A linear permanent magnet motor model-free predictive current control method

Abstract

The application discloses a kind of linear permanent magnet motor model-free predictive current control methods, it is related to motor control technical field.The application includes the following steps: step 1, design linear permanent magnet motor under two-phase rotating coordinate system voltage equation, and then obtain the state space equation of linear permanent magnet motor;Step 2, after step 1, design linear permanent magnet motor drive system current loop controller based on model-free deadbeat predictive current control.The application, for the problem that electrical parameter variation and unmodeled dynamic disturbance can directly affect current dynamic performance, designed enhanced adaptive disturbance observer to estimate parameter uncertainty and other unmodeled disturbances in real time;Finally, enhanced adaptive disturbance observer is combined with model-free deadbeat predictive current control method, effectively solve the problem that conventional model-free deadbeat predictive current control method is strongly dependent on parameter accuracy, significantly improve the robust performance of linear permanent magnet motor system against parameter variation.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically to a model-free predictive current control method for a linear permanent magnet motor. Background Technology

[0002] Linear permanent magnet motors, due to their elimination of intermediate transmission mechanisms, possess advantages such as compact structure, high thrust density, and fast dynamic response, and are widely used in high-end equipment fields such as precision machining, rail transportation, and aerospace. As the core of the motor drive system, the current loop's control performance directly determines the entire system's dynamic response speed, steady-state accuracy, and anti-interference capability.

[0003] Deadbeat Predictive Current Control (DPCC) is a high-performance control strategy based on an accurate mathematical model of the controlled object. It achieves rapid and accurate current tracking by calculating the voltage vector that minimizes current error within a single sampling period. However, traditional DPCC heavily relies on the accuracy of the motor's mathematical model parameters. In actual operation, linear permanent magnet motors experience perturbations in motor parameters (such as stator resistance, inductance, and permanent magnet flux linkage) due to factors like temperature rise, magnetic saturation, and cogging effects. Simultaneously, inverter nonlinearities (such as dead-zone effects and transistor voltage drops) introduce unmodeled dynamics. These factors lead to model mismatch, causing a sharp decline in the performance of DPCC controllers designed based on nominal parameters. This manifests as increased current tracking error, increased harmonic content, and even system oscillation and instability.

[0004] To reduce dependence on model parameters, model-free control (MFC) theory has been introduced. In this theory, the ultra-local model (ULM) encompasses all uncertain dynamics of the system through a single, online-updable synthetic disturbance term, thus eliminating reliance on a precise mathematical model. However, the accuracy of the disturbance term estimation in the ultra-local model is crucial. Inaccurate estimations, especially when sensor accuracy is limited or sampling frequency is low, will still negatively impact control performance.

[0005] To address this, a model-free predictive current control method for linear permanent magnet motors is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a model-free predictive current control method for linear permanent magnet motors in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention specifically adopts the following technical solution: A model-free predictive current control method for a linear permanent magnet motor includes the following steps: Step 1, designing the voltage equation of the linear permanent magnet motor in a two-phase rotating coordinate system, thereby obtaining the state-space equation of the linear permanent magnet motor; Step 2, after Step 1, designing a current loop controller for the linear permanent magnet motor drive system based on model-free, deadbeat predictive current control; Step 3, after Step 2, firstly designing an enhanced adaptive disturbance observer to achieve real-time compensation of disturbance estimates; secondly, performing stability analysis of the enhanced adaptive disturbance observer; and finally combining the model-free, deadbeat predictive current control and the enhanced adaptive disturbance observer to achieve a robust linear permanent magnet motor drive system.

[0008] Furthermore, in step 1, the voltage equation of the linear permanent magnet motor in a two-phase rotating coordinate system can be expressed as equation (1): Equation (1); Where, and represent the d-axis and q-axis components of the stator voltage; and represent the d-axis and q-axis components of the stator current; and represent the d-axis and q-axis inductances; represents the stator resistance; represents the permanent magnet flux linkage; represents the pole pitch; and represents the mover velocity. The mechanical motion equation of a linear permanent magnet motor can be expressed as equation (2): Equation (2); in, Indicates electromagnetic thrust; Indicates load thrust; Indicates positioning force; Indicates the mass of the mover; Indicates the coefficient of viscous friction; The expression for electromagnetic thrust is given by equation (3): Equation (3).

[0009] Furthermore, when using the control mode of id=0, the electromagnetic thrust expression can be simplified to equation (4): Equation (4); where For thrust coefficient, .

[0010] Furthermore, the d-axis components of the stator current are selected separately. q-axis component of stator current and speed As state variables, the model of the linear permanent magnet motor in the two-phase rotating coordinate system is shown in equations (5)-(6) below: Equation (5); Equation (6).

[0011] Furthermore, step 2 specifically includes the following steps: Step 2.1: Establish the hyperlocal model as follows: Equation (7); where These are definite parameters. This represents the unknown part of the hyperlocal model, including the modeling part and the perturbation part; Step 2.2: Based on the hyperlocal model, design the dq-axis hyperlocal model of LPMM as follows: Equation (8); where fd and fq represent the disturbances of the known and unknown parts of the system, mainly including various uncertain disturbances such as back electromotive force and stator resistance voltage drop.

[0012] Step 2.3: According to equation (8), the state-space expression of the current hyperlocal model is obtained: Equation (9); where, , , , By discretizing equation (9) using Euler, we can obtain: Equation (10) Step 2.4: Based on deadbeat predictive current control, the dq-axis reference current is used instead. In vector control, the outer loop bandwidth is much smaller than the inner loop bandwidth, therefore the following relationship exists: Equation (11); Based on the above equation, the formula for calculating the dq-axis stator reference voltage can be obtained. Equation (12).

[0013] Furthermore, step 3 specifically includes the following steps: Step 3.1: Design an enhanced adaptive disturbance observer and establish the state-space equation of the linear permanent magnet motor considering uncertain disturbance terms, as shown in equation (13): Equation (13); where , , , , , In a surface-mount LPMM, Ld=Lq=Ls; the state estimation equation can be described as follows: , Equation (14); in These are estimates of the state variables. It is a disturbance estimate; define variables. and , representing the state estimation error and the disturbance estimation error respectively, can be expressed as follows using equations (13) and (14): Equation (15); According to the Lyapunov function, there exists and Then there is To obtain the adaptive mechanism, the Lyapunov function is constructed as follows: , Equation (16); in It is an adaptive gain. The derivative of the above equation is derived as follows: Equation (17); Assuming the observed perturbation bandwidth is much smaller than the observation period, the observer adaptive mechanism can be obtained from equation (17) to satisfy the stability condition. Therefore, the adaptive mechanism is constructed as follows: Equation (18); in The value at its maximum value and Between; define the following equation: , Equation (19); Then we can obtain the following equation: Equation (20); Due to the Lyapunov function It is bounded and convergent, and If it is bounded, then we can obtain the following formula: , Equation (21); also, If it is bounded, then Continuous, according to Barbalat's lemma, we have: Equation (22); when hour Since the rate of change of f is zero, therefore... The obtained disturbance estimate equals the actual value. Therefore, the enhanced adaptive disturbance observer can estimate the disturbance of the LPMM control system and then eliminate the current static error or system instability caused by model mismatch and parameter changes through feedforward compensation of the observation. Step 3.2: Analyze the stability of the enhanced adaptive disturbance observer; In the adaptive mechanism shown in equation (18), an appropriate adaptive gain can guarantee the stability of the proposed scheme. Subsequently, according to the Lyapunov function, the selection criteria for the adaptive gain in the digital control system are as follows; The Lyapunov function is selected as: Equation (23); where It is the estimation error of the state variables in the discrete domain; the convergence criterion of the Lyapunov function must satisfy the following equation: Equation (24); where It is the change of the Lyapunov function over one period. ,when and Inequality (24) holds. This can be given by the following inequality: Equation (25); Based on the state equation (13) and the state estimation equation (14), the adaptive mechanism yields... The change can be obtained using the following equation: Equation (26); where It is the control matrix. Equivalent sampling, It is the sampling period. By substituting (26) into (25), we can obtain the following equation: Equation (27); To satisfy the convergence criterion of the Lyapunov function (24), adaptive gain The options are as follows: Equation (28); where .

[0014] A model-free predictive current control system for a linear permanent magnet motor includes: a model-free, deadbeat-free predictive current controller for calculating a voltage reference value based on a current reference value and a feedback value; an enhanced adaptive disturbance observer for real-time estimation of system disturbances; a feedforward compensation module for compensating the disturbance estimate into the voltage reference value; and a space vector modulation module for generating inverter switching signals. Further, ...

[0015] Furthermore, the enhanced adaptive disturbance observer includes: a state observer for estimating motor state variables; a disturbance estimator for updating the disturbance estimate based on state error and an adaptive mechanism; and a stability judgment module for adjusting the adaptive gain online to ensure system stability.

[0016] A linear permanent magnet motor drive device includes a model-free predictive current control system for achieving high dynamic response and robust current control.

[0017] The beneficial effects of this invention are as follows: This invention presents a model-free, deadbeat-free predictive current control method for linear permanent magnet motors based on an enhanced adaptive disturbance observer. It designs a linear permanent magnet motor drive system based on model-free, deadbeat-free predictive current control, thereby achieving excellent current dynamic response performance of the linear permanent magnet motor drive system. Furthermore, addressing the issue that disturbances such as changes in electrical parameters and unmodeled dynamics directly affect current dynamic performance, an enhanced adaptive disturbance observer is designed to estimate unmodeled disturbances such as parameter uncertainties in real time. Finally, combining the enhanced adaptive disturbance observer with the model-free, deadbeat-free predictive current control method effectively solves the problem of strong dependence on parameter accuracy in conventional model-free, deadbeat-free predictive current control methods, significantly improving the robustness of the linear permanent magnet motor system against parameter changes. Attached Figure Description

[0018] Figure 1 This is the timing diagram of the current loop control in the model-free, deadbeat-free predictive current control of the linear permanent magnet motor of the present invention; Figure 2 This is a structural block diagram of the model-free, deadbeat-free predictive current control in the linear permanent magnet motor model of the present invention. Figure 3 This is a structural block diagram of the present invention; Figure 4 This is a structural block diagram of the model-free, deadbeat-free predictive current control of the linear permanent magnet motor of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In the description of the embodiments of the present invention, it should be noted that the terms "inner", "outer", "upper", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0023] like Figures 1 to 4As shown, a model-free predictive current control method for a linear permanent magnet motor includes the following steps: Step 1, designing the voltage equation of the linear permanent magnet motor in a two-phase rotating coordinate system, thereby obtaining the state-space equation of the linear permanent magnet motor; Step 2, after Step 1, designing a current loop controller for the linear permanent magnet motor drive system based on model-free, deadbeat predictive current control; Step 3, after Step 2, firstly designing an enhanced adaptive disturbance observer to achieve real-time compensation of disturbance estimates; secondly, performing stability analysis of the enhanced adaptive disturbance observer; finally, combining model-free, deadbeat predictive current control and the enhanced adaptive disturbance observer to achieve a robust linear permanent magnet motor drive system.

[0024] A model-free predictive current control system for a linear permanent magnet motor includes: a model-free, deadbeat-free predictive current controller for calculating a voltage reference value based on a current reference value and a feedback value; an enhanced adaptive disturbance observer for estimating system disturbances in real time; a feedforward compensation module for compensating the disturbance estimate into the voltage reference value; and a space vector modulation module for generating inverter switching signals.

[0025] Specifically, the enhanced adaptive disturbance observer includes: a state observer for estimating motor state variables; a disturbance estimator for updating disturbance estimates based on state errors and an adaptive mechanism; and a stability judgment module for adjusting the adaptive gain online to ensure system stability.

[0026] The model-free predictive current control system is used to achieve current control with high dynamic response and strong robustness.

[0027] Example 1: First, such as Figure 1 As shown, the current loop control timing diagram in the model-free, deadbeat-free predictive current control method for linear permanent magnet motors based on an enhanced adaptive disturbance observer is presented. The design is as follows: Figure 2 The linear permanent magnet motor current loop controller shown is based on model-free, deadbeat predictive current control, thereby improving the dynamic and steady-state performance of the linear permanent magnet motor current control. Then, to improve the disturbance rejection and robustness of the control system, an enhanced adaptive disturbance estimation technique was introduced based on model-free, deadbeat-free predictive current control. An enhanced adaptive disturbance observer was designed and its stability was verified, thereby achieving real-time estimation of parameter uncertainty disturbances, such as... Figure 3 The diagram shown is a block diagram of the enhanced adaptive disturbance observer. Secondly, the disturbance values ​​estimated by the enhanced adaptive disturbance observer are fed forward to compensate the model-free, deadbeat-free predictive current control, thereby improving the system's robustness against uncertain disturbances.

[0028] Example 2: This invention relates to model-free, deadbeat-free predictive current control of linear permanent magnet motors based on an enhanced adaptive disturbance observer, such as... Figure 4 As shown, please follow these steps: Step 1, in a two-phase rotating coordinate system, the voltage equation of the linear permanent magnet motor can be expressed as equation (1): Equation (1); Where, and represent the d-axis and q-axis components of the stator voltage; and represent the d-axis and q-axis components of the stator current; and represent the d-axis and q-axis inductances; represents the stator resistance; represents the permanent magnet flux linkage; represents the pole pitch; and represents the mover velocity. The mechanical motion equation of a linear permanent magnet motor can be expressed as equation (2): Equation (2); in, Indicates electromagnetic thrust; Indicates load thrust; Indicates positioning force; Indicates the mass of the mover; Indicates the coefficient of viscous friction; The expression for electromagnetic thrust is given by equation (3): Equation (3).

[0029] When the control mode of id=0 is adopted, the electromagnetic thrust expression can be simplified to equation (4): Equation (4); where For thrust coefficient, .

[0030] Select the d-axis components of the stator current respectively q-axis component of stator current and speed As state variables, the model of the linear permanent magnet motor in the two-phase rotating coordinate system is shown in equations (5)-(6) below: Equation (5); Equation (6).

[0031] Step 2: Following Step 1, design a current loop controller for the linear permanent magnet motor drive system based on model-free, deadbeat-free predictive current control, thereby achieving high dynamic response of the current loop in the linear permanent magnet motor drive system; specifically, the following steps are taken: Step 2.1: Establish the hyperlocal model as follows: Equation (7); in These are definite parameters. This represents the unknown part of the hyperlocal model, including the modeling part and the perturbation part; Step 2.2: Based on the hyperlocal model, design the dq-axis hyperlocal model of LPMM as follows: Equation (8); Here, fd and fq represent the disturbances in the known and unknown parts of the system, mainly including various uncertain disturbances such as back electromotive force and stator resistance voltage drop.

[0032] Step 2.3: According to equation (8), the state-space expression of the current hyperlocal model is obtained: Equation (9); in, , , , By discretizing equation (9) using Euler, we can obtain: Equation (10); Step 2.4: Based on deadbeat predictive current control, the dq-axis reference current is used instead. In vector control, the outer loop bandwidth is much smaller than the inner loop bandwidth, therefore the following relationship exists: , Equation (11); Based on the above formula, the formula for calculating the dq-axis stator reference voltage can be obtained: Equation (12).

[0033] The model-free deadbeat predictive current controller is shown in equation (12). In actual motor control systems, as the motor runs, parameters such as resistance, permanent magnet flux linkage, and right-angle shaft inductance change, ultimately causing errors in the reference voltage calculation, which in turn leads to a decrease in the performance of the model-free deadbeat predictive current control and even system instability. Therefore, compensation measures are needed to compensate for unmodeled disturbances and improve the robustness of the model-free deadbeat predictive current control. Step 3: In response to the influence of complex uncertainties such as parameter perturbations and model mismatch in the linear permanent magnet motor drive system, an enhanced adaptive disturbance observation technique is proposed to compensate the estimated uncertain disturbances to the feedforward channel, thereby significantly improving the robustness of the model-free deadbeat predictive current control against parameter uncertainty disturbances.

[0034] Step 3: Following Step 2, firstly, an enhanced adaptive disturbance observer is designed to achieve real-time compensation of the disturbance estimate; secondly, stability analysis of the enhanced adaptive disturbance observer is performed. Finally, model-free, deadbeat predictive current control and the enhanced adaptive disturbance observer are combined to realize a robust linear permanent magnet motor drive system. Step 3 specifically includes the following steps: Step 3.1: Design an enhanced adaptive disturbance observer and establish the state-space equation of the linear permanent magnet motor considering uncertain disturbance terms, as shown in equation (13): Equation (13); in , , , , , In a surface-mount LPMM, Ld=Lq=Ls; the state estimation equation can be described as follows: , Equation (14); in These are estimates of the state variables. It is a disturbance estimate; Define variables and , represent the state estimation error and the disturbance estimation error, respectively. The following expressions can be obtained from equations (13) and (14): Equation (15); According to the Lyapunov function, there exists and Then there is To obtain the adaptive mechanism, the Lyapunov function is constructed as follows: , Equation (16); in It is an adaptive gain. The derivative of the above equation is derived as follows: , Equation (17); Assuming the observed perturbation bandwidth is much smaller than the observation period, the observer adaptive mechanism can be obtained from equation (17) to satisfy the stability condition. Therefore, the adaptive mechanism is constructed as follows: Equation (18); in The value at its maximum value and Between; define the following equation: , Equation (19); Then we can obtain the following equation: Equation (20); Due to the Lyapunov function It is bounded and convergent, and If it is bounded, then we can obtain the following formula: , Equation (21); also, If it is bounded, then Continuous, according to Barbalat's lemma, we have: Equation (22); when hour Since the rate of change of f is zero, therefore... The obtained disturbance estimate equals the actual value. Therefore, the enhanced adaptive disturbance observer can estimate the disturbance of the LPMM control system and then eliminate the current static error or system instability caused by model mismatch and parameter changes through feedforward compensation of the observation. Step 3.2: Analyze the stability of the enhanced adaptive disturbance observer; In the adaptive mechanism shown in equation (18), an appropriate adaptive gain can guarantee the stability of the proposed scheme. Subsequently, according to the Lyapunov function, the selection criteria for the adaptive gain in the digital control system are as follows; The Lyapunov function was chosen as: Equation (23); in It is the estimation error of the state variables in the discrete domain; the convergence criterion of the Lyapunov function must satisfy the following equation: , Equation (24); in It is the change of the Lyapunov function over one period. ,when and Inequality (24) holds. This can be given by the following inequality: Equation (25); Based on the state equation (13) and the state estimation equation (14), the adaptive mechanism yields... The change can be obtained using the following equation: , Equation (26); in It is the control matrix. Equivalent sampling, It is the sampling period. By substituting (26) into (25), we can obtain the following equation: Equation (27); To satisfy the convergence criterion of the Lyapunov function (24), adaptive gain The options are as follows: Equation (28); in .

[0035] In summary, this invention presents a model-free, deadbeat-free predictive current control method for linear permanent magnet motors (LPMs) based on an enhanced adaptive disturbance observer. It designs a LPM drive system based on this model-free, deadbeat-free predictive current control, thereby achieving excellent current dynamic response performance. Furthermore, addressing the issue that disturbances such as changes in electrical parameters and unmodeled dynamics directly affect current dynamic performance, an enhanced adaptive disturbance observer is designed to estimate unmodeled disturbances such as parameter uncertainties in real time. Finally, combining the enhanced adaptive disturbance observer with the model-free, deadbeat-free predictive current control method effectively solves the problem of strong dependence on parameter accuracy in conventional model-free, deadbeat-free predictive current control methods, significantly improving the robustness of the LPM system against parameter changes. The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A model-free predictive current control method for a linear permanent magnet motor, characterized in that, Includes the following steps: Step 1: Design the voltage equation of the linear permanent magnet motor in a two-phase rotating coordinate system, and then obtain the state-space equation of the linear permanent magnet motor. Step 2: After step 1, design a current loop controller for a linear permanent magnet motor drive system based on model-free, deadbeat predictive current control. Step 3: After step 2, firstly, an enhanced adaptive disturbance observer is designed to achieve real-time compensation of the disturbance estimate; secondly, the stability analysis of the enhanced adaptive disturbance observer is performed; finally, the model-free, deadbeat predictive current control and the enhanced adaptive disturbance observer are combined to realize a robust linear permanent magnet motor drive system.

2. The model-free predictive current control method for a linear permanent magnet motor according to claim 1, characterized in that, In step 1, the voltage equation of the linear permanent magnet motor in a two-phase rotating coordinate system can be expressed as equation (1): Equation (1); Where, and represent the d-axis and q-axis components of the stator voltage; and represent the d-axis and q-axis components of the stator current; and represent the d-axis and q-axis inductances; represents the stator resistance; represents the permanent magnet flux linkage; represents the pole pitch; and represents the mover velocity. The mechanical motion equation of a linear permanent magnet motor can be expressed as equation (2): Equation (2); in, Indicates electromagnetic thrust; Indicates load thrust; Indicates positioning force; Indicates the mass of the mover; Indicates the coefficient of viscous friction; The expression for electromagnetic thrust is given by equation (3): Equation (3).

3. The model-free predictive current control method for a linear permanent magnet motor according to claim 2, characterized in that, When the control mode of id=0 is adopted, the electromagnetic thrust expression can be simplified to equation (4): Equation (4); where For thrust coefficient, .

4. The model-free predictive current control method for a linear permanent magnet motor according to claim 3, characterized in that, Select the d-axis components of the stator current respectively q-axis component of stator current and speed As state variables, the model of the linear permanent magnet motor in the two-phase rotating coordinate system is shown in equations (5)-(6) below: Equation (5); Equation (6).

5. The model-free predictive current control method for a linear permanent magnet motor according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Establish the hyperlocal model as follows: Equation (7); where These are definite parameters. This represents the unknown part of the hyperlocal model, including the modeling part and the perturbation part; Step 2.2: Based on the hyperlocal model, design the dq-axis hyperlocal model of LPMM as follows: Equation (8); where fd and fq represent the disturbances of the known and unknown parts of the system, mainly including various uncertain disturbances such as back electromotive force and stator resistance voltage drop; Step 2.3: According to equation (8), the state-space expression of the current hyperlocal model is obtained: Equation (9); where, , , , By discretizing equation (9) using Euler, we can obtain: Equation (10); Step 2.4: Based on deadbeat predictive current control, the dq-axis reference current is used instead. In vector control, the outer loop bandwidth is much smaller than the inner loop bandwidth, therefore the following relationship exists: Equation (11); Based on the above equation, the formula for calculating the dq-axis stator reference voltage can be obtained. Equation (12).

6. The model-free predictive current control method for a linear permanent magnet motor according to claim 1, characterized in that, Step 3 specifically includes the following steps: Step 3.1: Design an enhanced adaptive disturbance observer and establish the state-space equations of the linear permanent magnet motor considering uncertain disturbance terms, as shown in equation (13): Equation (13); where , , , , , In a surface-mount LPMM, Ld=Lq=Ls; the state estimation equation can be described as follows: , Equation (14); in These are estimates of the state variables. It is a disturbance estimate; Define variables and , represent the state estimation error and the disturbance estimation error, respectively. The following expressions can be obtained from equations (13) and (14): Equation (15); According to the Lyapunov function, there exists and Then there is To obtain the adaptive mechanism, the Lyapunov function is constructed as follows: , Equation (16); in It is an adaptive gain. The derivative of the above equation is derived as follows: Equation (17); Assuming the observed perturbation bandwidth is much smaller than the observation period, the observer adaptive mechanism can be obtained from equation (17) to satisfy the stability condition. Therefore, the adaptive mechanism is constructed as follows: Equation (18); in The value at its maximum value and between; Define the following equation: , Equation (19); Then we can obtain the following equation: Equation (20); Due to the Lyapunov function It is bounded and convergent, and If it is bounded, then we can obtain the following formula: , Equation (21); also, If it is bounded, then Continuous, according to Barbalat's lemma, we have: Equation (22); when hour Since the rate of change of f is zero, therefore... The obtained disturbance estimate equals the actual value. Therefore, the enhanced adaptive disturbance observer can estimate the disturbance of the LPMM control system and then eliminate the current static error or system instability caused by model mismatch and parameter changes through feedforward compensation of the observation. Step 3.2: Analyze the stability of the enhanced adaptive disturbance observer; In the adaptive mechanism shown in equation (18), an appropriate adaptive gain can guarantee the stability of the proposed scheme. Subsequently, according to the Lyapunov function, the selection criteria for the adaptive gain in the digital control system are as follows; The Lyapunov function is selected as: Equation (23); where It is the estimation error of the state variables in the discrete domain; the convergence criterion of the Lyapunov function must satisfy the following equation: Equation (24); where It is the change of the Lyapunov function over one period. ,when and Inequality (24) holds. This can be given by the following inequality: Equation (25); Based on the state equation (13) and the state estimation equation (14), the adaptive mechanism yields... The change can be obtained using the following equation: Equation (26); where It is the control matrix. Equivalent sampling, It is the sampling period. By substituting (26) into (25), we can obtain the following equation: Equation (27); To satisfy the convergence criterion of the Lyapunov function (24), adaptive gain The options are as follows: Equation (28); where .

7. A model-free predictive current control system for a linear permanent magnet motor, characterized in that, include: A model-free, deadbeat-free predictive current controller is used to calculate voltage reference values ​​based on current reference values ​​and feedback values. An enhanced adaptive disturbance observer for real-time estimation of system disturbances; The feedforward compensation module is used to compensate the disturbance estimate to the voltage reference value; The space vector modulation module is used to generate inverter switching signals.

8. The system according to claim 7, characterized in that, The enhanced adaptive disturbance observer includes: a state observer for estimating motor state variables; a disturbance estimator for updating the disturbance estimate based on state error and an adaptive mechanism; and a stability judgment module for adjusting the adaptive gain online to ensure system stability.

9. A linear permanent magnet motor drive device, characterized in that, The system includes a model-free predictive current control system as described in claim 8 or 9, for achieving high dynamic response and robust current control.

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