Predictive Current Control Strategy Research Articles

An improved two-beat deadbeat synchronous predictive current control strategy for MMC based on Newton interpolation method

An improved two-beat deadbeat synchronous predictive current control strategy for MMC based on Newton interpolation method

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.

Development of constant switching frequency model predictive control for DFIG-based wind energy conversion system in standalone and grid connected operation

This paper presents a constant switching frequency (CSF) model predictive control (MPC) for a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) in standalone and grid-connected operations. The system comprises a cascade connection of a rotor side converter (RSC) and a grid side converter (GSC) with a common DC-link. A battery energy storage system (BESS) is also connected to the common DC-link via a bi-directional dc–dc converter (BDDC). The RSC is controlled through CSF-model predictive current control (CSF-MPCC) scheme in both standalone mode (SAM) and grid-connected mode (GCM) of operations. The GSC control employs the CSF-MPCC scheme in grid-connected operation, whereas CSF-model predictive voltage control (CSF-MPVC) scheme is implemented in standalone operation. Moreover, the power balance is achieved by controlling the BDDC using MPCC to discharge or charge the BESS in response to surplus or deficient power generation. The effectiveness of the proposed control approach for DFIG-based WECS in both SAM and GCM is verified through MATLAB-based simulation and experimental results under different dynamic conditions like variable wind velocity changing from 12 m/s to 8 m/s, different grid demands from 3 kW to 1.5 kW and load change of 4 kW to 6.5 kW. Moreover, the robustness of the proposed control is corroborated by parametric sensitivity, time delay, and grid unbalance, along with its low-voltage ride-through (LVRT) capability during an LLLG fault.

Torque and d-q axis current dynamics of an inverter fed induction motor drive that leverages computational intelligent techniques

<abstract> <p>Emphasizing the significance of Model Predictive Control (MPC) in modern optimization of control systems, the proposed research distinctively highlights its predictive prowess through the application of current state variables and well-structured mathematical models. We introduced a Predictive Current Control (PCC) strategy applied to a Three-Phase Inverter-fed Induction Motor (IM), with a particular focus on the Sequential Model methodology. The Sequential Model MPC algorithm employed a cost functional approach, predicated on the square of the discrepancy between reference and stator-measured currents of the IM in d-q reference frame. This method, implemented and tested in both MATLAB/Simulink and Python environments, utilized a minimization principle to guide the switching states of the inverter, thereby ensuring the accuracy of voltage signals for the induction motor. The projected study further included a comparative analysis of the electromagnetic torque, load currents, rotor speed, and angle deviations derived from the Sequential Model with those obtained through the Ant Colony Optimization (ACO) and Nelder-Mead methods. The results distinctly illustrated the robust adaptability of the Sequential Model methodology, outperforming the ACO and Nelder-Mead techniques in certain aspects such as minimum current errors, better speed regulations, and rotor angle trajectories.</p> </abstract>

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

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

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.

Robust three‐voltage‐vector‐based model‐free predictive current control for permanent magnet synchronous motor with ultra‐local model

AbstractA large current ripple may arise in the finite control set model‐free predictive current control (FCS‐MFPCC) strategy because only one voltage vector can be applied during the whole control period. Therefore, an improved three‐vector‐based model‐free predictive current control (MFPCC) strategy to improve the current tracking performance is proposed by increasing the number of the applied voltage vector. Based on the extreme existence theorem for the function of two variables, the optimal voltage vector can be synthesized. Compared with the one‐vector‐based FCS‐model‐based predicted current control (FCS‐MBPCC) and two‐vector‐based FCS‐MBPCC in the time and frequency domains, the maximum tracking error is decreased by 42.17% and 48.58%, and the total harmonic distortion is decreased by 40.97% and 37.56%, respectively. Besides, compared with the previous three‐vector‐based strategy, the computational time of the proposed strategy is reduced from 57.6 to 45.89 , which is reduced by 20.33%. Furthermore, the switching frequency of the proposed method can be decreased by 70.3% compared with the deadbeat MFPCC. The experimental results in the time and frequency domains demonstrate that the proposed three‐vector‐based MFPCC can not only improve the current tracking performance but also improve robustness against parameter mismatches.

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

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.

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

Autoregressive Moving Average Model-Free Predictive Current Control for PMSM Drives

To eliminate the influence of the parameter mismatches and obtain high model quality, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed in this paper and applied to the permanent magnet synchronous motor (PMSM) speed control system. Since the ARMA model group which is a family of mathematical models containing AR, MA and ARMA structures considers operating states within several sampling periods to achieve better model accuracy, the plant is online designed as this type, and its coefficients are estimated according to the sampled data by the normalized least-mean-square (NLMS) algorithm with adaptive normalized step length to achieve improved model quality with reduced calculation burden. Compared with the ultra-local MF-PCC strategy, advantages of better stator current quality and robustness are demonstrated by the experimental results, as well as the reduced calculation burden compared with the recursive least square (RLS) algorithm used to estimate the coefficients.

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

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

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.

Space-Vector-Equalized Predictive Current Control Scheme for the Modular Multilevel Converter With Improved Steady-State Performance

The large number of control options and steady-state tracking errors are two main challenges for model predictive control in the modular multilevel converter (MMC). To solve these issues, each arm of the three-phase MMC is considered as a whole, and a space-vector-equalized predictive current control scheme is proposed for the three-phase MMC in this paper. Firstly, eight equalized space vectors are involved, and only six nonzero vectors are evaluated in each period. As a result, the number of evaluated control options can be significantly reduced. Then, two optimal vectors are selected, and their dwell times are calculated based on the predicted output currents of the MMC. Furthermore, the compensation terms are designed and added to the dwell times so that the steady-state performance can be improved by the proposed scheme. Experimental results show that the proposed scheme provides fast dynamic response and outstanding steady-state performance for the three-phase MMC.

A Simplified Voltage Vector Preselection-Based Multivector Predictive Current Control for Improved Torque Performance of PMSM Drive

Predictive Current Control (PCC) is a pioneering approach for constrained non-linear systems demanding dynamic performance in industrial and transportation applications. This article proposes a multivector PCC scheme based on a simplified voltage vector (VV) preselection for improved torque performance of PMSM drives. The prediction and optimization operations in the basic PCC (BPCC) scheme increase the computational burden of the processor. The proposed method aims to reduce the complexity of the control scheme with a VV-preselection based on an effective position estimation using the gradient concept. This helps to directly determine the optimal VV without evaluating cost-function using all the VVs. However, the application of a single VV in BPCC leads to large undulations in the torque and flux responses. This necessitates the application of multiple VVs in each control period to ensure improved torque response. To achieve this, the duration of application of the VVs is determined using the cost-function ratio of the different VVs chosen in the proposed scheme. This effectively reduces the steady-state torque ripples of the drive while retaining its dynamic response. The performance of the proposed scheme is evaluated through experimentation and compared comprehensively with BPCC and recent literature.

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.

A Novel Double-Voltage-Vector Model-Free Predictive Current Control Method for Two-Level Voltage Source Inverters

An improved model-free predictive current control (MFPCC) scheme based on double-voltage-vector for two-level voltage source inverters (2L-VSIs) is proposed in this paper. First, two basic voltage vectors are chosen in each control period, whose durations are calculated based on the cost function. Then, a novel current gradient updating method is developed by using the applied two voltage vectors to calculate the current gradients of the remaining six voltage vectors. As a result, the stagnation effect can be eliminated and current ripples and total harmonic distortion can be reduced effectively. In addition, due to the nature of vector diagram symmetry, the processing of current gradient updating is simplified and the calculation speed is increased. The effectiveness of the proposed scheme is verified by simulation and experimental results from the 2L-VSI system.

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.

Continuous-Control-Set Model-Free Predictive Fundamental Current Control for PMSM System

Although the robustness is enhanced essentially by the model-free predictive control, the accuracy of its data-driven model is directly affected by the unnecessary current harmonics, such as the harmonics caused by the control strategy itself. To reduce the influences of these harmonics, a continuous-control-set (CCS) model-free predictive fundamental current control (MF-PFCC) strategy is proposed in this paper for the permanent magnet synchronous motor (PMSM) driving system. Harmonics due to the controller including side frequency effect and frequency coupling effect are analyzed in principle, and a frequency-converting double second-order generalized integrator (DSOGI) structure is designed to exclude the fundamental terms from the sampled current to cut the coupling path of the effects and to improve the accuracy of the data-driven model in the MF-PFCC without any time-variable physical parameters. The analyses of the effects and the effectiveness of the proposed method are verified by experimental and supplementary simulation results, as well as advantages including the reduced influence of effects and enhanced robustness with improved stator current quality.