Predictive Current Control Algorithm Research Articles

Robustness Improved Method for Deadbeat Predictive Current Control of PMLSM with Segmented Stators

Permanent magnet linear synchronous motors (PMLSMs) with stator segmented structures are widely used in the design of high-power propulsion systems. However, due to the inherent delay and segmented structure of the systems, there are parameter disturbances in the inductance and flux linkage of the motors. This makes the deadbeat predictive current control (DPCC) algorithm for a current loop less robust in the control system, leading to a decrease in control performance. Compensation methods such as compensation by observer and online estimation of parameters, are problematic to apply in practice due to the difficulty of parameter adjustment and the high complexity of the algorithm. In this paper, a robustness-improved incremental DPCC (RII-DPCC) method—which uses incremental DPCC (I-DPCC) to eliminate flux linkage parameters—is proposed. The stability of the current loop was evaluated through zero-pole analysis of the discrete transfer function. Current feedforward was introduced to improve the stability of I-DPCC. The inductance stability range of I-DPCC was increased from 0.8–1.25 times to 0–2 times the actual value, and the theoretical stability range was increased more than 4 times, effectively improving the robustness of the predictive model to flux linkage and inductance parameters. Finally, the effectiveness of the proposed method was verified through numerical simulation and experiment.

Finite control set model-free predictive current control of permanent magnet synchronous motor

This thesis proposes finite control set model-free predictive current control (FCSMFPCC) algorithm of permanent magnet synchronous motor (PMSM). To address issue of degraded performance during motor operation due to parameter mismatch, a model-free predictive current control arithmetic is devised, which utilizes ultra-local model (ULM) of PMSM. This proposition takes sum of known and sealed disturbances of the system as an unknown quantity, and uses sliding mode observer (SMO) to take stock of sealed quantity. To address the issue of operation delay in predictive control, a two-step method is used to make up for the system. When using price function to choose the first-rank voltage vector, vector selection optimization link is added, which effectively simplifies the algorithm while optimizing the inverter switching action. The simulation outcomes highlight the advantages of the described control algorithm in terms of better dynamic response performance and stronger robustness.

A new generalized predictive current control algorithm with integration of fractional part function based modulation technique: Single phase self‐balanced type multilevel inverter topologies

SummaryIt is generally impossible to control the inverter output current (IOC) based on operator needs if only pulse width modulation (PWM) techniques are used. Conventionally, authors have reported the finite control set‐based predictive current control algorithm (FCS‐PCCA) to control the IOC with a fast dynamic response. However, this type of algorithm without a PWM stage generates a variable switching frequency (VSF) of operation, and it leads to filter design problems, inequitable thermal stress, and more current distortions that have been presented. To alleviate all these issues, a new generalized PCCA with the integration of a fractional part function‐based single carrier modulation technique (G‐PCCA‐FPF‐SCMT) has been proposed, and it provides constant switching frequency (CSF) of operation. In this technique, the normalized optimum inverter output voltage (N‐O‐IOV) has been fed with FPF‐SCMT. The particular modulation stage implementation is straightforward since even inverter level number increases, the number of references and triangular carrier signals is always only one. The complete control implementation process of the proposed technique is extremely low compared with all traditional CSF‐based PCCAs. To verify the proposed technique experimentally in both steady and transient state operations, one of the conventional five‐level switched capacitor‐based inverter topologies has been considered and elucidated vividly in both open‐loop and closed‐loop operations.

Voltage-Fed single stage inverter for generating systems with Multi-Input inverter using Pulse Width Modulation

A voltage-fed single-stage multiple-input inverter is developed for hybrid wind/photovoltaic energy generating systems. In this research proposes a revolutionary multi-input inverter that simplifies and reduces the power system's cost. Inverter comprises the DC-DC converters and full-bridge ac inverters, which are buck/buck-boost. This study proposed a multi-input converter for hybrid photovoltaic and wind energy systems, with the basis and enhanced functionalities created by the scholars included in the design. A Novel Predictive Current Control Algorithm (NPCCA) is proposed for Hybrid a renewable energy source which depends on improved space-vector Pulse-Width-Modulation (PWM) to provide high-quality current and quick dynamic reaction to incorporate changes. Also discussed are the solar array and wind turbine output power characteristics. The Maximum Power Point-Tracking (MPPT) methodology is accomplished using the perturbation and observation methods to resist all non-linearities in the typical I-V curves. The proposed multi-input converters results in lower production costs for the converters since they use fewer semiconductors and take up less space. Soft-starting and circuit safety features are added for real-world uses. The simulation outcome of the NPCCA framework attained in terms of accuracy is 92% and efficiency is 92% when compared with the other existing approaches.

Model predictive and SoC balancing control of a CHB inverter in battery energy storage application using reward functions

SummaryThis article presents an improved model predictive current control algorithm combined with a novel state of charge (SoC) balancing approach for a three‐phase cascaded H‐bridge inverter in battery energy storage applications. Based on the model predictive control with elimination of redundant voltage vectors, neighboring vectors of the last voltage vectors are used to find the optimal ones in only one control cycle, which solves the problems of high computational burden and slow dynamic response. To overcome the unsuitability of the conventional cost function in SoC balancing, the reward functions that effectively balance the inter‐phase SoCs, inner‐phase SoCs, and inner‐phase dc voltages are presented respectively. Simulation and hardware‐in‐the‐loop‐based experimental results validate the effectiveness of the proposed strategy.

A Predictive Negative Sequence Current Control Algorithm for Voltage Imbalance Compensation and Power Oscillation Minimization under Unbalanced Conditions

The predictive-based strategy employed in this paper allows an inverter to simultaneously inject positive and negative sequence reactive current to compensate for voltage imbalance, taking into account imbalance adverse effects on power oscillations. A suggested optimization cost function of a predictive-based controller is formulated to solve the trade-off problem between grid voltage support and voltage imbalance compensation by determining the optimal negative sequence reactive power to be injected to the reference current of a generator. The proposed predictive-based control strategy is evaluated under various distributed generation operating conditions in terms of injected active to reactive power ratio. Additionally, its performance is compared with the performance of an active power oscillations minimization (p̃-minimization) control strategy, where the active power oscillations due to reactive power disappear. In contrast with p̃-minimization control strategy, the proposed predictive-based strategy managed to reduce the active power oscillation. The adequacy of the proposed strategy is verified via simulations of a distributed energy resource (DER) grid-connected inverter under voltage imbalance caused by an unbalanced load. The simulation work presented in this paper was conducted using MATLAB/Simulink software. As a part of a smart inverter functions to preserve the energy supply under unbalanced conditions, this research would serve as a platform for studying the inverter optimal control of negative sequence component.

Sliding Mode Predictive Current Control for Single-Phase H-Bridge Converter with Parameter Robustness

As the important technology of renewable energy systems, power electronics technology is directly bound up with the prospect and development of renewable energy technology. As the output end of renewable energy systems, a single-phase H-bridge converter needs to stabilize the output current. When predictive current control (PCC) tracks the reference current, the dynamic response is the fastest, but the control delay and the changes in model parameters will cause the output current steady-state error. The sliding mode predictive current control (SMPCC) algorithm is proposed to control the output current better. The proposed SMPCC scheme uses the combination of traditional PCC and variable structure scheme, and it establishes the mathematical model according to the state equation of the converter. Taking the exponential reaching law as control law, the expression of the variable structure controller is obtained. The MATLAB experimental and simulation results show that SMPCC can not only improve its robustness to the parameter changes but also obtain better steady-state performance while enhancing the rapidity of the current changes. In conclusion, SMPCC has a better control effect in the converter.

Low complexity duty cycle modulation model predictive current control of permanent magnet synchronous motor

This study proposes a model predictive current control algorithm for the synchronous permanent magnet motor with low complexity duty cycle modulation. Three single-phase voltage vectors are simulated to synthesize the target voltage vector that enables the current to achieve deadbeat control. The algorithm can change the amplitude and direction of the synthesized vector and extend the selection range of the synthesized vector to the whole hexagonal region. That is to say, this method can realize the deadbeat control of the d-axis and q-axis currents, and reduce the current pulsation. Moreover, this method only needs one action time operation, which can directly convert the action time of the voltage vector into a three-phase duty cycle after equivalent processing and over-modulation of the action time of the voltage vector. It is not necessary to traverse multiple sectors for optimization, which simplifies the modulation method and reduces the computational complexity. The effectiveness and reliability of the proposed method are verified by theoretical derivation and experimental results.

An Improved Predictive Current Control of Eight Switch Three-Level Post-Fault Inverter With Common Mode Voltage Reduction

Eight-switch three-phase post-fault inverter (ESTPI) is widely used as the reconfigured topology of three-phase three-level neutral point clamped DC/AC inverter when open-circuit fault occurs. A key technical challenge for the ESTPI is the post-fault controller design. The existing post-fault control approaches for the ESTPI suffer from the difficulty in trading off the current tracking performance, neutral point voltage (NPV) balancing and common mode voltage (CMV) levels. To solve this problem, this paper proposes an improved predictive current control algorithm. The proposed algorithm contributes to achieve accurate output current tracking while greatly reducing the CMV with a balanced NPV. Simulation and experimental results are presented to validate the effectiveness of the proposed algorithm.

Improved Model Predictive Current Control of NPC Three-Level Converter Fed PMSM System for Neutral Point Potential Imbalance Suppression

For the neutral point clamped (NPC) three-level converter fed permanent magnet synchronous motor (PMSM) system, the performance of the conventional model predictive current control (MPCC) algorithm will be deteriorated if the amplitude of the neutral point potential (NPP) is large. Additionally, the adjustment process of the weighted coefficients of the conventional MPCC algorithm is complex because of numerous control terms in the cost function. To solve the above issues, an improved MPCC algorithm is proposed in this paper. Firstly, Newtonian iteration is used to transfer the stator current into stator voltage in the cost function. Then, the NPP term in the conventional cost function can be eliminated by introducing the partition control of the NP potential, which also eliminates the whole adjustment process of weighting coefficients. Finally, based on the amplitude of the NPP, the amplitude and phase angle of medium and small vectors are modified to improve the control performance of the torque and flux. Experimental results show that the fluctuation of the neutral point potential can be suppressed rapidly. Meanwhile, the performance of the torque, flux and current are also improved compared with the conventional MPCC.

Dual-Vector Predictive Current Control of Open-End Winding PMSM With Zero-Sequence Current Hysteresis Control

Dual-inverter fed open-end winding permanent magnet synchronous motor (OW-PMSM) drive topology can extend the speed range under limited dc bus voltage, but it suffers from the zero-sequence current (ZSC) problem when with common bus configuration. Predictive current control (PCC) is a potential high-performance control method for electric drive. However, the PCC algorithm for dual-inverter system may bring a huge computational burden because of more space vectors (SVs) to traverse. In order to improve the steady-state current performance and suppress the ZSC, this article proposes a dual-vector PCC (DV-PCC) method for OW-PMSM with ZSC hysteresis control. The reference voltage vector is predicted in advance according the model. A novel sector judgment method is used to judge the sector and subsector where the reference voltage vector is located, and determines the two candidate SVs. Then the dwell time of the two candidate SVs is calculated through the projection length of the reference voltage vector. Moreover, the switching states redundancy feature of dual-inverter system is employed to suppress ZSC. As some SVs correspond to multiple switching states of different zero-sequence voltages (ZSVs), the switching state whose ZSV polarity is opposite to the ZSC polarity is selected as the output to realize the hysteresis control of ZSC. When the ZSVs of the candidate SVs contradict the hysteresis control demand, the zero-vector insertion method is used to guarantee the effective suppression of ZSC. The experiments are implemented in comparison with the existing DV-PCC method.

Digital Predictive Current-Mode Control for Asymmetrical Half-Bridge LED Constant-Current Driver

In this paper, we propose three advanced digital predictive current-mode control (DPCC) algorithms based on a simplified estimation method of duty cycle lost time for asymmetric half-bridge (AHB) LED constant-current driver. They are the digital predictive peak current mode control(DPPCC) algorithm based on leading-edge modulation, the digital predictive quasi-valley current mode control(DPqVCC) algorithm based on trailing-edge modulation, and the digital predictive quasi-average current mode control(DPqACC) algorithm based on double-edge modulation. Simulation and experimental results demonstrate that the three DPCC algorithms can effectively offset the effect of delay on digital control performance, which confirms the superiority of DPqACC. When the DPqACC is applied, the series-load-jump transition times are below 4.5 ms, the maximum load adjustment rate is 0.5%/ V, and the output current can be tuned continuously between 0.3 A and 4.5 A with the longest transition time of 1 ms. Moreover, the three DPCC algorithms are capable of compensating for low-frequency ripples due to the rapid regulation speed of the inner loop. With the electrolytic capacitance removed without additional ripple compensation, the maximum peak-to-peak value of the low-frequency secondary ripple is 0.1163 A (2.91%) when the input is 400 53sin(2ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>lin</i></sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ) V and the output is 4 A. This approach provides an excellent solution for the digital design of low-ripple AC-DC LED constant-current drivers without electrolytic capacitance.

Model Predictive Current Control Algorithm Based on Joint Modulation Strategy for Low-Inductance PMSM

The disc coreless permanent magnet synchronous machine (PMSM) has many advantages. The inductance is far lower than that of the machines with the core structure. However, the general converter is designed for general machines. The current fluctuations are large when the low-inductance PMSM (LIPMSM) is driven by the general converter. To solve the above problem, the integrated design concept is adopted to design the converter, including the modulation algorithm, control algorithm, and the corresponding topology. The joint modulation strategy is proposed to increase the modulation frequency, which adjusts the output voltage by using the bus circuit and inverter circuit. The model predictive current control (MPCC) algorithm based on the joint modulation strategy is proposed, which has a small computational burden and high control accuracy. The most important feature of the proposed MPCC is that it has a modulation algorithm. To further reduce the current fluctuations, the bus voltage is adjusted in real time according to the bus voltage optimization algorithm. A new topology of the converter is designed to implement the algorithm function. Finally, the correctness and feasibility of the proposed method are verified. Compared with the general converter, the new one has a great improvement on the LIPMSM.

Design and Implementation of LCL Filter-Based Electric Machine Emulator With Disturbance Compensation

The electric machine emulator (EME), using digital simulation and power electronics to emulate the characteristics of actual machines, can greatly accelerate the testing of electric drives. However, most existing EMEs are based on typical L filter and linear controller, which causes control conflicts and bandwidth limitation. To address this issue, this paper presents an EME based on LCL filter with passive damping for a three-phase permanent magnet synchronous motor. To improve the dynamic emulating accuracy, a dual closed-loop deadbeat predictive current control algorithm is proposed, which is computationally efficient and easy to implement. The system stability is analyzed in the discrete domain, and the parameter constraints of the filter are obtained. Then, two unknown input observers are designed to compensate for the disturbance currents and voltages caused by modeling errors. Moreover, instead of the empirical method, a theoretical one considering the harmonic suppression, bandwidth, stability and resonance is presented for filter design. Finally, the performance of the proposed EME is validated through simulation and experimental results under various conditions such as machine start-up, torque step change, and speed reversal.

Key Technology Optimization and Development of New Energy Enterprises of Photovoltaic Power Generation

In order to improve the algorithm of time-varying parameters and unknownparameters adaptability, avoid assuming the approximate part deviationcaused by the algorithm, this paper proposes a adaptive control algorithm, thealgorithm based on lyapunov direct method to predict the output voltage inthe process of estimating each parameter in a reasonable manner to parameterestimation error with the actual output current and current automatic adjust-ment. The adaptive control of current tracking is realized and the error causedby assuming voltage or current and neglecting line resistance is avoided inthe predictive current control algorithm. The simulation results show thatthe tracking current can track the target current with high precision fromt = 0 in the presence of random noise, and the power factor is close to 1,showing a good steady-state performance. Frequency domain waveform, thecalculated harmonic distortion rate is 2.2418%, waveform quality is good andeach harmonic amplitude is small. Conclusion: adaptive control algorithmcan quickly and accurately realize current tracking and greatly suppress thenoise.

Model Predictive Control and Position Sensorless Control Algorithm for Induction Motor

In view of the traditional finite set model predictive control of induction motor, the enumeration method is used to select only one optimal voltage vector in one control cycle, which causes the deviation of stator current in the tracking control process, and a duty cycle model predictive current control algorithm is proposed. In this algorithm, duty cycle control is introduced on the basis of traditional model prediction algorithm. In a control period, an optimal non-zero vector and a zero vector act together, and the action time of the two is calculated. Based on the duty cycle model predictive control algorithm, the model reference adaptive speed estimation algorithm is introduced to improve the robustness of the model. Finally, the dynamic and static characteristics of vector control and two model predictive control algorithms are analyzed through simulation comparison, and the feasibility of the proposed speed estimation algorithm is verified on the basis of the duty ratio model predictive algorithm, and the robustness of the two estimation methods is compared. The superiority of the prediction algorithm of duty cycle model and the improved speed estimation algorithm is proved.

Research on model predictive peak current control algorithm for real-time inductance Identification

Compared with the traditional PI control, the current loop based on model predictive control has better dynamic performance and is widely used in the field of power electronic control. Peak current control (PCC) can conveniently protect the peak current of inductance, avoid the saturation of inductance, and easily realize the over-current protection of converter. In this paper, a model predictive peak current control algorithm for real time inductance identification is proposed for BOOST converter, and a method of double sampling peak current and valley current is proposed to realize real time identification of inductance value in a sampling period, which improves the dynamic performance and robustness of the algorithm. Simulation and experiment show that the proposed method can identify the inductance value in real time and track the reference value of the inductance current in two control periods.

Improved fast three vector predictive current control of permanent magnet synchronous motor

In order to improve the steady-state performance of the traditional model predictive control and reduce the amount of calculation of the control algorithm, this paper proposes a new fast three-vector predictive current control algorithm, which uses the principle of deadbeat control of the dq-axis current to calculate the reference voltage vector, and obtains the reference voltage vector position through sector judgment, combined with the three-vector model predictive control algorithm, the optimal voltage vector is directly applied to the motor, without the need to predict the behavior of the system under the voltage vector, which greatly reduces the amount of calculation. At the same time, due to the introduction of three vectors, three functions are applied in one cycle. The voltage vector greatly improves the steady-state characteristics of the system. The simulation results show that the proposed new three-vector predictive current control algorithm can reduce the amount of calculation and improve the steady-state characteristics of the system.

Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

The objective of the paper is to present the efficient and dynamic sensorless speed control of a surface permanent magnet synchronous motor (SPMSM) drive at a wide speed range. For high-performance speed sensorless control, a finite control set model predictive current control (FCS-MPCC) algorithm based on a model reference adaptive system (MRAS) is proposed. With the FCS-MPCC algorithm, the inner current control loop is eliminated, and the limitations of the cascaded linear controller are overcome. The proposed speed sensorless control algorithm provides an efficient speed control technique for the SPMSM drive owing to its fast dynamic response and simple principle. The adaptive mechanism is adopted to estimate the rotor shaft speed and position used in FCS-MPCC for dynamic sensorless control. FCS-MPCC uses a square cost function to determine the optimal output voltage vector (VV) from the switching states that give the low cost index. A discrete-time model of a system is used to predict future currents across all the feasible VVs produced by the voltage source inverter. The VV that reduced the cost function is adopted and utilized. Simulation results showed the efficacy of the presented scheme and the viability of the proposed sensorless speed control design under various load conditions at a wide speed operation range.

Model predictive current control for two-phase switches clamping of three-phase inverter

Traditional finite-control-set model predictive control has been widely concerned for heavy power voltage source inverters owing to its simplicity, as well as control flexibility. However, the drawbacks of widespread current harmonic, high switching loss and heavy computational burden are fully exposed. In this article, three-phase voltage source inverter is taken as the research object and a model predictive current control algorithm for two-phase switches clamping is proposed. Firstly, deadbeat control and delay compensation strategy are combined to calculate reference voltage vector and determine two legs that have maximum and minimum reference voltage vectors. Then, the optimal voltage vector set can be directly selected by clamping switches on two legs that have maximum and minimum reference voltage vectors, which decreases the number of commutations on three legs with a large current. The proposed algorithm not only reduces the switching loss and computational burden, but also increases the efficiency of operation. The simulation experiments are implemented to estimate the control effect of the proposed algorithm. Comparison results indicate the proposed algorithm can be more effective than existing algorithms.