Simplified Model Predictive Control for Dual Inverter-Fed Open-Winding Permanent Magnet Synchronous Motor

This paper proposes a novel pseudo 3-D space vector modulation (SVM)-based sequential model predictive control (SMPC) for the open-winding permanent magnet synchronous motor (OW-PMSM), which eliminates the weighting factor and improves the current quality. In the OW-PMSM drive with a common dc bus, because of the large difference between the 0-axis and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes impedance, the weighting factor is required in traditional model predictive control (MPC) to balance the 0-axis and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes current control. To avoid the weighting factor and its tedious tuning work, an improved SMPC is put forward, where two sequential predictive stages are established to evaluate voltage vectors (VVs). In the first stage, several possible zero-sequence voltage components of all candidate VVs are evaluated to obtain the VV group with the best 0-axis current tracking performance. In the second stage, according to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i>-axes current control, a multivector-based modulation scheme is designed to synthesize the optimal VV. Due to the characteristics of the modulation process in two predictive stages, the proposed modulation process is named pseudo 3-D SVM, which achieves better current quality and output performance. Experimental verification is provided to prove the effectiveness of the proposed scheme.

Nowadays, Predictive Torque Control (PTC) strategy is recognized as a strong tool for controlling the motor drive. Intuitive and multi-objective controlling are significant benefits of PTC. Owing to these benefits, its application is introduced for Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drive. The basic PTC operated OW-PMSM drive consequences high Common Mode Voltage (CMV) and leads to early failure of motor bearings. In this paper, CMV elimination is proposed for OW-PMSM drive using voltage vector selection in PTC operation. In proposed PTC, the possible voltage vectors (VVs) are identified to gain zero CMV and preselected as prediction VVs for cost-function evaluation. From cost-function evaluation, optimal VV is considered for controlling OW-PMSM drive. In addition, decrease in switching frequency is achieved through proper utilization of optimal VV’s redundant switching states. Therefore, the overall modifications in proposed PTC of OW-PMSM drive ensure simple operational control, CMV elimination, and switching frequency and loss reduction. The claims of proposed PTC are verified through Matlab/Simulink platform and its proficiency is highlighted against basic PTC operation. Thus, the enhanced operation of proposed PTC for OW-PMSM drive with zero CMV is justified.KeywordsCommon Mode Voltage (CMV)Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drivePredictive Torque Control (PTC)

In the open-winding permanent magnet synchronous motor (OW-PMSM) system fed by dual inverter with common dc bus, suitable control strategy should be adopted to suppress the torque ripple generated by the third harmonic flux and 0-axis current. However, the mathematical model will change when the motor is under open-phase fault so that the coordinate transformations and control strategy should be redesigned accordingly in order to suppress the torque ripple. In this article, the postfault OW-PMSM system is remodeled considering the third harmonic flux. Based on the postfault model, new coordinate transformations are designed to obtain a concise torque and decoupling voltage expressions for the simple and accurate control of torque. Moreover, the torque ripple is suppressed by modifying the current reference. In addition, a simple decoupling modulation is designed to directly obtain the switching signals for the dual inverter. Experimental results confirm the effectiveness of the proposed fault-tolerant control strategy.

Multi-level model predictive controller with satisfactory optimization for multi-level converters

The research on grid-connected inverters (GCI) is gaining momentum as more and more focus is given to clean energy sources like solar photovoltaic (PV) and wind energy. Conventional GCI uses the proportional-integral (PI) controller in conjunction with the sinusoidal pulse width modulation (SPWM) technique for pumping power to the grid. Model predictive control (MPC) is becoming more relevant in the field of power electronics. Conventional MPC uses a single prediction horizon or single step. This paper proposes the estimation of the optimization criterion for more than one prediction horizon, termed a multistep MPC. The multistep MPC is an extension of the basic MPC. The MATLAB/Simulink simulations of the proposed multistep MPC and the conventional single step MPC are presented in this paper. The total harmonic distortion (THD) values are compared between the conventional MPC and multistep MPC for varied operating conditions. The settling time of the controllers as well as the deviations in the cost function are compared. The simulation results indicate a better performance of the multistep MPC in comparison with a conventional single horizon MPC.

To improve the parameter robustness of the open-winding permanent magnet synchronous motor (OW-PMSM), this paper proposes an improved model-free predictive current control (MFPCC) method. First, the first-order ultra-local model of OW-PMSM is established, which does not contain any motor parameter. Secondly, a sliding mode observer (SMO) is introduced into the model to estimate the current in d-q-0 axis. Then, to suppress the torque ripple generated by the zero-sequence current (ZSC), the central hexagonal modulation strategy and the q-axis current injection method are combined, and the compensation current that needs to be injected into the q-axis is calculated according to the estimated value of ZSC. Finally, after considering the one-step delay, the voltage reference at the next instant is calculated based on the estimated values of d-q axis current and compensation current. Simulation and experimental results indicate that the proposed method can improve the parameter robustness of OW-PMSM, and the performance of current and torque has been improved under various parameter mismatch conditions.

This article presents a generalized control heuristic to reset supply air temperature set-points for direct-expansion units with capacity modulation and variable airflow and integrates the heuristic into a simplified model predictive control framework that precools building thermal mass through zone temperature set-point adjustments. The resulting control methodology is appropriate for control of medium-sized commercial buildings that employ variable air volume air distribution with direct-expansion units for primary cooling. The heuristic is shown to work well for different direct-expansion systems with different compressor types and fan–duct combinations. To assess the integrated energy savings potential of the heuristic strategy and simplified model predictive control, a simulation platform was developed for a medium-sized commercial building. Simulations were carried out for a 100-day cooling season in different U.S. locations and under different control strategies. The heuristic control provided energy savings of between 5% and 10% relative to conventional control, depending on the system type and location. Approximately 4% additional energy savings was achieved when the heuristic was integrated into a simplified model predictive control having only moderate computational requirements. The cost savings could be significantly greater if time-of-use and demand charges were considered.

The traditional model predictive torque control (MPTC) for permanent magnet synchronous motor (PMSM) utilizes the voltage equation in the rotor synchronous rotating coordinate system to predict the values of the next control period. Multiple coordinate rotation transformations with large number of calculations are required. Besides, the current model flux observer is utilized, which is easily affected by the motor parameters. In order to solve these problems, a simplified MPTC is proposed in this paper. The corresponding variable values of the next control period are directly predicted in the static coordinate system, thus the computational burden is significantly reduced. In addition, the proposed strategy is easy to combine with other high-performance stator flux observer techniques to further improve system performance. The experiment verifies the feasibility and effectiveness of the proposed strategy.

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conventional model predictive control (MPC), which uses open-loop optimization or simplified closed-loop optimal control techniques in each horizon. In LPC, the control task in each horizon is formulated as a closed-loop nonlinear optimal control problem and a finite-horizon iterative reinforcement learning (RL) algorithm is developed to obtain the closed-loop optimal/suboptimal solutions. Therefore, in LPC, RL and adaptive dynamic programming (ADP) are used as a new class of closed-loop learning-based optimization techniques for nonlinear predictive control with stochastic disturbances. Moreover, LPC also decomposes the infinite-horizon optimal control problem in previous RL and ADP methods into a series of finite horizon problems, so that the computational costs are reduced and the learning efficiency can be improved. Convergence of the finite-horizon iterative RL algorithm in each prediction horizon and the Lyapunov stability of the closed-loop control system are proved. Moreover, by using successive policy updates between adjoint time horizons, LPC also has lower computational costs than conventional MPC which has independent optimization procedures between two different prediction horizons. Simulation results illustrate that compared with conventional nonlinear MPC as well as ADP, the proposed LPC scheme can obtain a better performance both in terms of policy optimality and computational efficiency.

Improved model predictive current control (MPCC) strategies in 3-D space are proposed in this article for an open-winding permanent-magnet synchronous motor (OW-PMSM) system with a common dc bus. In order to simply determine the optimal voltage vector (VV), a cost function related to VVs is established to replace that related to stator currents in conventional MPCC and the weighting factors are obtained by theoretical derivation. In order to reduce the calculation time of the optimal VV, a 3-D space system is established and the candidate VVs and the reference VV can be placed in the 3-D space. Then by means of the space geometric theory, the optimal VV can be selected faster and only four calculations are required in each sampling period. For better tracking of the reference VV, a VV-tracking (VVT) MPCC strategy is proposed and three VVs are selected according to the volt-second balance principle. Experimental results verify the effectiveness of the proposed strategies.

Due to the evolution of high processing microprocessors, the model predictive control (MPC) has been widely used in power electronic applications. In spite of simplicity, flexibility and fast dynamic response, the conventional MPC (C‐MPC) has a drawback of computational burden. This study focuses on Lyapunov MPC (L‐MPC) method, in which Lyapunov control law is employed in the cost function to minimise the error between the desired control variables and the actual control variables of three‐phase four‐leg inverter to optimise the closed‐loop system performance. The proposed control algorithm takes advantage of a predefined Lyapunov control law which minimises the required calculation time by the Lyapunov model equations just once in each control loop to predict the future variables. L‐MPC technique improves the digital speed by 23.8% as compared to C‐MPC. It reduces current tracking error and total harmonic distortion (THD) in the variation of inverter control parameters. The stability of the system is established through Lyapunov function with the help of backsteping control method. The LabVIEW field programmable gate array (FPGA) rapid prototyping controller is used to validate the proposed control method. The experimental results showed that the proposed system has better performance as compared to C‐MPC.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the “PI-like fuzzy controller”. Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller.

This article proposes a simplified finite-set model predictive control (FS-MPC) algorithm for full-frequency operation of five-level nested neutral-point-clamped (5L-NNPC) inverters. Compared to the existing FS-MPC of 5L-NNPC inverters, the proposed algorithm substantially reduces the computational burden and simplifies the tuning of the weighting factor while ensuring low current harmonics and full-frequency capacitor voltage balance. The full-frequency operating conditions that the candidate sets should satisfy are first acquired considering the periodic characteristics of the capacitor voltages. On this basis, a computationally efficient varying candidate set selection method is proposed for high-frequency operation, while a simple compensation set is introduced for low-frequency operation. Besides, an online approach is proposed to calculate available switching states for different candidate sets, eliminating tedious offline preprocessing. Furthermore, a staged cost function evaluation method is presented to further simplify the computation. Experimental results validate the proposed algorithm.