Marine permanent magnet synchronous motor single vector current control method based on model prediction, electronic equipment, storage medium and device
By adopting a model-predictive single-vector current control method, the computational complexity and stability issues of marine permanent magnet synchronous motor control algorithms are solved, achieving high-precision current control and fast response, which is applicable to marine propulsion systems.
Patent Information
- Application Number
- CN202511693161.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-06
AI Technical Summary
Traditional marine permanent magnet synchronous motor control algorithms are computationally complex, have insufficient dynamic response, and poor stability, making them difficult to meet the high dynamic and high reliability requirements of marine propulsion systems.
A model-based single-vector current control method is adopted. By constructing a current prediction equation through a discretized mathematical model in a synchronously rotating coordinate system, the optimal voltage vector is selected to reduce computational complexity, achieve close tracking of the reference value of the current, and correct the model parameters in real time through a closed-loop feedback mechanism.
It achieves high-precision current control, reduces the amount of calculation, improves the real-time response speed and anti-disturbance stability of the system, adapts to the high dynamic conditions of ships, and has a simple control structure that is easy to apply in engineering.
Smart Images
Figure CN121485545A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of marine electric propulsion technology, and more specifically, relates to a model-based prediction-based single-vector current control method for marine permanent magnet synchronous motors, electronic equipment, storage medium, and device. Background Technology
[0002] Permanent magnet synchronous motors (PMSMs) are widely used in marine electric propulsion systems due to their high efficiency, high power density, and excellent speed regulation performance.
[0003] Traditional vector control (FOC) methods achieve closed-loop current control through PI regulators, but when ship loads change frequently or external disturbances occur, current fluctuations and torque pulsations are easily generated, affecting system stability.
[0004] Model Predictive Control (MPC) uses a mathematical model of the motor to predict the current change trend in the next sampling cycle and selects the optimal voltage vector through a cost function to achieve a faster dynamic response. However, traditional multi-vector MPC algorithms need to traverse multiple voltage vectors in each control cycle, resulting in large computational loads and poor real-time performance, making them unsuitable for high-dynamic, high-reliability applications such as ship propulsion systems.
[0005] Therefore, how to reduce the computational complexity of the control algorithm for marine permanent magnet synchronous motors, improve the dynamic response speed and anti-disturbance stability while ensuring control accuracy has become an urgent technical problem to be solved in the field of marine electric propulsion systems. Summary of the Invention
[0006] The purpose of this invention is to provide a model-based prediction-based single-vector current control method, electronic device, storage medium, and apparatus for marine permanent magnet synchronous motors, thereby overcoming the shortcomings of existing marine permanent magnet synchronous motor control algorithms, such as complex calculations, insufficient dynamic response, and poor stability.
[0007] To achieve the above objectives, in a first aspect, the present invention provides a model-predictive-based single-vector current control method for marine permanent magnet synchronous motors, comprising the following steps: S1: Collect the three-phase current and speed signals of the marine permanent magnet synchronous motor, perform coordinate transformation on the three-phase current signals, and obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. S2: Based on the d-axis current component, the q-axis current component, and the motor parameters, construct the motor voltage equation, and discretize the voltage equation to obtain the current prediction equation; S3: Construct a cost function with the goal of minimizing the current prediction error, and calculate the current prediction error corresponding to different voltage vectors based on the cost function. Select the voltage vector that minimizes the current prediction error as the optimal voltage vector. S4: Convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output the corresponding voltage through the PWM modulation signal; S5: Sample the three-phase current output by the inverter in real time, feed the three-phase current back to step S2, and update the current prediction equation parameters for the next sampling period.
[0008] Optionally, in step S2, the motor voltage equation is: , in, The current component is the d-axis component. For the q-axis current component, For d-axis voltage, This is the q-axis voltage. For stator resistance, For d-axis inductance, It is the q-axis inductance. It is the electric angular frequency. It is a permanent magnet flux linkage. The d-axis back electromotive force is given. The back electromotive force is the q-axis. These are inherent parameters of the motor; the electrical angular frequency Obtained dynamically from the aforementioned rotational speed signal. The current prediction equation is as follows: , in, for Predicted current at time of day for The actual current at any given moment, The sampling period is For stator inductance, For voltage vectors, It is a current vector. It is the back electromotive force; Depend on and Obtained by vector synthesis, Depend on and Obtained by vector synthesis, The stator inductance is obtained by vector synthesis of the d-axis back electromotive force and the q-axis back electromotive force. These are inherent parameters of the motor.
[0009] Optionally, in step S3, the cost function is: , in, For current prediction error, This is the reference value for the current.
[0010] In a second aspect, the present invention provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed, enable the at least one processor to perform the model prediction-based single-vector current control method for marine permanent magnet synchronous motors as described in the first aspect.
[0011] Thirdly, the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the model prediction-based single-vector current control method for marine permanent magnet synchronous motors described in the first aspect.
[0012] Fourthly, the present invention provides a model-predictive single-vector current control device for marine permanent magnet synchronous motors, comprising: The signal sampling module is configured to collect the three-phase current and speed signals of the marine permanent magnet synchronous motor, and perform coordinate transformation on the three-phase current signals to obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. The current prediction module is configured to predict the current based on the d-axis current component. The q-axis current component The motor voltage equation is constructed based on the motor parameters, and the voltage equation is discretized to obtain the current prediction equation. The optimal voltage vector selection module is configured to construct a cost function with the goal of minimizing the current prediction error, calculate the current prediction error corresponding to different voltage vectors based on the cost function, and select the voltage vector that minimizes the current prediction error as the optimal voltage vector. The PWM modulation module is configured to convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output a corresponding voltage through the PWM modulation signal. The feedback module is configured to sample the three-phase current output by the inverter in real time, feed the three-phase current back to the current prediction module, and update the parameters of the current prediction equation for the next sampling period.
[0013] The beneficial effects of this invention are as follows: It provides a model-based prediction-based single-vector current control method for marine permanent magnet synchronous motors. Through a discretized mathematical model in a synchronous rotating coordinate system, it achieves accurate single-step current prediction, ensuring that the output current closely tracks the reference value, resulting in small current prediction errors and excellent control accuracy. Simultaneously, the single-vector optimization strategy eliminates the need to traverse all voltage vectors, reducing computational complexity and control cycle, making it suitable for the high-dynamic operating conditions of ships. Furthermore, the closed-loop feedback mechanism can correct the prediction model in real time, resulting in small current and torque fluctuations under load disturbances and parameter deviations, significantly improving system robustness. Moreover, the control structure is simple, requiring no special hardware adaptation, and can be directly applied to various marine permanent magnet synchronous motors. It is also easy to implement in engineering and has strong scalability.
[0014] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description
[0015] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.
[0016] Figure 1 A flowchart of a model prediction-based single-vector current control method for marine permanent magnet synchronous motors according to Embodiment 1 of the present invention is shown.
[0017] Figure 2 The schematic diagram of the single-vector current control method for marine permanent magnet synchronous motor based on model prediction according to Embodiment 1 of the present invention is shown.
[0018] Figure 3 The electromagnetic torque and load torque waveforms of Embodiment 1 of the present invention are shown.
[0019] Figure 4 The diagram shows the waveforms of the given speed and actual speed of the motor in Embodiment 1 of the present invention. Detailed Implementation
[0020] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
[0021] Example 1
[0022] See Figure 1 and Figure 2This embodiment provides a model-predictive-based single-vector current control method for marine permanent magnet synchronous motors, including the following steps: S1. Signal Acquisition and Coordinate Transformation: Acquire the three-phase current and speed signals of the marine permanent magnet synchronous motor, perform coordinate transformation on the three-phase current signals, and obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. S2. Establish the mathematical model of the motor: Based on the d-axis current component, q-axis current component and motor parameters, construct the motor voltage equation, and discretize the voltage equation to obtain the current prediction equation; In this step, the motor voltage equation is: , in, The current component is the d-axis component. For the q-axis current component, For d-axis voltage, This is the q-axis voltage. For stator resistance, For d-axis inductance, It is the q-axis inductance. It is the electric angular frequency. It is a permanent magnet flux linkage. The d-axis back electromotive force is given. The back electromotive force is the q-axis. These are inherent parameters of the motor; electrical angular frequency. Obtained dynamically from the speed signal. The current prediction equation is: , in, for Predicted current at time of day for The actual current at any given moment, The sampling period is For stator inductance, For voltage vectors, It is a current vector. It is the back electromotive force; Depend on and Obtained by vector synthesis, Depend on and Obtained by vector synthesis, The stator inductance is obtained by vector synthesis of the d-axis back electromotive force and the q-axis back electromotive force. These are inherent parameters of the motor.
[0023] S3. Cost function calculation and single vector selection: Construct a cost function with the goal of minimizing the current prediction error, and calculate the current prediction error corresponding to different voltage vectors based on the cost function. Select the voltage vector that minimizes the current prediction error as the optimal voltage vector. In this step, the cost function is: , in, For current prediction error, This is the reference value for the current.
[0024] Specifically, by constructing a cost function that minimizes the current prediction error, computational efficiency, optimization accuracy, and robustness are simultaneously improved. This cost function is only the absolute error between the reference current and the predicted current, eliminating the need for complex matrix operations or weight coefficient adjustments, thus reducing computation time and further ensuring a shorter control cycle. By focusing solely on minimizing the current prediction error, it avoids optimal vector selection deviations caused by conflicts in weights from multiple objectives. Especially during sudden changes in ship load, it can more accurately select the appropriate voltage vector, reducing current overshoot. Simultaneously, the absolute error form has a certain suppressive effect on current noise such as sensor interference under ship operating conditions, avoiding misjudgments of the cost function due to noise, ensuring the stability of optimal voltage vector selection, and indirectly improving the smoothness of motor torque output.
[0025] S4, PWM Modulation and Output Execution: Convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output the corresponding voltage through the PWM modulation signal; S5. Feedback Control and Update: Sample the three-phase current output by the inverter in real time, feed the three-phase current back to step S2, and update the current prediction equation parameters for the next sampling cycle.
[0026] This embodiment addresses the problems of current fluctuations and torque pulsations easily generated by traditional vector control (FOC) under frequent changes in ship load, and the large computational burden and poor real-time performance of traditional multi-vector model predictive control (MPC). It achieves a triple improvement in real-time performance, stability, and engineering applicability through a core control logic of single-vector optimization and closed-loop feedback. This method achieves unified current prediction and control by discretizing the motor mathematical model, selecting only one optimal voltage vector in each sampling period, eliminating the need to traverse multiple vectors, significantly reducing computational complexity, and adapting to the high dynamic requirements of ship propulsion systems, such as rapid response under sudden load torque changes. Simultaneously, through a closed-loop design of acquisition, prediction, and feedback, the parameters of the current prediction equation are updated in real time, effectively suppressing current and torque fluctuations caused by external disturbances such as wave impacts, ensuring stable operation of the propulsion system. Under sudden load changes and dynamic operating conditions, current and torque fluctuations are significantly reduced, and system robustness is improved.
[0027] The following simulation model of a marine permanent magnet synchronous motor drive system is used to verify this method. The simulation results are as follows: Figure 3 and Figure 4 As shown, when the load torque abruptly changes from 5 N·m to 20 N·m, the electromagnetic torque quickly tracks the reference value, and the fluctuation amplitude is significantly reduced; the motor speed quickly recovers to steady state after the disturbance occurs, with a small steady-state error; under speed variation and load disturbance conditions, the current prediction error is significantly reduced, and the system stability is good. Simulation results show that the method of the present invention achieves fast response and high-precision control without increasing hardware complexity, meeting the real-time control requirements of ship propulsion systems.
[0028] Example 2
[0029] This embodiment also provides an electronic device, which includes: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, The memory stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the model prediction-based single-vector current control method for marine permanent magnet synchronous motors in Embodiment 1.
[0030] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0031] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.
[0032] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.
[0033] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.
[0034] Example 3
[0035] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the model prediction-based single-vector current control method for marine permanent magnet synchronous motors in Embodiment 1.
[0036] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.
[0037] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
[0038] Example 4
[0039] Referring to the figure, this disclosure provides a password generation apparatus, including: The signal sampling module is configured to acquire the three-phase current and speed signals of the marine permanent magnet synchronous motor, and perform coordinate transformation on the three-phase current signals to obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. The current prediction module is configured to predict based on the d-axis current component. q-axis current component The motor voltage equation is constructed based on the motor parameters, and the voltage equation is discretized to obtain the current prediction equation. The optimal voltage vector selection module is configured to construct a cost function with the goal of minimizing the current prediction error, calculate the current prediction error corresponding to different voltage vectors based on the cost function, and select the voltage vector that minimizes the current prediction error as the optimal voltage vector. The PWM modulation module is configured to convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output the corresponding voltage through the PWM modulation signal. The feedback module is configured to sample the three-phase current output by the inverter in real time, feed the three-phase current back to the current prediction module, and update the parameters of the current prediction equation for the next sampling period.
[0040] Optionally, in the current prediction module, the motor voltage equation is: , in, The current component is the d-axis component. For the q-axis current component, For d-axis voltage, This is the q-axis voltage. For stator resistance, For d-axis inductance, It is the q-axis inductance. It is the electric angular frequency. It is a permanent magnet flux linkage. The d-axis back electromotive force is given. The back electromotive force is the q-axis. These are inherent parameters of the motor; electrical angular frequency. Obtained dynamically from the speed signal. The current prediction equation is: , in, for Predicted current at time of day for The actual current at any given moment, The sampling period is For stator inductance, For voltage vectors, It is a current vector. It is the back electromotive force; Depend on and Obtained by vector synthesis, Depend on and Obtained by vector synthesis, The stator inductance is obtained by vector synthesis of the d-axis back electromotive force and the q-axis back electromotive force. These are inherent parameters of the motor.
[0041] Optionally, in the optimal voltage vector selection module, the cost function is: , in, For current prediction error, This is the reference value for the current.
[0042] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A single-vector current control method for marine permanent magnet synchronous motors based on model prediction, characterized in that, Includes the following steps: S1: Collect the three-phase current and speed signals of the marine permanent magnet synchronous motor, perform coordinate transformation on the three-phase current signals, and obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. S2: Based on the d-axis current component, the q-axis current component, and the motor parameters, construct the motor voltage equation, and discretize the voltage equation to obtain the current prediction equation; S3: Construct a cost function with the goal of minimizing the current prediction error, and calculate the current prediction error corresponding to different voltage vectors based on the cost function. Select the voltage vector that minimizes the current prediction error as the optimal voltage vector. S4: Convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output the corresponding voltage through the PWM modulation signal; S5: Sample the three-phase current output by the inverter in real time, feed the three-phase current back to step S2, and update the current prediction equation parameters for the next sampling period.
2. The single-vector current control method for marine permanent magnet synchronous motors based on model prediction according to claim 1, characterized in that, In step S2, the motor voltage equation is: ， in, The current component is the d-axis component. For the q-axis current component, For d-axis voltage, This is the q-axis voltage. For stator resistance, For d-axis inductance, It is the q-axis inductance. It is the electric angular frequency. It is a permanent magnet flux linkage. The d-axis back electromotive force is given. The back electromotive force is the q-axis. These are inherent parameters of the motor; the electrical angular frequency Obtained dynamically from the aforementioned rotational speed signal. The current prediction equation is as follows: ， in, for Predicted current at time of day for The actual current at any given moment, The sampling period is For stator inductance, For voltage vectors, It is a current vector. It is the back electromotive force; Depend on and Obtained by vector synthesis, Depend on and Obtained by vector synthesis, The stator inductance is obtained by vector synthesis of the d-axis back electromotive force and the q-axis back electromotive force. These are inherent parameters of the motor.
3. The single-vector current control method for marine permanent magnet synchronous motors based on model prediction according to claim 2, characterized in that, In step S3, the cost function is: ， in, For current prediction error, This is the reference value for the current.
4. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the model prediction-based single-vector current control method for marine permanent magnet synchronous motors as described in any one of claims 1-3.
5. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the model prediction-based single-vector current control method for marine permanent magnet synchronous motors as described in any one of claims 1-3.
6. A model-predictive-based single-vector current control device for marine permanent magnet synchronous motors, characterized in that, include: The signal sampling module is configured to collect the three-phase current and speed signals of the marine permanent magnet synchronous motor, and perform coordinate transformation on the three-phase current signals to obtain the d-axis current component and q-axis current component in the synchronous rotating coordinate system. The current prediction module is configured to predict the current based on the d-axis current component. The q-axis current component The motor voltage equation is constructed based on the motor parameters, and the voltage equation is discretized to obtain the current prediction equation. The optimal voltage vector selection module is configured to construct a cost function with the goal of minimizing the current prediction error, calculate the current prediction error corresponding to different voltage vectors based on the cost function, and select the voltage vector that minimizes the current prediction error as the optimal voltage vector. The PWM modulation module is configured to convert the optimal voltage vector into a PWM modulation signal, and control the inverter to output a corresponding voltage through the PWM modulation signal. The feedback module is configured to sample the three-phase current output by the inverter in real time, feed the three-phase current back to the current prediction module, and update the parameters of the current prediction equation for the next sampling period.
7. The model-predictive single-vector current control device for marine permanent magnet synchronous motors according to claim 6, characterized in that, In the current prediction module, the motor voltage equation is: ， in, The current component is the d-axis component. For the q-axis current component, For d-axis voltage, This is the q-axis voltage. For stator resistance, For d-axis inductance, It is the q-axis inductance. It is the electric angular frequency. It is a permanent magnet flux linkage. The d-axis back electromotive force is given. The back electromotive force is the q-axis. These are inherent parameters of the motor; the electrical angular frequency Obtained dynamically from the aforementioned rotational speed signal. The current prediction equation is as follows: ， in, for Predicted current at time of day for The actual current at any given moment, The sampling period is For stator inductance, For voltage vectors, It is a current vector. It is the back electromotive force; Depend on and Obtained by vector synthesis, Depend on and Obtained by vector synthesis, The stator inductance is obtained by vector synthesis of the d-axis back electromotive force and the q-axis back electromotive force. These are inherent parameters of the motor.
8. The model-predictive single-vector current control device for marine permanent magnet synchronous motors according to claim 7, characterized in that, In the optimal voltage vector selection module, the cost function is: ， in, For current prediction error, This is the reference value for the current.