Data-Driven Speed Synchronization Predictive Control of a Five-Leg VSI Driving Dual-PMSM System

The multiphase and multilevel motor drives have attracted more and more attention during the last decade since they offered advantages of high-power rating, lower voltage and current stresses in semiconductor switches, higher fault-tolerant ability, compared to the conventional two-level three-phase drives. This paper presents a model predictive current control (MPC) for a T-type three-level inverters fed dual three-phase permanent magnet synchronous motor (PMSM) drives. The key is to propose new space voltage vector synthesis method for voltage vector candidates in MPC scheme of the T-type three-level inverters fed dual three-phase PMSM drives. The proposed scheme can alleviate the computation burden of MPC and suppress the current harmonics induced from inherent property of dual three-phase PMSM. The experimental results are given to verify the validation of the proposed scheme.

In this paper, the five-leg inverter-dual permanent magnet synchronous motor system is taken as the research object. To solve the problem of large steady-state speed fluctuation when applying the traditional model predictive control strategy, based on the mathematical models of the five-leg inverter and the dual permanent magnet synchronous motor, the causes of the fluctuation are analyzed, and then an improved model predictive control algorithm with error feedback is proposed. The algorithm makes full use of the high-speed operation ability of modern digital processor, and introduces the prediction value of error feedback correction model, so that the rolling optimization is not only based on the model, but also makes full use of the actual value of the feedback state variables. At the same time, the second-order Euler discrete method is used to further improve the model progress. Simulation and experimental results show that the model predictive control strategy with error feedback can effectively improve the steady-state speed control performance of the system while maintaining the dynamic performance of the original system and the decoupling control effect of the two motors.

In this study, a novel mode l of a Hybrid Electric Vehicle (HEV) based off the 2016 Chevy Volt is implemented and examined. The study will also examine the operation of the modified Series Parallel Hybrid Electric Vehicle (SPHEV) and modeled powertrain components contained within. The 2016 Chevy Volt has six unique modes of operation; however, the dual Permanent Magnet Synchronous Motor (PMSM) EV drive as well as the application of regenerative braking is the area of focus for this initial study. In these modes of interest we will examine the following characteristic outputs of our model: Current in/out of Battery, DC converter low voltage side current, state of charge (SOC) of the battery, Voltage at Both sides of DC-DC converter, Vehicle Torque vs Vehicle Speed and power loss through the DC-DC converter. Observing the simulation results conducted in PSIM10 HEV Design Suite we can support the functionality of the model.

To address the issue of speed synchronization deterioration in dual permanent magnet synchronous motor (PMSM) systems under uncertain disturbances, this study proposes a power-law-enhanced nonsingular fast terminal sliding mode (NFTSM) control strategy and introduces an innovative reaching law. This synchronization architecture embeds the enhanced sliding mode algorithm within a cross-coupled control framework, where the redesigned reaching law is specifically tailored to optimize steady-state operating characteristics. For individual motor regulation, an improved deadbeat predictive current control (DPCC) method incorporating a super-twisting sliding mode observer (STSMO) resolves inherent one-step delay limitations and parameter model discrepancies. This observer architecture converts discontinuous control variables into higher-order sliding surface derivatives, significantly mitigating the characteristic chattering phenomenon prevalent in classical first-order sliding mode implementations. The STSMO also adopts a predictive current substitution method to eliminate time-delay effects. Comprehensive simulation analysis shows that compared to traditional PI control, there are significant improvements in three operational aspects: system anti-interference capability, speed trajectory tracking accuracy, and inter-motor synchronization consistency.

Multi-phase permanent magnet synchronous motors (PMSM) are widely used in electric vehicles owing to their merits such as high efficiency, high precision and high reliability. This paper presents a novel dual measurement vector injection method (DMVIM) for dual three-phase PMSM using two hall-effect DC link current sensors. The principle of phase current reconstruction method is described as well as the basic driving system of dual three-phase PMSM. The mathematical model of dual three-phase PMSM is established. Then, the DMVIM is presented in detail and the reference voltage vector trajectories of the two windings are analyzed. Furthermore, since the extra voltage vectors are introduced with this method, the compensation algorithms of DMVIM are proposed to modify the reconstruction errors. Both the simulation results and the experimental results validate the proposed DMVIM as well as its compensation algorithms.

This paper presents a coordinated single-loop speed control strategy for dual permanent magnet synchronous motors (PMSMs) sharing a common mechanical shaft. Traditional cascaded control frameworks, while effective in dynamic response and disturbance rejection, often suffer from bandwidth limitations and increased system complexity, especially in multi-motor configurations. To address these limitations, this work proposes a simplified single-loop control structure that enables coordinated speed tracking while considering the unequal load-carrying capacities of the two motors. The proposed method incorporates an online load distribution mechanism, ensuring efficient and safe torque sharing without requiring inner current control loops. A unified speed feedback is used for both motors, while their individual torque contributions are adjusted according to real-time operating conditions. The effectiveness of the proposed control strategy is verified through extensive simulation studies conducted in MATLAB/Simulink.

This paper proposes an improved model predictive control (MPC) scheme with a robust prediction and stability-constrained finite states for three-phase voltage source inverters ( $3\phi $ -VSIs) with an $LC$ filter. In this paper, the stability-constrained finite states are selected via the asymptotic stability conditions as a key factor to reduce the total harmonic distortions (THDs) and steady-state errors. Meanwhile, the Kalman filter-based observers improve the overall robustness against model mismatches and noises via a robust prediction. To select the stabilized finite states, the stability conditions are derived by the equivalent feedback-gains and constrained in the exhausting search of the proposed finite-set (FS) MPC. Unlike conventional FS-MPC methods, three control targets (i.e., robustness, stability, and optimality) are simultaneously achieved as the new contributions to remarkably enhance the voltage control performance of the $3\phi $ -VSI, which are also the challenge of the conventional control methods. To verify the superiority of the proposed FS-MPC, comparative studies are conducted on a prototype three-wire $3\phi $ -VSI system with a TI TMS320F28335 DSP under practical conditions (i.e., parameter mismatches, linear/nonlinear-load step-changes). The experimental results confirm that the performance of the proposed FS-MPC has been significantly improved in terms of lower THDs, smaller steady-state errors, faster dynamic response, and more robustness under critical system changes as compared with the conventional FS-MPC.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

This paper presents the experimental and simulation test results of the independent vector control characteristics of two three-phase permanent magnet synchronous motors (PMSMs) fed by a five-leg inverter (5LI). The 5LI is a single inverter that can drive two motors independently. In this paper, experimental results are verified for the independent position and speed control in constant torque region of two PMSMs fed by the 5LI with vector control. Simulation results are verified for the independent position and speed control including constant power region of two PMSMs fed by the 5LI with the vector control.

To address the issue of reduced motion accuracy in tracked sandblasting robots caused by nonlinear slippage on steel grit-covered hard surfaces, a hierarchical control framework combining model and data learning is proposed to enhance dual-motor coordination precision and terrain disturbance suppression capability. The inner layer employs a slip-mode control-cross-coupling (SMC-CCC) algorithm, combined with a novel composite approach law, to simultaneously optimize motor tracking error and synchronization error. The outer layer employs a coupled simulation of multi-body dynamics and discrete element method (MBD-DEM) to generate high-fidelity data, which trains a radial basis function neural network (RBFNN) to predict and compensate in real time for slip disturbances caused by track-ground interactions. The standard deviation of dual permanent magnet synchronous motor synchronous error decreased to 1.12, while the standard deviation of tracking error dropped to 7.86. After RBFNN compensation, vehicle lateral deviation was reduced to 70.08 mm—a 92.28% decrease compared to the uncontrolled condition—with slip rate stabilizing between 20% and 25%. In summary, the proposed hierarchical framework effectively addresses motion instability issues beneath steel grit surfaces: SMC-CCC ensures motor coordination precision, while RBFNN achieves adaptive compensation for nonlinear slippage based on MBD-DEM data, providing a viable solution for high-precision motion control in complex terrain.

This paper presents an impartial comparison of the dynamic behavior of permanent magnet synchronous motors (PMSM) under three control techniques i.e., model predictive current control (MPCC), model predictive direct speed control (MPDSC) and field-oriented control (FOC). In MPCC, the voltage vectors applied to the inverter are determined by minimizing a cost function comprising the error in currents; whereas, in MPDSC, the voltage vectors applied to the inverter are determined according to minimization of cost function which includes speed and current errors. In MPCC, PI controller is used to regulate the speed, whereas in the MPDSC, there are no PI controllers. When the FOC is applied, modulated voltage vectors are determined according to three PI controllers for speed and current control. Most crucial implementation issues, which affects the dynamic performance of PMSMs, are addressed during the comparison between three control techniques. These issues include delay time, switching frequency and parameters mismatch. The delay time, due to the large number of calculations, is compensated by predicting the state variables two sampling intervals ahead. Switching frequency is included in the objective function to reduce switching losses. The effect of delay time and switching frequency is related to MPCC and MPDSC; meanwhile parameters mismatch is associated with all control methods. The dynamic behaviors are assessed via simulation results at different load conditions. The final evaluation was performed in terms of rising time, settling time, total harmonic distortion and average switching frequency.

Vector control has been widely used to manipulate the permanent magnet synchronous motor (PMSM), in which the traditional double proportional integral (PI) controllers have commonly been utilized. However, it is difficult to set the suitable parameters of PI and maintain the PMSM performance under the different operating regimes. In order to solve these problems, a double closed-loop model predictive control (MPC) strategy based on the vector control has been presented for PMSM in this paper. Firstly, a simplified discrete model of PMSM is established in the d-q coordinate system without considering the interference. Then, the MPC is developed to substitute the PI controller in the speed-loop and the q-axis current-loop, respectively, and a double closed-loop MPC control system is formed. Finally, some case studies are carried out. The simulation and experimental results demonstrate that the presented MPC strategy improves the dynamic and steady-state performance of PMSM.

In this paper, using the two control methods of model predictive control (MPC), model predictive current control (MPCC) and model predictive torque control (MPTC) is applied to the control system of permanent magnet synchronous motor(PMSM). The algorithms are based on the minimization of different cost functions, and the corresponding optimal voltage vector can be obtained. Comparison of the dynamic and steady state performance of the tested PMSM with MPCC and MPTC is elaborated. The delay compensation is considered to reduce the fluctuation of current and torque, in order to reduce the computational burden by using a single vector, comparison of two methods in the speed control system of dynamic and steady-state performance and speed tracking performance and resistance to load disturbance. The simulation results show that both MPCC and MPTC can be well applied to the PMSM driving systems with good speed tracking performance. However the MPCC can achieve less harmonics in the stator currents, and consequently the circular flux linkage trajectory is smoother, while the MPTC shows advantages in smaller ripples in the mechanical torque.

In this paper, a novel speed control scheme for the single inverter dual permanent magnet synchronous motor (SI-DPMSM) drive system is proposed, based on the sliding mode control (SMC) theory. For stable operation of the SI-DPMSM drive, active damping control should be added. Even though the parameters of the two motors do not match with each other, the stable control performance is obtained by the sliding mode control which is robust to disturbance and model uncertainties. The proposed controller has been verified by simulation and experimental results.

A fault-tolerant control method is proposed for dual-permanent magnet synchronous motor (PMSM) drive in the application of electric vehicles. After the inverter fails, the faulty leg is immediately disconnected from the drive by means of hardware isolation. The proposed method based the improved hysteresis controller is adopted to "sum" the currents of the failed bridge legs and directly drive the remaining five legs, which keeps the two motors running steadily even if there is a fault in the inverter. Finally, the effectiveness of the fault-tolerant method proposed in this paper is validated by the experiment.