Predictive Current Control Research Articles

Comparative Study of In-Wheel Motor Control Systems Based on Multiple Controllers

This paper proposes a comparative study of hub motor control based on multiple control strategies (pi control, direct torque control, model predictive current control) to address the problem of slow dynamic response in traditional vector control of electric vehicle permanent magnet synchronous hub motors. By strictly extracting the mathematical model of the motor, the desired target voltage vector and current vector are derived using three controllers and applied to the controlled object. Finally, experiments are conducted on a simulation platform, and the experimental conclusions are used to compare and analyze the reliability and adaptability of different controllers.

Adaptive Ultralocalized Time-Series for Improved Model-Free Predictive Current Control on PMSM Drives

Adaptive Ultralocalized Time-Series for Improved Model-Free Predictive Current Control on PMSM Drives

A New Sliding-Mode Observer-Based Deadbeat Predictive Current Control Method for Permanent Magnet Motor Drive

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

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

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

Improved Deadbeat Predictive Current Control With Embedded Resonant Polynomial and Disturbance Observer for PMSM Current Distortion Rejection

Improved Adaptive CCS-MPCC for Distorted Model Parameters Mitigation of IPMSM Drives

In this study, a robust adaptive model continuous-control-set model predictive current control (CCS-MPCC) method is proposed for interior permanent magnet synchronous motors (IPMSMs) considering the existing disturbances, unmodeled dynamics, and parameter variations. The novel aspect of this research is that it merges the improved dynamic, useful properties of model predictive control (MPC), which includes easy implementation and fast dynamic response, together with the most effective criterion of an adaptive controller (i.e., robustness to model uncertainties), that result in enhanced dynamic and steady-state control performance despite unknown and changing disturbances. Unlike the conventional CCS-MPCC approach that heavily depends on the accurate knowledge of the system model to attain improved control performance, the proposed method achieves satisfactory control performance (e.g., fast dynamic response, smaller steady-state error, and low stator current THD) by stabilizing the state errors to approach zero and compensating for variations in the model parameters using the designed feedback control terms and adaptive control terms, respectively. The stability of the proposed control method is ensured by the state errors asymptotically decaying to zero. The proposed approach was simulated and then implemented on a PSIM simulation tool and a prototype IPMSM test bench using TI TMS320F28335 DSP, respectively, to confirm its feasibility.

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.

FCS-MPC for Three-Level NPC Inverter-Fed SPMSM Drives Without Information of Motor Parameters and DC Capacitor

The conventional finite control set-model predictive control (FCS-MPC) suffers from poor robustness against model mismatches. This paper presents an improved FCS-MPC with a reconstructed mathematical model to predict current variations without using a lookup table (LUT) or motor parameters. The model coefficients and current variations related to different voltage vectors can be updated during each control period. As a result, the prediction error is significantly reduced at low switching frequency when compared with the prior LUT-based model-free predictive current control (MFPCC). Additionally, the tracking accuracy of the proposed method at high speeds is improved due to the elimination of approximation error. Furthermore, a simple scheme is developed to suppress neutral-point-potential drift without knowledge of the dc-bus capacitor. Simulation and experimental tests, along with comparisons with prior arts carried out on a three-level inverter-fed surface-mounted permanent magnet synchronous motor drive, confirm the superiority of the proposed method.

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

A Fast-Response PV Simulator Based on Improved Deadbeat Predictive Current Control

Model-Free Predictive Current Control of SPMSM Based on Enhanced Extended State Observer

Conventional model-free predictive current control (MFPCC) using an extended state observer (ESO) based on ultra-local model significantly decreased the sensitivity to partial motor parameters. However, the performance and stability of ESO will be degraded in the face of amplified high-frequency (HF) perturbation caused by inductance parameter mismatch. To solve the above problem, an enhanced ESO-based MFPCC method is proposed in this article. On the basis of constructing the discrete equation of ESO, an accurate and fast calculation of inductance parameter is proposed, which is real-time convergence to the actual value by deadbeat control principle, and fed back to the lumped disturbance and current observation calculation. It effectively eliminates the effects of HF perturbation on ESO. Simultaneously, the inductance and compensated lumped disturbance obtained from ESO are provided to the MFPCC command voltage calculation module, further improving the parameter robustness of MFPCC. The stability of the proposed ESO is ensured by the design of observer gain. Experiments are carried out on 1kW surfaced permanent magnet synchronous machines (SPMSM) test bench. Compared with the conventional ESO-based MFPCC method, the proposed method is superior in dynamic response, steady-state performance, and parameter robustness.

Experimental Assessment of Predictive Current Control Applied to Induction Machine Drive Systems Operating Under Single-Phase Open-Circuit Fault


 Predictive Current Control (PCC) has been widely applied in several applications. However, the literature has not discussed its use as a fault tolerance control algorithm in induction drive systems. In this way, this paper discusses the PCC method in two faulttolerant squirrel-cage induction machine drive systems operating under single-phase open-circuit faults. PCC’s postfault performance is compared to Field Oriented Control (FOC) for different steady- and transient-state scenarios, analyzing harmonic distortion, torque ripple, and the transition from healthy to postfault operation. Also, experiments tested the robustness of postfault PCC to low-speed operation, parametric variation, and a step change in reference rotor speed, showing that PCC also presents fault-tolerant operation under these conditions.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding‐Mode Disturbance Observer

A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

An improved ANFIS model predictive current control approach for minimizing torque and current ripples in PMSM-driven electric vehicle

An improved ANFIS model predictive current control approach for minimizing torque and current ripples in PMSM-driven electric vehicle

Advanced MPC Control Strategy for Active Front-End Converters in Distributed Energy Resources

The stability of grid-connected active front-end converters (AFEs) is often compromised by the intricate interplay between inherent converter nonlinearities and grid dynamics. This article presents a novel approach to dynamic model predictive current control (MPC) by leveraging recursive least squares (RLS) to accurately estimate the properties of a physical model. This estimation mitigates stability issues, setting the stage for improved control. Unlike conventional methods, our proposed RLS-based MPC, enriched with an auto-tuning feature, empowers controller development without the upfront need for precise external dynamics. This not only ensures high disturbance rejection but also maintains a high-fidelity control performance, rendering the approach versatile for applications where obtaining or predicting precise external dynamics is challenging. At each sampling interval, a cost function is applied to predicted variables to discern optimal switching states. Through extensive simulation studies covering diverse grid impedance changes and system nonlinearities, we evaluate the controller’s effectiveness. To underscore its superiority over traditional controls, simulation results are validated on a laboratory hardware platform equipped with Typhoon HIL and dSPACE real-time emulators, further attesting to the robustness and practical viability of our proposed approach.

Model-free predictive current control based on improved sliding mode disturbance observer

The motor parameters of permanent magnet synchronous motor (PMSM) can be mismatched in operation due to temperature variations and magnetic saturation. The parameter mismatch leads to a significant decrease in the robustness of traditional deadbeat predictive current control (DPCC), which leads to issues such as current static error and motor vibration noise. In this paper, a model-free predictive current control (MFPCC) method based on an improved sliding mode disturbance observer (SMDO) was proposed. The method estimated the total disturbance of the system using the improved SMDO, which could effectively suppress sliding mode chattering and accelerate the convergence speed of current errors. Subsequently, the predicted control voltage was calculated using a hyper-local model and compensated for the time delay. The experimental results demonstrated that the improved MFPCC-SMDO method generated lower motor noise compared to the traditional DPCC method when the controller’s inductance parameters are mismatched.

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.

Field-Weakening Strategy with Modulated Predictive Current Control Applied to Six-Phase Induction Machines

The predictive current controller has arisen as a practicable technique for operating multiphase machines due to its fast dynamic response, control flexibility, and overall good performance. However, this type of controller has limitations, e.g., it tends to suffer from steady-state tracking errors in (d−q) currents; high computational burden; and high (x−y) currents, which become more pronounced at higher speeds, thereby worsening its sustainability. While some proposals have addressed these limitations by incorporating modulation stages and new cost functions, there is still room for improvement, particularly at higher speeds. In line with the pursuit of sustainable advancements, this article explores the integration of a field-weakening strategy with a modulated predictive current controller applied to a six-phase induction machine to improve its performance at current tracking for higher speed ranges. Experimental tests were conducted to validate the effectiveness of the proposed controller, assessing stator current tracking, reduction in the (x−y) currents, and the total harmonic distortion.

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.

Single-Phase Charging of Six-Phase Integrated On-Board Battery Charger Using Predictive Current Control

Integrated On-Board Battery Chargers (IOBCs) have shown promise as an elegant charging solution for electric vehicles in recent literature. Although the three-phase charging technique of IOBCs has extensively been discussed in the literature, single-phase charging is still a challenging research topic. The Predictive Current Control (PCC) approach has shown many benefits, including a straightforward algorithm, simple implementation, comparatively quick response, and appropriate performance, when compared to conventional control techniques. This paper investigates the impact of single-phase charging of a six-phase-based IOBC system with different winding configurations using PCC, which, up to the best authors’ knowledge, has not been conceived thus far. Under single-phase charging, the zero-sequence current component is utilized to ensure zero torque production during charging mode. Since the impedance of the zero subspace is highly affected by the employed winding design, the performance of PCC with different winding layouts of either induction machine (IM) or permanent magnet synchronous machine (PMSM) is investigated and compared. The proposed method is experimentally validated using a 1.1kW six-phase IM and a 2 kW 12-slot/10-pole PMSM. Finite Element analysis is also carried out to investigate the effect of single-phase charging mode on the induced radial forces and vibration level when PM machine is employed.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.