Model Predictive Current Control Strategy Research Articles

Optimal Switching Sequence MPC for Four-Leg Two-Level Grid-Connected Converters

This paper presents a versatile and effective model predictive current control strategy based on optimal switching sequences for four–leg two–level grid–connected converters. The proposed control methodology, presents an excellent dynamic performance, good tracking of high-frequency reference signals, fixed switching frequency, and well-formed harmonic spectrum, enabling the proposed controller to govern shunt active filters and distributed generation applications based on four-leg converters. Experimental results demonstrate the effectiveness and high-quality performance obtained with the proposed strategy.

Design and control of utility grid-tied bipolar DC microgrid

Abstract This paper explains in detail the design and control of a utility grid-connected bipolar DC microgrid, which consists of a solar photovoltaic system (SPV), a wind energy conversion system (WECS), a battery energy storage system (BESS) at the DC bus, and a three-level neutral point clamped (NPC) converter to connect the DC microgrid to the utility grid. A three-level bidirectional DC–DC converter at the battery energy storage system (BESS) is controlled with model predictive current control (MPCC), and a three-level NPC converter is controlled with a voltage-oriented control based proportional resonant (VOC-PR) controller. The proposed MPCC scheme for the three-level bidirectional DC–DC converter and the VOC-PR control scheme for the three-level NPC converter coordinate with each other to balance and regulate the voltage at the bipolar DC microgrid, control the power flow to the loads at the DC microgrid and the load at the point of common connection (PCC), and also inject or draw power from the utility grid and BESS based on the availability of Renewable Energy Sources (RES). Simulation and experimental results are presented to demonstrate the effectiveness of the proposed MPCC control scheme.

Three-Vector Model Predictive Current Control Strategy of PMSM for Marine Electric Propulsion Based on Three-Level Inverter

Three-Vector Model Predictive Current Control Strategy of PMSM for Marine Electric Propulsion Based on Three-Level Inverter

Optimized FCS-MPCC based on disturbance feedback rejection for IPMSMs under demagnetization fault in high-speed trains

This paper proposes an optimized finite control set model predictive current control (FCS-MPCC) strategy based on disturbance feedback rejection control (DFRC) method for interior permanent magnet synchronous motors (IPMSM) employed in high-speed trains. The strategy aims to mitigate the impact of parameter mismatch caused by permanent magnet demagnetization faults. Firstly, a discrete predicting model is established for IPMSMs, considering the mismatch in flux linkage, resistance, and inductance. The analysis is conducted to evaluate the impact of disturbances caused by demagnetization faults on the traditional FCS-MPCC. Secondly, second-order sliding mode disturbances observers (SMDOs) are utilized to detect real-time disturbances under demagnetization faults. The stability analysis of the second-order SMDOs is conducted using a Lyapunov function. Furthermore, disturbance controllers are developed to regulate the disturbances and generate compensating values for the FCS-MPCC. Lastly, experimental validation is performed on two IPMSMs to demonstrate the effectiveness of the proposed DFRC-based FCS-MPCC strategy against parameter mismatch issues caused by demagnetization faults.

A Hierarchical Control Strategy Based on Dual-Vector Model Predictive Current Control for Railway Energy Router

The multiport and multidirectional energy flow of railway energy routers (RERs) poses a significant challenge when integrating photovoltaic (PV) systems and energy storage systems (ESSs). To address this issue, this paper proposes an improved hierarchical control strategy for RERs with a reference signal generation layer and an inverter control layer. In the reference signal generation layer, a time-segmentation energy allocation strategy based on a state machine is proposed to manage the multidirectional energy flow in RERs resulting from PV systems and ESSs while minimizing peak power demand. In the inverter control layer, a dual-vector model predictive current control (MPCC) strategy is designed for back-to-back inverters. The dual-vector MPCC strategy eliminates the need for individual PWM blocks, thereby enhancing RER current-tracking accuracy and efficiency. The prominent advantage of the dual-vector MPCC strategy is its ability to achieve high current-tracking accuracy while minimizing active power losses. Simulations and hardware-in-the-loop experiments are conducted to validate the feasibility and effectiveness of the proposed method.

A Simplified Multivector-Based Model Predictive Current Control for PMSM With Enhanced Performance

To address the challenges of poor steady-state performance and large amount calculation of conventional model predictive current control (MPCC), a simplified multi-vector-based MPCC with enhanced performance is proposed in this paper. Firstly, an effective voltage vectors (VVs) selection method based on current-error is proposed, which can directly determine the optimal VVs without cost function enumeration. Compared with the standard method, the number of vectors that need to be evaluated is reduced from 7 to 1. Meanwhile, the duty cycle of each VV is calculated based on the current error to realize error-free control. In addition, to alleviate the deleterious effect of dead-time on the control performance, an improved MPCC scheme with optimal utilization of dead-time is proposed. The key is that the dead-time existing in MPCC is regarded as a dead-time voltage vector (DVV), which can be rationally utilized to improve the control performance. Both theoretical analysis and experimental results are given to verify the effectiveness of the proposed MPCC schemes.

Model Predictive Current Control Strategy for Improved Dynamic Response in Cascaded H-Bridge Multilevel Inverters

Model Predictive Current Control Strategy for Improved Dynamic Response in Cascaded H-Bridge Multilevel Inverters

Control of Three-level Four Wire Shunt Active Power Filter in Grid Connected PV-Fuel Cell System Employing Model Predictive Current Control Strategy

In this paper a Model Predictive Current Control (MPCC) of Four Wire Shunt Active Power Filter (4WSAPF) with Photo Voltaic (PV) system is proposed. The shunt active power filter requires an energy source to correct distortions in the current, and it utilizes a photovoltaic array with a DC-DC boost converter as a source of DC power. this study presents the application of a Particle Swarm Optimization (PSO) maximum power point tracking (MPPT) technique to the DC-DC boost converter, aiming to obtain the highest possible power from the photovoltaic array. The reference signal for the SAPF is obtained from the pqr theory. To fulfil the requirement of the active power filtering function under balanced, unbalanced, and distorted (including 3rd and 5th harmonics) power supply voltages, the inverter switching state control has been achieved based on MPCC. The effectiveness of the proposed configuration is verified through results obtained from simulation studies conducted in the MATLAB/Simulink environment.

Multivector-Based Model Predictive Current Control With Zero-Sequence Current Suppression for Three-Phase Series-End Winding Permanent Magnet Synchronous Motor Drives

To suppress the zero-sequence current and enhance the steady-state performance of the three-phase series-end winding permanent magnet synchronous motor (TPSW-PMSM) drives, a multi-vector-based model predictive current control scheme with zero-sequence current suppression is developed in this paper. The concept of the desired voltage is utilized to preselect the candidate voltage vectors and offer the guidance of the geometric division of the sector. In one sampling period, multi-voltage vectors are employed, and the combinations include one active voltage vector and one null voltage vector, and two active voltage vectors, which improves the steady-state performance. Meanwhile, the duty cycle of each voltage vector is derived based on an intuitive geometric expression of the cost function. Further, the relationship between the zero-sequence voltage injection and the switching sequence is revealed, and it is employed to suppress the zero-sequence current actively. In addition, overmodulation operation is also taken into consideration in the proposed scheme. Finally, the experimental study validates the effectiveness of the proposed scheme.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding Mode Observer With Enhanced Current and Speed Tracking Ability Under Disturbance

For model predictive current control (MPCC) strategy, its poor dynamic response caused by speed loop PI control and poor parameter robustness are the main factors restricting its development. So this paper proposes a model predictive current control based on sliding mode observer with better tracking ability of current and speed under disturbance. First, this paper analyzes the effect of motor parameter mismatches on current predicted values from multiple perspectives. And a sliding mode disturbance observer is designed to compensate lumped disturbances including parameter mismatches and unmodeled dynamic disturbances into prediction model. Then, the speed loop PI controller in model predictive current control is replaced by a deadbeat predictive controller to improve response speed of the speed loop. Meanwhile, the sliding mode load torque observer is constructed to observe the load torque and sends it into the deadbeat predictive controller to improve the anti-load disturbance performance of the control system. Furthermore, a sliding mode reaching law based on sinusoidal saturation function is proposed to suppress chattering of sliding mode observer. The experiments demonstrate that the proposed control strategy not only has faster dynamic response but also has good speed and current tracking performance under lumped disturbance and load disturbance.

Control Strategy for Array Buoy Type Wave Energy Converters' Power Conversion

In order to solve the problem of complex inner‐loop control structure, difficult debugging of control parameters and poor anti‐interference ability in the double PI power conversion control method of array buoy type wave energy converter, this article proposes a power conversion control strategy in which the one step delay compensation model predictive current control strategy is proposed for the machine side rectifier and the three closed‐loop control structure with PI voltage outer‐loop, quasi‐proportional resonant (QPR) current inner‐loop and proportional capacitor current inner‐loop is proposed for the grid‐side LCL inverter. In addition, this article proposes a graded power generation scheme for a single oscillating buoy and two power generation branches, which aims to address the issue of a restricted safe working pressure range for the accumulator in the single buoy and single power generation branch mode. Based on the mathematical model of the system's key components, the MATLAB/Simulink program is applied for modeling and simulation. The results indicate that the recommended graded power generation scheme and power conversion control mechanism may accomplish stable operation of the system under three diverse wave conditions, showing the scheme's practicality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Research on an improved model predictive current control for direct‐drive wave energy converters with linear generators

AbstractThis paper deals with the current control strategy of a permanent magnet linear generator (PMLG) for a direct‐drive wave energy converter (DDWEC). As the mover is in a linear reciprocating motion, the reference of the controller of the generator is dynamic fluctuations with the mover movement under maximum power point tracking (MPPT) conditions. Therefore, the dynamic requirements of the controller become higher. To increase dynamic control performance, an improved model predictive current control (IMPCC) strategy is proposed here. Through a three‐step cascaded optimization algorithm, the target voltage vector is optimized from the three dimensions of the sector of the voltage vector, the angle of the voltage vector and the length of the voltage vector. The target voltage vector is output to the three‐phase rectifier by using SVPWM to complete the entire control process. Simulation and experimental results show that the proposed control strategy effectively reduces the current ripple and the vibration of PMLG and improves the stability of DDWEC.

Experimental Validation of Torque Ripple Reduction in MMC-Fed BLDC Motor Using the Proposed Phase Modulated Model Predictive Control

This paper presents the torque ripple analysis of a brushless DC (BLDC) motor. This torque ripple is generated by generating the ripple in the stator current. For reducing the ripple, a phase-modulated model predictive current control strategy has been implemented in an MMC-fed BLDC motor. The performance of the torque ripple of the BLDC motor is enhanced by using the proposed phase-modulated model predictive control (PMMPC) technique as compared to the model predictive control (MPC) and proportional-integral (PI) controller. Moreover, the performance of the torque ripple is enhanced by increasing the switching frequency for all controllers. A comparative analysis of the torque ripple of the BLDC motor is depicted for different current control and different switching frequency. A harmonic distribution is also shown for all control techniques at switching frequencies of 1, 5, and 10 kHz. The harmonic content is minimum for the PMMPC technique when compared to the other mentioned control technique. The experimental validation of simulation results has also been done here.

A New Model Predictive Current Control Strategy for Hybrid Energy Storage System Considering the SOC of the Supercapacitor

Proportional-integral (PI) control and model predictive control (MPC) are mainly utilized in battery and supercapacitor (SC) hybrid energy storage system (HESS) of dc microgrid. Unfortunately, the regulation time of the PI controller is long, while large current ripples are introduced by MPC when the system frequency is low. This article proposes a new model predictive current control (new MPCC) strategy for HESS based on MPC and PI controllers. This method reduces current ripples, and meanwhile, enhances system stability and rapidity. MPC is adopted as the outer loop and it calculates reference values for the inner PI current loop. Furthermore, the state of charge (SOC) of the SC is considered based on fuzzy control to optimize the reference power of the SC inner controller. First, the topology and mathematical model of HESS in the dc microgrid are analyzed. Then based on PI control and MPC, the new MPCC is proposed, and compared with MPC and PI methods, respectively. Finally, the fuzzy control method takes SOC of SC into account to optimize the SC reference current. Simulation and experimental results show that the proposed new control strategy could effectively decrease current ripples and dynamic processes and entirely perform the high energy density characteristic of batteries and high power density characteristic of SC.

Three-Level Inverter-PMSM Model Predictive Current Control Based on the Extended Control Set

In the neutral point clamped (NPC) three-level inverter-permanent magnet synchronous motor system, traditional model predictive current control (MPCC) uses the system predictive model to traverse the 27 basic voltage vectors, to achieve the d-q axis current component and neutral point voltage of the multi-objective optimal control. Finite control set model predictive control predicts the state change of the control target at future moments based on a finite number of switching states of the inverter. The control principle of this method is simple and easy to implement, but the control effectiveness of this control strategy is limited because only one basic vector can be selected as the optimum output per control period. In this paper, a model predictive current control strategy based on an extended control set (ECS-MPCC) is proposed, which can improve the control performance of the system by extending the control set to select multiple vectors in a single control period compared to the traditional strategy. In addition, to address the disadvantage of extending virtual space vectors leading to an increase in computation, this paper proposes a fast search method for optimal vector based on region reduction. The proposed method avoids the optimization process traversing all virtual space vectors, thus enabling a fast search for the optimal vector. The experimental results show that the proposed control strategy has good steady-state and dynamic performance.

Improved model predictive current control for multi‐mode four‐switch buck–boost converter considering parameter mismatch

AbstractFour‐switch Buck‐Boost (FSBB) converter usually adopts multi‐mode control methods in applications requiring both voltage step‐up and step‐down. Model predictive current control (MPCC) can improve the dynamic performance and simplify multi‐mode switching control, but parameter mismatch will deteriorate its control performances. To this end, the theoretical steady‐state error of MPCC caused by parameter mismatch is derived; the influence of parameter mismatch on the dynamic and steady‐state performances is analyzed. Further, an improved MPCC strategy with the parameter disturbance observer (PDO) and dynamic adjustment block for the FSBB converter is presented. The PDO is developed to correct the prediction model and eliminate steady‐state errors due to parameter mismatch. The dynamic adjustment block related to the tracking error can further avoid the dynamic performance degradation. Additionally, a load disturbance observer (LDO) is designed to improve the dynamic response to the unknown load variation. Then, multi‐mode operation of the FSBB converter is realized with the proposed MPCC, which determines the operating mode according to the prediction results without extra mode detection. Finally, simulation and experimental results demonstrate that the proposed strategy can improve the dynamic and steady‐state performances of MPCC with parameter mismatch and achieve multi‐mode operation with high control performances.

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

This paper presents the modeling and predictive control of permanent magnet toroidal motor (PMTM). Based on the spatial structure characteristics and working principle of toroidal motor, the time-varying inductance parameters are derived. The nonlinear mathematical model of toroidal motor is established in rotating coordinate system. The output fluctuation of the toroidal motor is analyzed. To improve the output performance of toroidal motor, the full predictive cascade speed and current control (F-MPC) strategy for toroidal motor is proposed. For F-MPC of toroidal motor, the finite control set model predictive current control (FCS-MPCC) strategy with delay compensation is adopted in current inner loop, and the deadbeat model predictive speed control (DB-MPSC) strategy is adopted in speed outer loop. Meanwhile, to achieve DB-MPSC, the reduced-order Luenberger observer is designed to obtain load torque in real time. F-MPC is the combination of current-speed prediction and Luenberger observer; it can overcome the limitation of excessive parameter adjustment of traditional cascade PI (Proportional Integral) controller. The simulation results show that compared with FOC (Field Oriented Control) and FCS-MPCC, F-MPC can improve the dynamic response and anti-interference performance of toroidal motor, and it can also reduce the fluctuation of output speed and torque significantly.

A Robust Model Predictive Current Control Strategy with Low Complexity for PMSM

A Robust Model Predictive Current Control Strategy with Low Complexity for PMSM

Nonlinear Function Integral Sliding Mode-Based Model Predictive Current Control for PMSM Drives With DC-Bus Voltage Observer

As for permanent magnet synchronous motor (PMSM) drive systems with dc-bus voltage sensor failure, a nonlinear function integral sliding mode (NFISM)-based finite-control-set model predictive current control (FCS MPCC) strategy with sensorless is put forward. In principle, measuring dc-bus voltage of voltage source inverter is vital for PMSM drives to perform FCS MPCC. Aiming at dc-bus voltage sensor unavailable, a new nonlinear extended state observer (ESO) is developed to estimate dc-bus voltage. In the light of load torque disturbance, a modified isokinetic reaching law is proposed and then outer loop NFISM speed regulator is brought forward to heighten the system robustness against load variation and attenuate the chatting level. What’s more, for the sake of decreasing the stator current ripple and improve dynamic response, an inner-loop FCS MPCC scheme is adopted. The proposed NFISM-based FCS MPCC with nonlinear ESO can make PMSM drives have satisfactory speed and torque performance. Compared with PI-based and ISM-based speed regulator, respectively, the proposed NFISM-based one enables FCS MPCC PMSM drive system to have stronger anti-load variation ability and weaker fluctuation. StarSim/Hardware experiments validate the feasibility and effectiveness of the proposed strategy.

Virtual Voltage Vector-Based Model Predictive Current Control for Five-Phase Induction Motor

The high-performance control technology of multi-phase motors is a key technology for the application of multi-phase motors in many fields, such as electric transportation. The model predictive current control (MPCC) strategy has been extended to multi-phase systems due to its high dynamic performance. Model-predictive current control faces the problem that it cannot effectively regulate harmonic plane currents, and thus cannot obtain high-quality current waveforms because only one switching state is applied in a sampling period. To solve this problem, this paper uses the virtual vector-based MPCC to select the optimal virtual vector and apply it under the premise that the average value of the harmonic plane voltage in a single switching cycle is zero. Taking a five-phase induction motor as an example, the steady-state and dynamic performance of the proposed virtual vector MPCC and the traditional model predictive current control were simulated, respectively. Simulation results demonstrated the effectiveness of the proposed method in improving waveform quality while maintaining excellent dynamic performance.