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In order to improve the stability of sensorless high-speed operation of permanent magnet synchronous motors and effectively expand the speed range, a voltage closed-loop flux weakening sensorless algorithm based on the active flux model is proposed. Firstly, the mathematical model of sensorless control of the PMSM is designed based on the active flux model, and the state of the PMSM flux weakening operation is analyzed. Then, based on the voltage closed-loop flux weakening control method, the corresponding flux weakening control algorithm is analyzed and designed. Meanwhile, based on the active flux model, the speed and rotor position of the motor are observed by sliding mode observer and phase-locked loop method. After that, the flux weakening control method and sensorless control method are combined to realize the sensorless flux weakening control method to improve the stability of the control system. Finally, the proposed algorithm is validated on the experimental platform. The experimental results show that the proposed method can prevent the system from losing control during flux weakening processes, effectively improve system stability, and have smaller angle errors. The speed convergence time is shortened by 80% compared to the non flux weakening control. [ABSTRACT FROM AUTHOR]

A model prediction based leading angle flux weakening control method is proposed to improve the dynamic and steady-state performance of permanent magnet synchronous motors during the flux weakening process. First, the mathematical model of a permanent magnet synchronous motor is used to construct the prediction model in this method, and then a thorough analysis of the permanent magnet synchronous motor's flux weakening control procedure is carried out. Secondly, based on the principle of model predictive control and the existing delay problems, the corresponding delay compensation method is proposed, and the leading angle flux weakening control method is applied to the proposed model predictive control algorithm, so as to achieve flux weakening speed-up control. Finally, the prototype is used to confirm the effectiveness and precision of the proposed technique. The experimental results show that the leading angle flux weakening control method based on model prediction has faster dynamic response to speed and current than the traditional vector flux weakening control method. At the same time, the steady-state current amplitude is smaller, which has superior current control. [ABSTRACT FROM AUTHOR]

A sensorless control method based on active disturbance rejection control (ADRC) and left inverse of a fuzzy neural network is proposed to realize the sensorless control of permanent magnet synchronous motor (PMSM) for machine tools. Firstly, on the basis of analyzing the mathematical model of PMSM and the theory of left inverse system, a left inverse system observer is constructed. Secondly, after verifying the left reversibility of the PMSM control system, the fuzzy neural network is used to construct the left inverse system, and the left inverse system is connected with the PMSM control system in series to realize the sensorless control of the PMSM. Thirdly, according to the mathematical model of PMSM and the sensorless speed observation results, an ADRC method to improve the sensorless control effect is proposed. Finally, the experimental platform of the sensorless control method based on ADRC fuzzy neural network left inverse is built. The experimental results show that the method can estimate the speed and position well. [ABSTRACT FROM AUTHOR]

A ux weakening (FW) control method of leading angle for a permanent magnet synchronous motor (PMSM) based on active disturbance rejection control (ADRC) is proposed to solve the problem of large uctuation of speed, current, and torque in the control process. Firstly, according to the mathematical model of PMSM and its voltage and current constraints, the leading angle FW control algorithm is introduced. Then, according to the ADRC theory and the mathematical model of PMSM, the speed loop ADRC and current loop ADRC are constructed. The controller parameters are combined with the control bandwidth, and the parameter variation ranges are obtained by analyzing the stability of the control system. Finally, the proposed ADRC methods are combined with the leading angle FW control method to realize the ADRC leading angle FW control for PMSM, and the proposed method is verified on the experimental platform. The experimental results show that the proposed method has less speed, current, and torque uctuations than the proportional integral (PI) controller method, which can effectively improve the motor control performance. At the same time, the controller parameters are combined with the bandwidth, which is convenient for practical engineering application. [ABSTRACT FROM AUTHOR]

A model predictive control method for permanent magnet synchronous motor based on parameter identification and dead time compensation is proposed to solve the problems of poor parameter robustness and large current errors. In this method, the prediction model is firstly established based on the mathematical model of the permanent magnet synchronous motor. After that, the current error caused by the parameter change in the prediction model and the current harmonics caused by the dead time effect are basically analyzed theoretically. Then, the adaptive linear neural network algorithm is proposed to identify the motor parameters and applied to the prediction model, and the harmonic components are filtered out using the adaptive linear neural network algorithm. The recursive least squares algorithm is used to quickly update the system weights to improve the dead time compensation control effect. Finally, the effectiveness and correctness of the proposed algorithm are verified on the experimental platform. The experimental results show that the predictive control method of permanent magnet synchronous motor model based on parameter identification and dead time compensation can effectively reduce the current error of the control system and accelerate the dynamic response of the speed. [ABSTRACT FROM AUTHOR]

In order to improve the robustness of the fractional order sliding mode controller (FSMC) for permanent magnet synchronous motor (PMSM) sensorless control, a fractional order sliding mode controller based on T-S fuzzy inference algorithm (FFSMC) is proposed to observe the rotor speed and position information. Based on the mathematical model of PMSM and sliding mode controller, a fractional order sliding mode controller is designed, and its stability is proved. The T-S fuzzy inference algorithm is used to tune the reaching law parameters of the FSMC, so that the reaching law parameters are no longer fixed values, but change with the state of the system. The correctness of the proposed method is verified by MATLAB simulation software. The effectiveness of the simulation results is verified by building a PMSM sensorless control experimental platform. The results show that the PMSM sensorless control based on FFSMC achieves parameter self-tuning and improves the observation accuracy. And the robustness of the control system is enhanced. [ABSTRACT FROM AUTHOR]

In order to improve the performance of decoupling control for an interior permanent magnet synchronous motor (IPMSM), a recursive least square algorithm with fuzzy forgetting factor is proposed to identify IPMSMparameters. Firstly, the problems of coupling and parameter identification of IPMSM are analyzed. Secondly, the identification process of resistance and flux linkage is analyzed, and the static parameters are identified as the initial value or constant value. Thirdly, fuzzy control is used to dynamically adjust the forgetting factor in the recursive least square algorithm to make the identification of direct axis and quadrature axis inductance parameters more accurate. Finally, the effectiveness and accuracy of the proposed parameter identification algorithm are verified on the platform, and the good performance of the proposed algorithm in decoupling control is verified. The experimental results show that the identification method can accurately identify the motor parameters in static state and dynamic state. At the same time, the forgetting factor is dynamically adjusted to improve the parameter identification effect and decoupling control performance of the motor. [ABSTRACT FROM AUTHOR]

The goal of vector control of interior permanent magnet synchronous motor (IPMSM) is to make IPMSM have excellent dynamic and steady-state performance, but there is coupling between the d-q axis in the synchronous rotating coordinate system, which affects the torque response performance. In view of the fact that the traditional voltage compensation strategy is sensitive to the change of motor parameters, genetic algorithm is introduced to identify the parameters, and a feedforward voltage compensation control based on genetic algorithm parameter identification is proposed. The compensation voltage is calculated by the inductance and flux value of the motor identified by genetic algorithm. Compensation voltage is used to counteract the change of feedback voltage caused by the change of motor parameters in feedforward decoupling control. Simulated and experimental results show that the proposed strategy can effectively achieve d-q axis current decoupling, improve the dynamic performance of the system, and have excellent robustness. [ABSTRACT FROM AUTHOR]