Model-Free predictive current control of permanent magnet synchronous motor based on inductance identification

Conventional predictive control (PC) has been employed for machine drives due to good dynamic response and easy implementation. However, the disturbance caused by machine parameter mismatch is one of main barriers to its widespread application. In order to deal with this issue, this work proposes a novel model-free predictive current control (MFPCC) for surfaced permanent magnet synchronous machines (SPMSM). The contribution of this work is that a novel extended state observer (ESO) is proposed and the proposed MFPCC only utilizes the input and output knowledge of the plant without using any machine parameter in controller, which can effectively suppress the disturbances. In addition, the effect of the initial machine inductance parameter mismatch on the conventional ESO of MFPCC is analyzed in detail. The proposed MFPCC is validated and compared against three methods, namely PC without ESO, PC using ESO, and MFPCC using ESO. The experimental results have been carried out to verify the effectiveness of the proposed MFPCC.

This paper proposes an improved model-free predictive current control (MFPCC) scheme for four-switch three-phase (FSTP) inverter-fed synchronous reluctance motor (SynRM) drive systems. Unlike the existing model-based predictive current control (MBPCC), the improved MFPCC does not need any motor parameters due to the current sensor based approach, where only once current measurement is required in each sampling interval. According to the measured stator current and pre-saved current differences, the future stator current can be predicted without estimating the back-EMF of motor. Next, a cost function is defined to compute all possible future current errors under four switching states generated by the FSTP inverter. The next switching state can be decided to control the FSTP in the next sampling interval to obtain the cost function. Additionally, a passivity-based adaptive backstepping complementary sliding-mode (PBABCSM) controller is adopted in this study to improve the position tracking performance of the FSTP inverter-fed SynRM drives. The stability of the closed-loop system has been verified through passivity theory and Barbalat's lemma. Furthermore, simulation results are provided to validate the effectiveness of the proposed method.

With the high-speed development of digital signal processors, model predictive current control (MPCC) has been widely used in power converters. However, the control robustness of MPCC is poor because of its strong dependence on the model parameters. In this paper, an ultra-local model-free predictive current control (MFPCC) for three-level grid-connected inverters (GCI) with LCL filters is proposed. Based on ultra-local theory, a third-order ultra-local model of the GCI with LCL filters is constructed. Then, a Kalman filter (KF) is introduced to estimate the three perturbations. Moreover, the perturbations are compensated to the predictive current model, thus reducing the number of model parameters involved in the predictive control. Finally, simulation results show that the proposed MFPCC improves the tracking performance of the grid current and strengthens the dynamic response capabilities under the parameter mismatch. Furthermore, the output power quality becomes higher, and the system robustness is also enhanced under noisy environment.

To lessen parameter dependence and suppress current harmonics, a continuous-control-set model-free predictive current control (MFPCC) strategy with voltage vector correction and duty cycle optimization is proposed for five-phase permanent magnet synchronous machine (PMSM) drives. Firstly, the MFPCC method with an ultralocal model is developed based on the mathematical model of a five-phase PMSM and the virtual voltage vector (VVV). The lumped parameters of the ultralocal model are estimated with current and voltage variations rather than observers. Thus, the stability of the five-phase PMSM drive is not affected. Secondly, the reason for the current distortion is investigated by analyzing the numerator and the denominator of the division operation in uncertain terms identification. A voltage vector correction mechanism is accordingly designed based on the power factor angle to decrease current distortions. Meanwhile, the duty cycle of the selected VVV is optimized to further reduce the tracking error. The proposed strategy not only theoretically eliminates the impact of machine parameters but also improves steady-state performance. Finally, the effectiveness of the MFPCC is validated by experiments.

Model Predictive Control (MPC) is a simple and efficient strategy for inverter control. However, its control performance is highly susceptible to the accuracy of model parameters. Although the extended state observer (ESO) addresses the issue of model parameter dependence, it can still cause system instability when the inductance parameter mismatch is severe. To alleviate the adverse impact of inductance parameter mismatch on control performance, this paper proposes a model-free predictive current control (MFPCC) algorithm based on an improved extended state observer (IESO) for the three-level neutral-point-clamped (3L-NPC) inverter system. First, an ESO is constructed on basis of the system’s ultra-local model. Second, the voltage gain coefficient of the ESO is dynamically updated to realize optimization of the observer’s performance. Notably, this coefficient can be derived solely based on the historical voltage and current data of the system. Simulation results demonstrate that the proposed IESO solves the problem that the conventional ESO contains excessive high-frequency harmonic components in the output current when the inductance parameter mismatch is severe, effectively reduces the total harmonic distortion (THD) of the output current, maintains the neutral-point voltage balance, and significantly enhances the system robustness.

Traditional model predictive current control (MPCC) is heavily dependent on the accuracy of motor parameters and incurs high computational costs. To address these challenges, this paper proposes an enhanced model-free predictive current control (MFPCC) strategy based on ultra-local models (ULMs). Initially, a Kalman filter (KF) is used to estimate the current gain, while an adaptive sliding mode observer (SMO) is employed to estimate current disturbances. Subsequently, an equivalent transformation of the cost function is carried out in the αβ domain, and the voltage vector combinations are reduced to a single one via sector distribution. Hence, the proposed MFPCC is independent of motor parameters and capable of reducing computational complexity. Simulation and experimental results demonstrate that the proposed MFPCC method significantly improves computational efficiency and the robustness of current prediction, enabling precise current tracking even in the presence of motor parameter mismatches.

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.

Conventional model predictive power control (MPPC) features a simple concept and quick dynamic response. However, it relies heavily on the system model and its parameter accuracy. Furthermore, the steady ripples are still high due to the use of one voltage vector during one control period. Recently, model-free predictive current control (MFPCC) has been proposed in the current control of PWM rectifiers. Despite the strong parameter robustness, the principle of MFPCC cannot be directly applied to power control, because the relationship between power and converter voltage is more complex. This paper first proposes a basic model-free predictive power control (MFPPC), which successfully extends the principle of MFPCC to power control. Subsequently, an improved MFPPC is proposed, which uses an extended finite control set of voltage vectors to improve the steady-state performance. Furthermore, by using the online updated ultralocal model of PWM rectifiers, the problem of stagnant power updating in basic MFPPC is solved. The ideal three-phase grid voltages are symmetrical and sinusoidal, but the actual grids are usually unsymmetrical. In this paper, the proposed method is extended to asymmetrical power grids by adding an appropriate compensated power to the original power references. The proposed basic MFPPC and improved MFPPC are compared to conventional MPPC. The presented experimental results confirm the effectiveness of the proposed methods.

Although the robustness is enhanced essentially by the model-free predictive control, the accuracy of its data-driven model is directly affected by the unnecessary current harmonics, such as the harmonics caused by the control strategy itself. To reduce the influences of these harmonics, a continuous-control-set model-free predictive fundamental current control (MF-PFCC) strategy is proposed in this article for the permanent magnet synchronous motor driving system. Harmonics due to the controller including side frequency effect and frequency coupling effect are analyzed in principle, and a frequency-converting double second-order generalized integrator structure is designed to exclude the fundamental terms from the sampled current to cut the coupling path of the effects and to improve the accuracy of the data-driven model in the MF-PFCC without any time-variable physical parameters. The analyses of the effects and the effectiveness of the proposed method are verified by experimental and supplementary simulation results, as well as advantages including the reduced influence of effects and enhanced robustness with improved stator current quality.

We will apply dual three-phase permanent magnet synchronous motors (DTP-PMSM) to the road sensing unit of the steer-by-wire system to improve redundancy and fault tolerance performance, meeting the requirements of intelligent transportation systems. However, open circuit faults inevitably lead to periodic torque ripple, parameter mismatch, and current harmonics, which pose a huge threat to drivers and traffic participants. Therefore, this article proposes a dynamic balancing strategy that can operate stably under both motor health and fault conditions. Firstly, this strategy does not require diagnostic fault information, thereby fundamentally avoiding performance degradation caused by diagnostic accuracy, time, model reconstruction, and strategy switching. The torque control module (TCM) is based on adaptive iterative learning control with a forgetting factor, which achieves torque tracking and suppression of periodic ripple after faults. The current control module (CCM) is based on ultra local model free predictive current control and extended state observer to track current and suppress the effects of parameter mismatch and current harmonics. Based on the multi-variable multi-objective sliding mode extreme value search strategy, the internal parameters and external weights of the two modules are dynamically adjusted to achieve dynamic balance between health and fault operation of TCM and CCM. The experimental results show that this strategy effectively improves the robustness of the system in a healthy state and the fault tolerance performance in a faulty state, effectively suppressing the periodic torque ripple and current harmonics caused by faults. Compared with the dynamic balancing strategy based on NSGA-II, this strategy increases the THD of torque by 13.45% and reduces the THD of current harmonics by 9.83%.

The robust deadbeat current predictive control (DPCC) is typically integrated with an extended state observer (ESO) to cope with uncertainties and disturbances of PMSM system, but usually fail to filter current harmonics caused by periodic disturbances, such as inverters nonlinearities. Furthermore, conventional DPCC primarily relies on forward difference discretization, which confronts stability challenges in the presence of parameter mismatches. Aiming at the above problem, a backward difference model free deadbeat predictive current control (BFDPC) strategy integrated with an improved extended sliding mode observer (IESMO) is proposed to simultaneously compensate for harmonic disturbances and lumped disturbance of PMSM system. Firstly, conventional DPCC method with sliding mode ESO is analyzed and the ESO amplitude characteristics with bandwidth is found. Secondly, an IESMO is proposed to simultaneously suppress the lumped and harmonic disturbances. Thirdly, a BFDPC method based on backward difference model by utilizing the Newtown-Raphson technique is designed to enhance the stability to parameter mismatches in DPCC. Finally, comparative experiments of DPCC, ESO-DPCC, and BFDPC strategies are implemented on a 64W PMSM experimental platform, and experimental results indicate that current harmonics under the proposed strategy can be effectively suppressed at both dynamic and steady conditions.

Model predictive control has been extensively researched for many years, which has the advantages of clear principles and fast dynamic response. However, it also has the disadvantage of high sensitivity of system parameters, which requires accurate system models and system parameters to exert its performance. Aiming at the problem that model predictive control is sensitive to the parameters of dual three-phase permanent magnet synchronous motor (DTP-PMSM), a model-free predictive current control method for DTP-PMSM based on the ultra-local model is proposed. The strategy establishes the current ultra-local model of the DTP-PMSM through the current and voltage information of the system. Then based on the current ultra-local model, a model-free stator current controller which does not depend on the DTP-PMSM parameter model is designed. Finally, the comparative analysis of the simulation experiment proves the validity and correctness of the proposed method.KeywordsModel-free predictive controlDual three-phase permanent magnet synchronous motor (DTP-PMSM)Ultra-local model

Model-based predictive control that highly depends on system parameters has been widely investigated. In contrast, model -free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. In this paper, a model free predictive control method based on discrete space vector modulation is used, which increases the effective vector control set by constructing a virtual vector which is composed of linear combination of real vectors. The THD of grid connected current is reduced. Simulation and experimental results confirmed the effectiveness of the proposed method.

本文主要研究表贴式永磁同步电机的无差拍预测电流控制(DPCC)，并提出了一种考虑采样延迟的控制策略，以克服数字控制中的延迟问题。模型参数不匹配是影响该控制方法广泛应用的主要障碍之一。为解决这一问题，本文提出了一种基于在线参数辨识的DPCC方案，通过模型参考自适应方法辨识电阻和电感，并结合q轴电流误差来辨识磁链参数，从而实现电机参数的动态更新，提升DPCC系统对电机参数变化的鲁棒性。为验证所提方案的有效性，进行了MATLAB/SIMULINK平台的仿真，并与传统DPCC进行了对比分析。结果表明，所提出的方案能够准确辨识定子电阻、电感及磁链参数，并有效抑制参数失配对DPCC系统性能的影响。This paper primarily studies the direct predictive current control (DPCC) for surface-mounted permanent magnet synchronous motors (SPMSM) and proposes a control strategy that considers sampling delay to overcome the delay issues in digital control. Parameter mismatch is one of the main obstacles affecting the widespread application of this control method. To address this issue, this paper presents an online parameter identification-based DPCC scheme. The scheme utilizes a model reference adaptive method to identify resistance and inductance, and combines the q-axis current error to identify the magnetic flux parameters, enabling dynamic updates of motor parameters and enhancing the robustness of the DPCC system against variations in motor para- meters. To validate the effectiveness of the proposed scheme, simulations were conducted on the MATLAB/SIMULINK platform and compared with traditional DPCC. The results demonstrate that the proposed scheme can accurately identify the stator resistance, inductance, and magnetic flux parameters, effectively mitigating the impact of parameter mismatch on the performance of the DPCC system.

To solve the problem that the conventional vector control system of permanent magnet synchronous motor is susceptible to motor parameters and load disturbances, a sliding mode predictive current control strategy of permanent magnet synchronous motor with cascaded variable rate sliding mode speed controller based on extended state observer (ESO) is proposed, which improves the dynamic and static performance of the system. First, a sliding mode speed controller is designed based on the novel variable rate exponential reaching law, and the ESO is used to estimate and compensate for parameter mismatch and disturbance, which improves the system’s rapidity and robustness while reducing the chattering. Then, to solve the problem that the traditional deadbeat predictive control of current loop ignores the influence of cross-coupling electromotive force, based on the above reaching law, a sliding mode controller is designed to equate the back electromotive force to effectively compensate the deadbeat predictive current controller, which not only retains the rapidity of current but also enhances the robustness. Finally, simulation and experimental results show the effectiveness of the proposed control strategy.