered in the scope of this study.…”

Section: Predictive Variables With Two-step Horizonmentioning

confidence: 99%

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Finite State Model-based Predictive Current Control with Two-step Horizon for Four-leg NPC Converters

Yaramasu

Rivera

Narimani

et al. 2014

Journal of Power Electronics

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This study proposes a finite-state model predictive controller to regulate the load current and balance the DC-link capacitor voltages of a four-leg neutral-point-clamped converter. The discrete-time model of the converter, DC-link, inductive filter, and load is used to predict the future behavior of the load currents and the DC-link capacitor voltages for all possible switching states. The switching state that minimizes the cost function is selected and directly applied to the converter. The cost function is defined to minimize the error between the predicted load currents and their references, as well as to balance the DC-link capacitor voltages. Moreover, the current regulation performance is improved by using a two-step prediction horizon. The feasibility of the proposed predictive control scheme for different references and loads is verified through real-time implementation on the basis of dSPACEDS1103.

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Section: Predictive Variables With Two-step Horizonmentioning

confidence: 99%

“…Accordingly, available digital implementation platforms, such as microprocessors, digital signal processors, and field-programmable gate arrays, can handle such computations. Recent scholarly works demonstrate the easy application of the FCS-MPC in various power converters [25]- [34].…”

Section: Introductionmentioning

confidence: 99%

Finite State Model-based Predictive Current Control with Two-step Horizon for Four-leg NPC Converters

Yaramasu

Rivera

Narimani

et al. 2014

Journal of Power Electronics

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“…Among various motor types, the synchronous reluctance motors (SynRMs) have been widely used in washing machines, air conditioners, and electric vehicles thanks to their robustness, simple structure, and without use of magnetic materials. In order to achieve high performance so as to save energy/power under different operation conditions, many current control methods have been developed in the past, such as hysteresis current control [2], pulse width modulation (PWM) [3], space vector PWM (SVPWM) [4], and model predictive current control (MPCC) [5,6]. The first two methods, as limited by their nature, cannot predict future current in next sampling period.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems

Lin

Huang

et al. 2018

Energies

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For current control in power conversion and motor drive systems, there exist three classic methods in the literature and they are the hysteresis current control (HCC), the sine pulse-width modulation (SPWM), and the space vector pulse width modulation (SVPWM). HCC is easy to implement, but has relatively large current harmonic distortion as the disadvantage. On the other hand, the SPWM and SVPWM use modulation technique, commonly together with at least one proportional-integral (PI) regulator to reduce load current ripples, and hence demanding more computation time. This paper aims to improve the performance of a recently proposed new current control method-the single-voltage-vector model predictive current control (SVV-MPCC), for synchronous reluctance motor (SynRMs) drives. To that end, a dual-voltage-vector model-free predictive current control (DVV-MFPCC) for SynRMs is proposed. Unlike the SVV-MPCC that applies only a single voltage vector per sampling period, the proposed DVV-MFPCC is capable of providing two successive segmentary current predictions in the next sampling period through all possible combinations from any two candidate switching states increasing the number of applicable switching modes from seven to nineteen and reducing the prediction error effectively. Moreover, the new control does not utilize any parameters of the SynRM nor its mathematical model. The performance is effectively enhanced compared to that of SVV-MPCC. The working principle of the DVV-MFPCC will be detailed in this paper. Finally, the SVV-MPCC, the single-voltage-vector model-free predictive current control (SVV-MFPCC), the dual-voltage-vector model predictive current control (DVV-MPCC), and the DVV-MFPCC are realized to control the stator currents of SynRM through a 32-bit microcontroller TMS320F28377S. Experimental results are provided to validate the new method and verify that the DVV-MFPCC performs better than do the SVV-MPCC, the SVV-MFPCC, and the DVV-MPCC.

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“…This is achieved minimizing a cost function inside a feasible region [1], [2]. That is, MPC computes optimal control inputs so that physical and operational constraints of the system to be controlled are fulfilled.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

Tarisciotti

Calzo

Gaeta

et al. 2016

IEEE Trans. Ind. Electron.

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Abstract-In recent years Model Predictive Control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel Distributed Model Predictive Control, which is able to achieve the same performance of the classical Model Predictive Control whilst reducing the computational requirements of its implementation. The proposed control approach is tested on a AC/AC converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions. Index Terms-Predictive control, Nonlinear control systems, Back-to-back converters.

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Assessing Finite-Control-Set Model Predictive Control: A Comparison with a Linear Current Controller in Two-Level Voltage Source Inverters

Cited by 196 publications

References 26 publications

Finite State Model-based Predictive Current Control with Two-step Horizon for Four-leg NPC Converters

Finite State Model-based Predictive Current Control with Two-step Horizon for Four-leg NPC Converters

A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems

A Distributed Model Predictive Control Strategy for Back-to-Back Converters

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