A dead-beat predictive control method

Abstract

The application relates to the technical field of motor control, and specifically provides a deadbeat predictive control method, steps of which comprise: establishing a voltage equation of a permanent magnet synchronous motor; establishing a deadbeat predictive control model based on the voltage equation of the permanent magnet synchronous motor and current loop disturbance; establishing a generalized adaptive super-helix observer according to adaptive theory and super-helix technology; obtaining a current loop controller through the deadbeat predictive control model and the generalized adaptive super-helix observer; establishing a quasi-resonant controller by using a conditional switching resonant mode; making the quasi-resonant controller and the deadbeat predictive control model jointly act on the current loop controller to obtain a final current loop controller; and combining the generalized adaptive super-helix observer and the improved quasi-resonant controller to provide satisfactory disturbance estimation accuracy and convergence speed, and effectively suppress periodic disturbance, so that the dynamic performance and the steady-state performance of the permanent magnet synchronous motor system are improved.

Description

Technical Field

[0001] This invention belongs to the field of motor control technology, specifically providing a deadbeat predictive control method based on a combination of a generalized adaptive superspiral observer and improved quasi-resonant control. Background Technology

[0002] In the field of electric drives, the demand for high-precision, high-dynamic current control of permanent magnet synchronous motors (PMSMs) is increasing, especially in high-performance servo systems such as CNC machine tools and robot servo control. However, various disturbances exist in actual PMSM systems. Non-periodic disturbances such as cross-coupling disturbances in the current loop, parameter perturbations, and external disturbances reduce system stability, while periodic disturbances such as flux harmonics and voltage harmonics reduce the system's control accuracy. Currently, widely used control strategies for non-periodic disturbances in PMSMs include active disturbance rejection control, sliding mode control, and predictive control; for periodic disturbances, resonant controllers, repetitive controllers, and iterative learning methods are often used.

[0003] Among these methods, deadbeat predictive control has emerged as a potential solution for high-performance current control due to its high dynamic response and compatibility with digital control systems. However, the performance of this algorithm largely depends on the accuracy of the parameters used in the predictive model. Factors such as temperature variations, magnetic saturation, cross-coupling, and unmodeled disturbances can lead to unavoidable disturbances and parameter mismatches during operation, resulting in a sharp decline in the algorithm's control performance.

[0004] Therefore, to mitigate the performance degradation caused by parameter mismatch and to compensate for disturbances such as cross-coupling disturbances and external disturbances in the current loop, a high-precision, high-dynamic-characteristic observer is required. For linear extended state observers, this necessitates a high-gain form. However, high-gain extended state observers are highly sensitive to measurement noise, which is amplified and then applied to the system, leading to system vibration or even instability. Thus, fast and accurate disturbance estimation and measurement noise suppression are inherently contradictory for extended state observers.

[0005] For periodic disturbances, quasi-resonant controllers not only solve the performance degradation caused by resonant frequency shifts, but also offer simplicity in implementation and ease of parameter tuning. However, during motor dynamics, the resonant term may produce side effects. In steady state, the resonant frequency coincides with the disturbance frequency, effectively suppressing the disturbance; however, when the motor is under dynamic conditions, this suppression capability is lost due to the significant deviation between the motor speed and the target value. Worse still, the control input signal during motor dynamics contains a large amount of AC components, which causes the output of the resonant term to be amplified sharply, thereby weakening the overall control performance.

[0006] Therefore, in order to solve the above problems, this application proposes a beat-free predictive control method. Summary of the Invention

[0007] This invention provides a deadbeat predictive control method to address the non-periodic and periodic disturbances present in the current loop of permanent magnet synchronous motors, thereby achieving high-performance current control for permanent magnet synchronous motors.

[0008] To achieve the above objectives, the technical solution created by this invention is implemented as follows: A method for predictive control without delay includes: Establish the voltage equation for the permanent magnet synchronous motor; Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, a deadbeat predictive control model is established. Based on adaptive theory and superhelical technology, a generalized adaptive superhelical observer is established. The current loop controller is obtained by using the deadbeat predictive control model and the generalized adaptive superspiral observer; A quasi-resonant controller is established using a condition-switching resonant mode. The quasi-resonant controller and the deadbeat-free predictive control model are then applied together to the current loop controller to obtain the final current loop controller.

[0009] Furthermore, the expressions for the aperiodic disturbance in the current loop disturbance are as follows: , , in, and They are respectively shaft and Aperiodic disturbances on the axis; and They are respectively shaft and Cross-coupling disturbances of the axes; and They are respectively shaft and Known disturbances to the axis; and They are respectively shaft and Disturbances caused by parameter perturbations on the axis; and They are respectively shaft and External uncertainties of the axis; and They are respectively shaft and Stator current of the shaft; for shaft and The nominal value of the shaft stator inductance; This is the nominal value of the stator resistance. This is the nominal value of the rotor flux; , , ; The expressions for the periodic disturbance in the current loop disturbance are as follows: , , in, and They are respectively shaft and magnetic flux harmonics of the shaft, and They are respectively shaft and The amplitude of the 6nth magnetic flux harmonic of the axis; Electric angular velocity; and They are respectively shaft and Voltage harmonics of the shaft; and These are the dead time and sampling period, respectively. This is the DC bus voltage; Permanent magnet synchronous motor The periodic disturbance of the axis is represented as: , in, and They are respectively shaft and Periodic disturbances of the axis; and They are respectively shaft and Stator inductance of the shaft; The first-order hyperlocal mathematical model is represented as: , in, and These are the model's output and input variables, respectively. It is a set of known and unknown disturbances; It is a scaling factor; The first-order hyperlocal mathematical model of the current loop is finally expressed as: , in, and These represent the total disturbances along the d-axis and q-axis, respectively. To control the gain.

[0010] Furthermore, the step of establishing a deadbeat-free predictive control model based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor specifically includes the following steps: The voltage equation of the permanent magnet synchronous motor is expressed as: , , in, and They are respectively shaft and Axis in Reference voltage at any given time; and They are respectively shaft and Axis in Reference current value at any given time; and They are respectively shaft and Axis in Stator current at time t; Stator inductance; The sampling period; Stator resistance; for electric angular velocity at time t, For rotor flux; Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, the deadbeat predictive control model of the permanent magnet synchronous motor based on the hyperlocal model is obtained, which is expressed as: , in, and These represent the total disturbances along the d-axis and q-axis, respectively. To control the gain.

[0011] Furthermore, the step of establishing a generalized adaptive superhelical observer based on adaptive theory and superhelical technology specifically includes the following steps: The extended state observation model for the dq-axis current equations of a permanent magnet synchronous motor is established as follows: , in, , , ; ; , To control the gain; ; ; ; for The first derivative of the total perturbation of the axis; Based on the extended state observation model, and using adaptive theory and superhelical technology, the generalized adaptive superhelical observer is as follows: , The error correction term is designed as follows: , in, This is an estimate of the state vector; For a specific moment; The sampling period; To control profits; for Stator voltage at time 1; ; and This is the gain value of the observer; It is a variable exponent term, and ; For adaptive gain term; and This is the error correction term.

[0012] Furthermore, in the step of obtaining the current loop controller through the deadbeat predictive control model and the generalized adaptive superspiral observer, the control law of the current loop controller is: .

[0013] Furthermore, the step of establishing a quasi-resonant controller using a condition-switching resonant mode, and then applying the quasi-resonant controller and the deadbeat-free predictive control model together to the current loop controller to obtain the final current loop controller specifically includes the following steps: The transfer function of the quasi-resonant controller is: , in, In order to be in The step function at the switching point; The switching point is adjustable; For current tracking error, For reference current, Stator current; This is the resonant gain; The damping frequency; For Laplace variables; The resonant frequency; Discretizing the quasi-resonant controller yields the discretized transfer function as follows: , in, For the complex frequency domain of a discrete-time signal; .

[0014] Furthermore, the discretized transfer function is added to the current loop controller, and combined with the deadbeat predictive control model, the final current loop controller is expressed as follows: .

[0015] Furthermore, the generalized adaptive superspiral observer is used to estimate and compensate for the aperiodic disturbance in the current loop disturbance; The quasi-resonant controller and the deadbeat predictive control model are implemented in parallel to estimate and compensate for periodic disturbances in the current loop.

[0016] Compared with the prior art, the present invention can achieve the following beneficial effects: (1) This application proposes a deadbeat predictive control method to address the aperiodic and periodic disturbance problems in the current loop of permanent magnet synchronous motors. By combining a generalized adaptive superspiral observer and an improved quasi-resonant controller, the method achieves fast and accurate estimation and compensation of disturbances in the current loop. Compared with the traditional deadbeat predictive control method, this method has significantly improved the disturbance estimation accuracy and convergence speed, thereby effectively improving the dynamic performance and steady-state performance of the permanent magnet synchronous motor system. (2) A generalized adaptive superspiral observer is adopted. Based on adaptive theory and superspiral technology, a new nonlinear function is constructed to replace the linear term in the traditional extended state observer. The observer is further able to estimate and compensate for the non-periodic disturbance in the current loop more quickly and accurately in a finite time by using adaptive gain to dynamically feedback the error intensity. (3) By designing an improved quasi-resonant controller and a deadbeat controller in parallel, the periodic disturbances existing in the current loop are effectively estimated and compensated, and the side effects of the traditional quasi-resonant controller in the dynamic process are avoided, thus improving the dynamic and steady-state performance of the permanent magnet synchronous motor system. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating a deadbeat predictive control method according to an embodiment of the present invention; Figure 2 A control structure diagram of a deadbeat predictive control method described in an embodiment of the present invention; Figure 3 A model structure diagram of the permanent magnet synchronous motor servo control system described in the embodiment of the present invention; Figure 4 A schematic diagram showing the experimental results comparing the d-axis and a-phase current curves; Figure 5 This is a schematic diagram showing the experimental results of comparing the q-axis current curves. Figure 6 A schematic diagram showing the experimental results of Fourier analysis of q-axis current; Figure 7 Inductor When mismatch shaft and Schematic diagram of phase current comparison experiment results; Figure 8 Inductor When mismatch Results of shaft current comparison experiment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0020] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] Please see Figure 1This is a flowchart illustrating a deadbeat predictive control method described in this embodiment. Addressing the aperiodic and periodic disturbances in the current loop of a permanent magnet synchronous motor (PMSM), a high-performance current control for the PMSM is achieved by combining a generalized adaptive superspiral observer and an improved quasi-resonant controller. Specific steps include: S1: Establish the voltage equation for the permanent magnet synchronous motor.

[0022] In this embodiment, hysteresis losses, eddy currents, and core saturation of the surface-mounted permanent magnet synchronous motor are ignored. The permanent magnet synchronous motor in the synchronous rotating coordinate system... The axis voltage equation is: , (1) in, and They are respectively shaft and Stator voltage of the shaft; and They are respectively shaft and Stator current of the shaft; Stator resistance; and They are respectively shaft and Stator inductance of the shaft; Electric angular velocity; This is the rotor flux.

[0023] For surface-mounted permanent magnet synchronous motors, it is represented as: (2) The The axis voltage equation is discretized using a first-order forward difference, resulting in the discrete voltage equation: , (3) To ensure that the actual current accurately tracks the reference current in the next cycle under the influence of the predicted control voltage, and Replace with reference current and ,get The predicted voltage equation at time t is: , (4) in, and They are respectively shaft and Axis in Stator voltage at time 1; The sampling period; for time Reference current value for the shaft; for time Reference current of the shaft; for time Stator current of the shaft; for time Stator current of the shaft; for The electric angular velocity at time t.

[0024] Due to the delay effect of electronic components, in Control voltage calculated at all times until It can only be applied at a specific moment. Therefore, a one-step delay compensation is needed for the voltage prediction equation, modeling it as: (5) in, express Control voltage at all times express The reference voltage at that moment.

[0025] Considering one-step delay compensation, (4) can be rewritten as: , (6) Because the sampling period is very short, , , Therefore, the traditional deadbeat voltage control equation considering one-step delay compensation is: , (7) in, and They are respectively shaft and Axis in Reference voltage at any given time; and They are respectively shaft and Axis in Reference current value at any given time; and They are respectively shaft and Axis in Stator current at time t; Stator inductance; The sampling period; Stator resistance; for electric angular velocity at time t, This is the rotor flux.

[0026] S2: Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, establish a deadbeat predictive control model.

[0027] In this embodiment, the aperiodic disturbances in the current loop of the permanent magnet synchronous motor mainly include dq-axis cross-coupling disturbances, parameter perturbations, and external uncertain disturbances; expressed as: (8) (9) in, and They are respectively shaft and Aperiodic disturbances on the axis; and They are respectively shaft and Cross-coupling disturbances of the axes; and They are respectively shaft and Known disturbances to the axis; and They are respectively shaft and Disturbances caused by parameter perturbations on the axis; and They are respectively shaft and External uncertainties of the axis; and They are respectively shaft and Stator current of the shaft; for shaft and The nominal value of the shaft stator inductance; This is the nominal value of the stator resistance. This is the nominal value of the rotor flux; , , .

[0028] The periodic disturbances in the current loop of a permanent magnet synchronous motor mainly include flux harmonics caused by the motor manufacturing process and voltage harmonics caused by inverter nonlinearity. The specific analysis process for flux harmonics and voltage harmonics is as follows: Ideally, the magnetic flux of a permanent magnet synchronous motor (PMSM) is sinusoidally distributed. However, in practical PMSM systems, due to manufacturing limitations and magnetic saturation, it is difficult to achieve an ideal sinusoidal magnetic flux distribution, resulting in harmonics in the actual magnetic flux. (Permanent magnet synchronous motor) The magnetic flux harmonics of the shaft are represented as: (10) in, and They are respectively shaft and magnetic flux harmonics of the shaft, and They are respectively shaft and The amplitude of the 6nth magnetic flux harmonic of the axis; It represents the electric angular velocity.

[0029] In IGBT voltage source inverters, a certain dead time needs to be set to prevent the inverter from short-circuiting due to the simultaneous closing of the upper and lower switches on the same side. However, the dead time will cause voltage harmonics. The voltage harmonics of the dq axis of a permanent magnet synchronous motor are expressed as... (11) in, and They are respectively shaft and Voltage harmonics of the shaft; and These are the dead time and sampling period, respectively. This is the DC bus voltage.

[0030] Combining magnetic flux harmonics and voltage harmonics, the permanent magnet synchronous motor The periodic disturbance of the axis is represented as: (12) in, and They are respectively shaft and Periodic disturbances of the axis; and They are respectively shaft and Stator inductance of the shaft; Furthermore, the first-order hyperlocal mathematical model is expressed as: (13) in, and These are the model's output and input variables, respectively. It is a set of known and unknown disturbances; It is a scaling factor; According to equation (12), the final mathematical model of the first-order hyperlocality in the current loop is: (14) in, and These represent the total disturbances along the d-axis and q-axis, respectively. To control the gain.

[0031] Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, the deadbeat predictive control model of the permanent magnet synchronous motor based on the hyperlocal model is obtained, which is expressed as: (15).

[0032] S3: Based on adaptive theory and superhelical technology, establish a generalized adaptive superhelical observer.

[0033] The extended state observation model for the dq-axis current equations of a permanent magnet synchronous motor is established as follows: (16) in, , , ; ; , To control the gain; ; ; ; for The first derivative of the total perturbation of the axis.

[0034] Based on the extended state observation model (16), and with adaptive theory and superhelical technology, the generalized adaptive superhelical observer is: (17) The error correction term is designed as follows: (18) in, This is an estimate of the state vector; For a specific moment; The sampling period; To control profits; for Stator voltage at time 1; ; and This is the gain value of the observer; It is a variable exponent term, and ; For adaptive gain term; and This is the error correction term.

[0035] The designed adaptive gain term is (19) in, and These are adjustable parameters; For gain, It is the arctangent function.

[0036] Based on the observer's pole placement strategy, the gain value is tuned as follows: (20) in, This represents the bandwidth of the observer.

[0037] The error equation of the system is then: (twenty one) The stability of the obtained generalized adaptive superspiral observer is assessed, specifically including: The Lyapunv function is constructed as follows: (twenty two) Where P is a positive definite symmetric matrix; Its derivative with respect to time is: (twenty three) in, ; .

[0038] set up (twenty four) in, Based on this, by choosing appropriate matrices R and S, it can be guaranteed that... .

[0039] Suppose there exists a positive definite symmetric matrix P and positive constants. The following matrix inequalities must be satisfied: (25) This proves that the permanent magnet synchronous motor system is stable over a finite time.

[0040] At this point, taking the derivative of the Lyapunv function yields the following derivative: (26) According to equation (22), we can obtain: (27) in, Let be an eigenvalue of P, and .

[0041] Scenario 1: When Then, equation (26) is written as: (28) From equation (27), inequality Therefore, equation (28) is valid, so it can be written as follows: (29) Scenario 2: When When, equation (26) is written as (30) From equation (27), inequality Therefore, equation (30) is valid, so it can be written as follows: (31) Lemma 1: For dynamic systems ,and , There exists a positive definite matrix. satisfy , Represents a dynamic system function. , and These represent three gains, which are parameters without any special meaning. Let represent a positive definite matrix.

[0042] if and Then the system is stable in finite time, and the settling time is... , It is the initial state; if and Then the system is stable in finite time, and the settling time is... , This indicates the system's settling time.

[0043] According to Lemma 1, the second convergence condition is satisfied, and the convergence time for both cases is: (32) (33) Compared to existing superspiral state observers, this application implements a dynamic adaptive gain that can be dynamically adjusted according to changes in the error input. Specifically, when At that time, the power index was set to ;when At that time, the power index was adjusted to This improves the convergence speed of the observer. Furthermore, by introducing a linear error term and combining it with a nonlinear term, the perturbation estimation capability is further enhanced. Additionally, an adaptive gain is introduced to further optimize the observer's performance, improving both convergence speed and estimation accuracy while preventing overcompensation.

[0044] S4: The current loop controller is obtained through the deadbeat predictive control model and the generalized adaptive superspiral observer.

[0045] The control rate of the current loop controller is: (34).

[0046] S5: A quasi-resonant controller is established using a condition-switching resonant mode. The quasi-resonant controller and the deadbeat predictive control model are then applied together to the current loop controller to obtain the final current loop controller.

[0047] To address periodic disturbances in permanent magnet synchronous motor systems, this application proposes an improved quasi-resonant controller employing a conditionally switched resonant mode to suppress flux harmonics and harmonics caused by inverter nonlinearity. The transfer function of the quasi-resonant controller is: (35) in, In order to be in The step function at the switching point; The switching point is adjustable; For current tracking error, For reference current, Stator current; This is the resonant gain; The damping frequency; For Laplace variables; The resonant frequency; Discretizing the quasi-resonant controller yields the discretized transfer function as follows: (36) in, For the complex frequency domain of a discrete-time signal; .

[0048] It is understandable that when a permanent magnet synchronous motor system is in a dynamic process, if The resonant term in the quasi-resonant controller is disabled, and its state is reset to zero. When the permanent magnet synchronous motor system approaches steady state, if These resonant terms are then reactivated. This application avoids the side effects of traditional quasi-resonant controllers during dynamic operation while maintaining their performance under steady-state conditions, thereby significantly improving its effectiveness.

[0049] Furthermore, the discretized transfer function is added to the current loop controller, and combined with the deadbeat predictive control model, the final current loop controller is expressed as follows: (37).

[0050] In the deadbeat predictive control method proposed in this application, the generalized adaptive superspiral observer is used to estimate and compensate for aperiodic disturbances in the current loop; the quasi-resonant controller and the deadbeat predictive control model are implemented in parallel to estimate and compensate for periodic disturbances in the current loop. Compared with traditional deadbeat predictive control methods, this application can efficiently estimate and compensate for disturbances existing in the current loop, achieving high-performance current control.

[0051] Please see Figure 3 The diagram below shows the model structure of the permanent magnet synchronous motor servo control system described in the embodiment of this invention. To verify the effectiveness of the proposed permanent magnet synchronous motor current control strategy, the performance of the deadbeat predictive control method based on a combination of a generalized adaptive superspiral observer and improved quasi-resonant control is analyzed based on experimental results. The structure diagram of the servo control system of the permanent magnet synchronous motor based on field-oriented control and the proposed current loop control scheme is shown below. Figure 3 As shown. In Figure 3 In the experiment shown, the velocity loop uses PI control, and the current loop uses the deadbeat predictive control method proposed in this application, which combines a generalized adaptive superspiral observer and improved quasi-resonant control. The specific process includes: First, given the mechanical angular velocity reference input of the permanent magnet synchronous motor as... The electrical angle of the motor is obtained using an encoder. and angular velocity electrical angle Used for coordinate transformation, angular velocity Used to obtain the q-axis reference current for speed closed-loop control. Used for current loop control; then, the a-phase, b-phase, and c-phase currents of the motor are obtained using current sensors. , and Then, the motor is obtained through coordinate transformation. Actual current of the shaft Current loop control input is obtained for current control. Furthermore, the switching signal state vector of the inverter is obtained through coordinate transformation and SVPWM pulse modulation method. Finally, the inverter adjusts the power supply based on the DC bus voltage. The three-phase current output of the switch signal drives the permanent magnet synchronous motor.

[0052] based on Figure 3 A model of a permanent magnet synchronous motor servo control system is presented, and comparative experimental results of the traditional deadbeat predictive current control method and the deadbeat predictive control method proposed in this application are given.

[0053] Please see Figure 4 A schematic diagram showing the experimental results comparing the d-axis and a-phase current curves; Figure 5 This is a schematic diagram comparing the experimental results of the q-axis current curves. Specifically, it includes: With speed reference as rpm and a sudden increase of 3 at 10 seconds Under the condition of step disturbance, from Figure 4 and Figure 5 The method adopted in this application can be obtained from shaft and The shaft current fluctuations were 0.129A and 0.147A, respectively, which are much smaller than those achieved using traditional methods. shaft and The shaft current fluctuations were 0.288A and 0.246A; additionally, the method employed in this application yielded... Harmony The total harmonic distortion of the shaft currents were 1.42% and 1.78%, respectively, which are much lower than those achieved using traditional methods. shaft and The total harmonic distortion of the shaft current is 3.39% and 2.63%; and after a sudden load application of 10 seconds, the results are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] The times for the shaft current to reach steady state were 1.875 s and 1.884 s, respectively. Based on this, it is verified that the method of this application has better dynamic and steady-state performance, and can achieve more robust and smooth current control of permanent magnet synchronous motors.

[0054] Please see Figure 6 This is a schematic diagram showing the experimental results of Fourier analysis of the q-axis current. Using the method described in this application, [the following is possible:]... The 6th and 12th harmonics of the shaft current were significantly reduced, verifying that the deadbeat predictive control method proposed in this application can effectively compensate for periodic disturbance estimation.

[0055] Please see Figure 7 , for inductance When mismatch shaft and Schematic diagram of phase current comparison experiment results; Figure 8 Inductor When mismatch Results of the shaft current comparison experiment. Specifically, this includes: In the case of inductor parameter mismatch, from Figure 7As can be seen, compared with traditional methods, the method proposed in this application is significantly less sensitive to inductance parameters, and the mismatch of inductance parameters is reduced. The effect of shaft current is almost negligible. Figure 8 As can be seen from this, in the dynamic characteristics during the inductance mismatch period, the traditional method... The shaft current overshoot is at its maximum. and The current overshoot values ​​were 0.885A and 0.5A respectively, while the current overshoot using the method of this application was the smallest, at 0.384A and 0.206A respectively; furthermore, the method of this application... and The recovery times for the two methods were 26 ms and 16 ms, respectively, compared to 29 ms and 23 ms for the traditional method. Furthermore, compared to the steady-state current after inductor mismatch, the method of this application exhibits the smallest change in current variation, with a total harmonic distortion of 1.78% (at...). (at time) and 1.68% (in (At that time), while the traditional method resulted in the largest changes, with total harmonic distortion values ​​of 3.33% and 2.34%, respectively. Furthermore, it can be seen from the current loop controller expression that the motor parameter resistance... and permanent magnet magnetic flux The proposed algorithm's control performance remains unaffected. This verifies that the deadbeat predictive control method proposed in this application has stronger robustness and can achieve higher precision current control.

[0056] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0057] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predictive control without delay, characterized in that, include: Establish the voltage equation for the permanent magnet synchronous motor; Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, a deadbeat predictive control model is established. Based on adaptive theory and superhelical technology, a generalized adaptive superhelical observer is established. The steps involved in establishing this observer include the following: The extended state observation model for the dq-axis current equations of a permanent magnet synchronous motor is established as follows: ， in, , , , and They are respectively shaft and Stator current of the shaft, and These represent the total disturbances along the d-axis and q-axis, respectively. ; , To control the gain, for shaft and The nominal value of the shaft stator inductance; ; ; , and They are respectively shaft and Stator voltage of the shaft; for The first derivative of the total perturbation of the axis; Based on the extended state observation model, and using adaptive theory and superhelical technology, the generalized adaptive superhelical observer is as follows: ， The error correction term is designed as follows: ， in, This is an estimate of the state vector; For a specific moment; The sampling period; To control profits; for Stator voltage at time 1; ; and This is the gain value of the observer; It is a variable exponent term, and ; For adaptive gain term; and This is the error correction term; the designed adaptive gain term is... （19） in, and These are adjustable parameters; For gain, It is the arctangent function; The current loop controller is obtained by using the deadbeat predictive control model and the generalized adaptive superspiral observer; A quasi-resonant controller is established using a condition-switching resonant mode. The quasi-resonant controller and the deadbeat-free predictive control model are then applied together to the current loop controller to obtain the final current loop controller.

2. The method for predictive control without delay according to claim 1, characterized in that, The expressions for the aperiodic disturbance in the current loop disturbance are as follows: ， ， in, and They are respectively shaft and Aperiodic disturbances on the axis; and They are respectively shaft and Cross-coupling disturbances of the axes; and They are respectively shaft and Known disturbances to the axis; and They are respectively shaft and Disturbances caused by parameter perturbations on the axis; and They are respectively shaft and External uncertainties of the axis; This is the nominal value of the stator resistance. This is the nominal value of the rotor flux; , , ; The expressions for the periodic disturbance in the current loop disturbance are as follows: ， ， in, and They are respectively shaft and magnetic flux harmonics of the shaft, and They are respectively shaft and The amplitude of the 6nth magnetic flux harmonic of the axis; Electric angular velocity; and They are respectively shaft and Voltage harmonics of the shaft; and These are the dead time and sampling period, respectively. This is the DC bus voltage; Permanent magnet synchronous motor The periodic disturbance of the axis is represented as: ， in, and They are respectively shaft and Periodic disturbances of the axis; and They are respectively shaft and Stator inductance of the shaft; The first-order hyperlocal mathematical model is represented as: ， in, and These are the model's output and input variables, respectively. It is a set of known and unknown disturbances; It is a scaling factor; The first-order hyperlocal mathematical model of the current loop is finally expressed as: ， in, and These represent the total disturbances along the d-axis and q-axis, respectively. To control the gain.

3. The method for predictive control without delay according to claim 2, characterized in that, The step of establishing a deadbeat-free predictive control model based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor specifically includes the following steps: The voltage equation of the permanent magnet synchronous motor is expressed as: ， ， in, and They are respectively shaft and Axis in Reference voltage at any given time; and They are respectively shaft and Axis in Reference current value at any given time; and They are respectively shaft and Axis in Stator current at time t; Stator inductance; The sampling period; Stator resistance; for electric angular velocity at time t, For rotor flux; Based on the voltage equation and current loop disturbance of the permanent magnet synchronous motor, the deadbeat predictive control model of the permanent magnet synchronous motor based on the hyperlocal model is obtained, which is expressed as: ， in, and These represent the total disturbances along the d-axis and q-axis, respectively. To control the gain.

4. The beat-free predictive control method according to claim 3, characterized in that, In the step of obtaining the current loop controller through the deadbeat predictive control model and the generalized adaptive superspiral observer, the control law of the current loop controller is: 。 5. The beat-free predictive control method according to claim 4, characterized in that, The step of establishing a quasi-resonant controller using a condition-switching resonant mode, and then applying the quasi-resonant controller and the deadbeat-free predictive control model together to the current loop controller to obtain the final current loop controller, specifically includes the following steps: The transfer function of the quasi-resonant controller is: ， in, In order to be in The step function at the switching point; The switching point is adjustable; For current tracking error, For reference current, Stator current; This is the resonant gain; The damping frequency; For Laplace variables; The resonant frequency; Discretizing the quasi-resonant controller yields the discretized transfer function as follows: ， in, For the complex frequency domain of a discrete-time signal; .

6. The method for predictive control without delay according to claim 5, characterized in that, Adding the discretized transfer function to the current loop controller and combining it with the deadbeat predictive control model, the final current loop controller is expressed as follows: 。 7. The method for predictive control without delay according to claim 1, characterized in that, The generalized adaptive superspiral observer is used to estimate and compensate for aperiodic disturbances in the current loop disturbance. The quasi-resonant controller and the deadbeat predictive control model are implemented in parallel to estimate and compensate for periodic disturbances in the current loop.

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