Virtual Vector Research Articles

DSVM-Based Model-Free Predictive Current Control of an Induction Motor

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations.

A Secure Localization Scheme for UASNs Based on Anchor Node Self-Adaptive Adjustment

The UASNs are widely used in underwater communications and monitoring, and many applications require accurate information regarding the position of nodes. However, intentional attacks against devices or information transmission may exist in the network, and the localization process is periodic, so it is necessary to quickly address attacks and optimize the network structure. This paper proposed an anchor node self-adaptive adjustment localization scheme (ASAL), in which the anchor node can adjust the state and depth of its participation. Two filters were used to adjust the states of referable nodes. The first filter was based on the distance difference of reverse information transmission after direct localization based on anchor nodes. The second was based on the error of the anchor node’s reverse localization after network localization was completed. In addition, a depth-adjustment mechanism of anchor nodes was proposed to optimize the network structure, the virtual force vector was introduced to describe the cost of depth adjustment, and the whale optimization algorithm was used to converge to the depth with the minimum total cost. The simulation results showed that the scheme can ensure localization accuracy and coverage in attack scenarios and reduce localization energy consumption.

Reliability Improvement by Fault-Tolerant Operation of NPC Inverter for Motor Driving

The high-reliability operation of three-level inverters is crucial to prevent equipment damage, process downtime, and economic losses. This paper investigates a three-level neutral-point-clamped inverter under all possible combinations of open-circuit and short-circuit faults and proposes a new postfault operation method without adding extra hardware. This method provides comprehensive solution for operations after single and multiple-device failures, increasing the inverter reliability by 24%. This paper classified post-fault modulations and uncovered previously unknown fault scenarios that can be addressed using the proposed control method. A new postfault modulation based on space vector modulation with virtual vectors is proposed. The feasibility of the proposed control method is verified by simulation and an experiment for one fault scenario in a three-level neutral-point-clamped inverter with a 3.73 kW motor load. This paper contributes to improving the reliability of three-level inverters.

Kinematics modeling method of continuum space manipulator based on virtual discrete-jointed manipulator models

Continuum space manipulators (CSMs) have key application prospects in the maintenance of spacecrafts in narrow environments and space debris removal. Similar to conventional articulated space manipulators (ASMs), CSMs encounter a number of kinematic, dynamic, and control problems caused by the dynamic coupling between the continuum manipulator and its microsatellite platform. However, the continuum manipulator has no obvious kinematic joints. In addition, its motion is generated by the continuum deformation of its elastic body; thus, the virtual manipulator (VM) model of ASM in the free-floating state becomes inappropriate for CSMs. In this study, we propose a kinematic equivalent method for a free-floating CSM, which is equivalent to a virtual discrete-jointed manipulator (VDJM) connected to a virtual ground. First, the constraint relationship is used to establish the virtual vector manipulator (VVM) model for the CSM such that its center of mass in the free-floating mode remains unchanged. Furthermore, on the basis of the VVM model and D-H parameters of the CSM, virtual discrete joints are introduced, and a VDJM model for the CSM is proposed. Compared with the VVM model, the developed VDJM model can more intuitively express the motion characteristics of the CSM. Finally, we demonstrate the application potential of the developed VDJM model in the inverse kinematics solution, workspace, and dynamic coupling of CSMs.

A Simplified Virtual-Vector-Based Model Predictive Control Technique With a Control Factor for Three-Phase SPMSM Drives

Compared with the single vector-based model predictive control (MPC) technique, the multiple vector-based MPC technique gains interests in improving the total harmonic distortion (THD) of the stator currents and reducing electromagnetic torque and stator flux ripples for a three-phase surface permanent magnet synchronous motor (SPMSM). However, the complicated enumeration and operation in vector selection and duty cycle calculation increase the computational burden and limit its peneration. Hence, in this paper, a simplified virtual vector-based MPC with a control factor for a three-phase SPMSM is proposed. Without sector identification and enumeration operations, the proposed technique utilizes the control factor to automatically generate a large number of virtual vectors to achieve equivalent performance effectiveness with multiple vector-based MPC technique with lower sampling frequency. Subsequently, regardless of the number of virtual vectors, the candidate virtual vectors are directly identified in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> coordinate system to greatly reduce the computational burden. In addition, the duration of the selected optimal virtual vector and the resultant switching sequences are easily obtained and arranged to even the switching losses among the switching devices and further optimize the THD of the stator current. The proposed technique is simplified and flexible. Its control effectiveness is verified by experimental results.

Virtual Voltage Vector-Based Sequential Model-Free Predictive Control for Multiparalleled NPC Inverters

Parallel inverters are widely used in the micro-grid systems to meet the demand for increased power capacity and enhanced reliability. However, unbalanced connections between the parallel inverters may cause a spike in the zero-sequence circulating current (ZSCC), leading to performance degradation. To address this issue, a virtual voltage vector-based sequential model-free predictive control method is proposed in this paper for parallel converters. Specifically, extended state observer (ESO) and virtual voltage vectors are introduced into the finite control-set model predictive control (FCS-MPC) architecture to cope with the multi-objective including ZSCC inhibition and robustness performance improvement. Next, to prevent the adjusting of weighting factors, a sequential model-free predictive control solution is constructed. The main feature of the proposed control methodology is that the reliability and robustness of the controlled system can be significantly enhanced without knowing the accurate mathematical models and parameters. Finally, the effectiveness of the suggested approach is verified by performance evaluation.

Optimal Switching Sequence Model Predictive Control for Modular Multilevel Converter

This paper proposes a new optimal switching sequence model predictive control method for modular multilevel converter (MMC). In this method, the common mode voltage (CMV) is cosidered, which makes the model more accurate. The proposed method realizes the optimization of multiple control objectives of output current tracking, circulating current suppression (CCS), and submodule voltage balancing. The sequence that allows the output current to track its reference value is calculated in the <inline-formula><tex-math notation="LaTeX">$\alpha -\beta$</tex-math></inline-formula> coordinate system. And the virtual vectors aim to suppress the circulating current without influencing the output current are inserted. Capacitor voltage balancing is achieved by redundant vectors selection and tranditional selection algorithm. These control objectives are calculated independently, thus weighting factor is not required. At the same time, the computation burden is reduced by restricting the voltage mutation. This method has a satisfactory dynamic response and steady-state performance, the simulation and experimental results are given to verify the effectiveness of the method.

Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments

Unmanned surface vehicles (USVs) should be allowed to respond to dynamic ocean environments and self-adjust their paths safely and efficiently. In this paper, considering the challenges posed by time-varying, partially unknown complex environments, a novel hierarchical motion planning framework is elaborately designed for a USV, which includes a global trajectory optimization and a local reactive collision avoidance strategy. By encapsulating the intricate nature of ocean environment, a global optimization path planning problem is developed to systematically strengthen the model's adaptability to the complex engineering problem. Incorporating adaptive elite selection and fuzzy probability set, an adaptive-elite GA with fuzzy inference (AEGAfi) is devised to fully exploit the underlying optimization problem, providing high-quality global paths. By applying virtual sensory vector onto the USV's sensing module, the COLREG-compliant local-reaction is achieved by governing feasible actions of USVs under dynamically unforeseen environments. Seamlessly bridged by the transition Clothoid path, the linkage between global optimization and dynamic-avoidance is strengthened by softening the replanning time restriction and maintaining path continuity. Eventually, the motion planning framework merits autonomous global-planning and local-reaction in an organically modular manner. Comprehensive simulations and comparisons in various ocean scenarios demonstrate the effectiveness and superiority of the proposed framework.

Development of CRISPR/Cas9 Construct in Rice (Oryza sativa subsp. indica) Using Golden Gate Cloning Method Towards Drought Tolerance

Rice (Oryza sativa) is a staple food consumed by the majority of the world’s population. Climate change, however, has created a significant threat to our food security as it posed severe effects on rice production. The emergence of genome editing technology has opened a new era in crop improvement. Hence, this study aims to develop the CRISPR/Cas9 construct of drought tolerance for O. sativa subsp. indica cv. IR64 using Golden Gate cloning method. For this purpose, the generation of CRISPR/Cas9 constructs involved several stages, i.e., characterization of SUMO E2-Conjugating Enzyme (OsSCE1) gene, single-guide RNA (sgRNA) design and vector construction. FGENESH, GeneMarkS, InterProScan, and Blast2GO programmes – were used for the OsSCE1 gene characterisation. The putative OsSCE1 gene isolated from IR64 was then verified by sequencing, and the gene was 585 bp long and showed 99% identity with O. sativa on chromosome 10. In silico analysis concluded the gene is involved in abiotic stress regulation. The 20 bp sgRNA was designed manually with the aid of gRNA prediction programmes including CCTop, and Benchling. The virtual vector was validated using the Golden Gate Cloning approach and later confirmed through sequencing. The assembly involved separate vectors containing the OsSCE1 sgRNA sequence, plant selectable marker, and Cas9 cassette to construct standardised elements for hierarchical modular cloning (MoClo). This study demonstrated that our format, as the gene insertion are achievable, resulting in a speedier and more efficient assembly process which may contribute to improve drought tolerance in indica rice. Further study on the Agrobacterium-mediated transformation using the developed construct may be conducted to determine the efficacy of knocking out candidate genes in improving drought tolerance ability O. sativa

Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data.

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.

A robust beamforming for MIMO radar against virtual array steering vector mismatch

AbstractThis letter considers the problem of beamforming in multiple‐input multiple‐output (MIMO) radar. The mismatch phenomenon of MIMO radar virtual array steering vector is addressed, which can better describe the mismatch characteristics of not only one‐way transmitting or receiving path but also the whole transmit‐receive path for a collocated MIMO radar. Then, a new robust beamforming method for MIMO radar against virtual array steering vector mismatch is proposed. The object function of this robust MIMO radar beamformer is constructed from an infinite norm of the output data, which is solved by linear programming. The performance of the proposed beamformer is verified by simulation results. Numerical results illustrate that proposed beamformer exhibits good performance improvement in virtual array steering vector mismatch compared to conventional methods.

Digital twin-based thermal analysis of a frequency synthesizer

A frequency synthesizer is a key component of radars and plays an important role in radar radio frequency transceiver systems. In this study, a digital twin (DT) model of the virtual entity of a frequency synthesizer is proposed to perform thermal analysis. First, the virtual entity of the DT model is established according to the physical entity of the frequency synthesizer. Then, based on the designed DT model of the virtual entity of the frequency synthesizer, the temperature distribution of the model and cooling channel is analyzed, and the temperature distribution of the key components is obtained. Finally, using the results from the DT virtual entity model, a frequency synthesizer temperature prediction method based on a stochastic configuration network (SCN) algorithm is proposed. In the test phase, the characteristic and virtual temperature vectors are used as the input and output of the SCN training set, respectively. Then, 10 different sets of inputs are selected to obtain 10 sets of simulated temperatures and SCN predicted temperatures. The results indicate that the SCN algorithm based on the DT frequency synthesizer can accurately predict the temperature of the physical frequency synthesizer. The thermal analysis of the frequency synthesizer based on the DT provides accurate solutions and reduces the time required by physical components to adjust to high temperatures.

Flux Weakening Controller Design for Series-Winding Three-Phase PMSM Drive Systems

Series-winding three-phase PMSMs have a higher bus voltage utilization than the conventional three-phase PMSMs with star connection. This topology is suitable for applications with a limited bus voltage. However, the zero-sequence current controller will reduce the bus voltage utilization of the series-winding PMSMs, which causes problems in the flux-weakening controller design. The conventional flux-weakening control algorithms will cause the series-winding PMSMs to enter the overmodulation region early and degrade the performance of the zero-sequence current suppression algorithm. In this paper, a new flux-weakening controller with a dynamic fundamental voltage limit (FW-DFVL) is designed for the series-winding three-phase PMSM traction system. Firstly, the space vector modulation method combines the proposed virtual zero-sequence vectors to realize both the fundamental current generation and the zero-sequence current suppression. The accurate bus voltage utilization in the fundamental current subspace can be derived from the proposed modulation method. Secondly, the gradient descent method generates the flux-weakening d-axis reference current with the dynamic fundamental voltage, which will converge faster than the conventional PI-based flux-weakening control scheme. Thirdly, the flux-weakening controller in the overmodulation region is also designed where the zero-sequence current will no longer be suppressed. The bus voltage utilization is Vdc in this operation mode. Finally, both the simulation and experimental results are utilized to verify the effectiveness of the proposed FW-DFVL, where faster dynamic performance and higher bus utilization are observed.

Model-Free Predictive Current and Disturbance Rejection Control of Dual Three-Phase PMSM Drives Using Optimal Virtual Vector Modulation

Model-based predictive current control (MBPCC) relies heavily on adequate system modeling and accurate parameters. The detailed models of dual three-phase permanent magnet synchronous motors (PMSMs) contain mutual cross-coupling dynamics which make it difficult to extract accurate parameters. Also, some of the physical parameters of PMSMs are generally nonlinear functions of current and rotor position. In this article, a model-free predictive current control (MFPCC) based on an ultralocal model and an extended state observer is proposed for an asymmetrical dual three-phase (ADTP) PMSM. The MFPCC method is shown to provide superior current regulation when compared to the standard MBPCC approach under uncertain parameter conditions. Furthermore, the harmonic currents and the current ripple in the ADTP PMSM drive have been regulated to near-zero values by using optimized voltage vectors comprised of virtual vectors and null vectors. A generalized center-aligned pulsewidth modulation (PWM) scheme is presented to facilitate the synthesis of the optimal virtual voltage vectors for implementation on a low-cost digital signal processing platform. The performance improvements of the MFPCC with optimal virtual vector modulation are verified and compared with the conventional MBPCC method in both simulations and experiments on a surface-mount type dual three-phase PMSM.

An Improved MPC With Reduced CMV and Current Distortion for PMSM Drives Under Variable DC-Bus Voltage Condition in Electric Vehicles

The conventional reduced-common-mode-voltage model predictive control (RCMV-MPC) faces the challenges of low control accuracy and high switching frequency at low speeds. To reduce CMV, improve steady-state performance and lower switching frequency, an improved MPC is proposed for permanent magnet synchronous motor (PMSM) drives with variable dc-bus voltage in electric vehicles. Firstly, the virtual vectors with low CMV and switching frequency are defined, the optimal virtual vector and its duty cycle are determined. Then, the limitation of designed virtual vectors with fixed dc-bus voltage is illustrated. Furthermore, based on the analysis of the effect of dc-bus voltage on current distortion, the optimal reference dc-bus voltage is adopted to minimize the current distortion. At last, the dc-bus voltage is adjusted to track the reference value by DC-DC converter. An experimental comparison of the conventional methods and the proposed scheme is carried out. The results have verified that the proposed scheme can reduce CMV, current harmonics and switching frequency, especially in low-speed regions. As DC-DC converter has become more popular in motor drives of electric vehicles, the proposed scheme could have a promising application perspective.

A Virtual Vector Based DTC Scheme With Enhanced Resolution for Dual Inverter Fed Five-Phase IM Drives

A virtual voltage vector based direct torque control (DTC) scheme with enhanced resolution for five-phase induction motor (IM) drives with open-end stator windings, fed by two inverters, is proposed in this article. In the proposed scheme, the voltage space vector plane is divided into 20 sectors, by using 40 virtual voltage vectors produced as resultants of the virtual voltage vectors of the individual inverters thereby substantially increasing the sector resolution compared to the 10-sector based conventional five-phase DTC schemes. This DTC scheme incorporates a five-level hysteresis comparator for torque control and the required voltage vectors of different magnitudes are produced by appropriately combining the vectors of the two inverters. The increased sector resolution along with five-level hysteresis torque controller facilitates finer flux and torque control. As the effective common mode voltage in the motor windings is made zero in every switching period, both inverters can be connected to a common dc supply without causing circulation of zero sequence current. The proposed scheme is validated by performing experiment on a five-phase IM drive.

Open-Phase Fault-Tolerant DTC Technique for Three-Level NPC VSI-Fed Five-Phase Induction Motor Drives

The demand for motor drives based on multiphase machines (MMs) is increasing due to their inherent fault-tolerant capabilities. To achieve satisfactory post-fault operation, a proper fault-tolerant controller is needed. Some direct-torque-control (DTC) techniques have been reported for five-phase induction motor (FPIM) drives under an open-phase fault (OPF). DTC offers simplicity, fast response, and less parameter sensitivity. However, the existing fault-tolerant DTC methods were designed for two-level voltage-source inverters (VSIs). A three-level neutral-point-clamped (NPC) VSI allows greater flexibility in the switching states, lower current and torque ripple, and reduced common-mode voltage (CMV). However, the increased number of switching states requires the design of new DTC methods, able to tolerate OPFs while providing satisfactory performance in various aspects. In this article, two fault-tolerant DTC techniques are developed for three-level NPC VSI-fed FPIM drives under an OPF. The possible switching states are synthesized with suitable virtual vectors (VVs), addressing the dc-link voltage balance, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\text {d}v/\text {d}t}$ </tex-math></inline-formula> , CMV, and dc-link utilization. The two methods are compared under an OPF in these terms, as well as regarding the average switching frequency. Comparative experimental results are provided to assess the viability of the developed DTC schemes at steady-state and dynamic conditions.

Generalized Deadbeat Solution for Model Predictive Control of Five-Phase PMSM Drives

Model predictive control (MPC) has been widely emerged and applied on five-phase PMSM drives. However, the conventional MPC schemes have employed the high-complexity enumeration process and/or virtual voltage vector(s) (V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (s)), which are designed offline in one subspace. Thus, the traditional MPC schemes may have a large computational burden, low tracking accuracy, and/or limited DC-bus voltage utilization. This article proposes a generalized low-complexity deadbeat (DB) solution for MPC schemes of the five-phase drives, which have a double-space control problem. In the proposed solution, the double-space control problem of the five-phase PMSM drive is formulated in a linear system of equations. To have a unique solution, five candidate real vectors are defined and solved for their duties during every sample interval. The optimal voltage vector(s) are directly selected without any enumeration process according to the highest duty cycle(s), which are designed online considering the control objectives of the two-subspace components. Hence, five different MPC schemes can be obtained from this solution. Compared with traditional MPC schemes, the salient features of the proposed DB-based schemes are a low computational burden, improved steady-state performance, high DC-bus voltage utilization, and a simple generalized execution. These features are verified using a five-phase PMSM drive experimental prototype.

Efficient and Accurate Pattern Synthesis for Radome‐Enclosed Planar Arrays Using Iterative FFT via Two‐Dimensional Least‐Square Active Element Pattern Expansion

AbstractThis work generalizes the iterative fast Fourier transform (FFT) via least‐square active element pattern expansion method to efficiently synthesize two‐dimensional (2D) patterns with accurate sidelobe and mainlobe shape control for planar arrays with radomes. The 2D active element patterns (2D‐AEPs) of a planar array‐radome structure are utilized to include the mutual coupling and radome effect. The 2D‐LSAEPE method is presented to approximate each 2D‐AEP as the pattern by exciting a virtual rectangular subarray with identical element patterns, and the weighting coefficients of the virtual subarray are found by minimizing the least‐square error of the 2D‐AEP approximation. With the help of the 2D‐LSAEPE, the pattern of a planar array with a radome can be efficiently calculated by FFT. To apply the FFT via 2D‐LSAEPE for planar array pattern computation, a blocked accumulating matrix is introduced which relates the virtual and physical excitation vectors. In addition, an interpolation‐based pre‐processing technique is implemented to obtain the virtual element pattern data in uniform grid of the (u, v)‐space so as to meet the requirement of employing the 2D‐FFT. By integrating the FFT with the 2D‐LSAEPE and the interpolation technique, an efficient iterative method is developed to implement accurate pattern synthesis for planar arrays with radomes. Three examples of synthesizing different planar array‐radome systems are conducted. Synthesis results show that the proposed method achieves much more accurate synthesis result than the traditional FFT‐based technique, while taking much less computation time than some other methods. This verifies the effectiveness of the proposed method.

Model Predictive Control With Duty Cycle Optimization and Virtual Null Vector for Induction Generator With Six Switch Converter

The incorporation of duty cycle optimization in the finite control set model predictive control increased its advantages, improving its steady-state performance and maintaining its fast dynamics. However, duty cycle optimization depends on the application of a null vector alongside the optimal vector, and some converters do not have a null voltage vector inherently. This is the case of the six-switch converter, a reduced switch count converter that replaces the back-to-back converter with only 50% of the number of switches, and whose union with predictive control has been little exploited. This article develops a model predictive control with duty cycle optimization for a variable speed turbine wind power system with the six-switch converter (SSC) connected to the grid and a squirrel cage induction generator (SCIG). Calculations are performed by the means of a virtual null voltage vector properly applied to the system. Experimental results show the proposed control technique improved the steady-state performance of the system, with lower torque ripples, active power ripples, and current total harmonic distortion, certifying the controller as a feasible technique for the application.