Permanent magnet synchronous motor model-free predictive current control method of hybrid trigger mechanism

Abstract

The invention discloses a model-free predictive current control method for a permanent magnet synchronous motor of a hybrid trigger mechanism. The model-free predictive current control method specifically comprises the following steps: step 1, establishing a mathematical model of the permanent magnet synchronous motor in a d-q plane; step 2, establishing a hyper-local model of the permanent magnet synchronous motor; 3, designing a second-order linear expansion state observer for estimating the current value of the permanent magnet synchronous motor; step 4, performing discretization processing on the state observer; step 5, designing an event trigger mechanism ESO; step 6, designing ESO of a hybrid trigger mechanism; step 7, acquiring a voltage vector of the current control period according to a hybrid trigger mechanism ESO; and step 8, according to the voltage vector determined in the step 7, controlling the on-off of a switching tube, and further controlling the d-q axis current actual value of the motor to change along with the reference value. According to the method, the problems of large current fluctuation and poor control effect caused by frequent updating of the estimation state in the existing motor model-free prediction current control are solved.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet synchronous motor control technology, and relates to a model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism. Background Technology

[0002] A permanent magnet synchronous motor (PMSM) is a type of synchronous motor that operates using the magnetic field of a permanent magnet. Compared to other motors, it has advantages such as high efficiency, fast dynamic response, and low noise, and is widely used in aerospace, robotics, and new energy fields. PMSMs are driven by inverters, which are power conversion devices that convert direct current (DC) to alternating current (AC). A commonly used three-phase full-bridge inverter circuit for PMSMs consists of three bridge arms composed of six switching transistors. To prevent short circuits, the upper and lower bridge arms of each phase cannot be turned on simultaneously. When the upper bridge arm of each phase is on, it is "1", and when the lower bridge arm is on, it is "0". The three bridge arms form a total of eight switching states. By using a modulation strategy to switch the switching states, the output voltage waveform is made sinusoidal.

[0003] Model-free predictive control (MDR) is an emerging control method for permanent magnet synchronous motors. Compared to traditional model predictive control (MMDC), MDR does not require detailed motor parameters; it controls the motor solely based on the input voltage and output current values ​​of the control system. However, MDR calculates the system's current and disturbance values ​​in each control cycle, resulting in unnecessary state updates. These frequent updates introduce disturbances caused by sensor noise, inverter nonlinearity, and other factors into the current loop, leading to more severe current ripple and impacting the control performance of the current loop. Summary of the Invention

[0004] The purpose of this invention is to provide a model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism, which solves the problem of large current fluctuations and poor control effect caused by frequent updates of the estimated state in existing model-free predictive current control of motors.

[0005] The technical solution adopted in this invention is a model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism, which specifically includes the following steps: Step 1, in dq A mathematical model of a permanent magnet synchronous motor is established in a plane; Step 2: Establish a hyperlocal model of the permanent magnet synchronous motor; Step 3: Design a second-order linear extended state observer to estimate the current value of the permanent magnet synchronous motor; Step 4: Discretize the state observer; Step 5, design the event triggering mechanism ESO; Step 6: Design an ESO with a hybrid triggering mechanism; Step 7: Obtain the voltage vector for the current control cycle based on the hybrid triggering mechanism ESO; Step 8: Based on the voltage vector determined in Step 7, control the switching transistor to turn on and off, thereby controlling the motor. dq The actual value of the shaft current follows the reference value.

[0006] The invention is further characterized by: The specific process of step 1 is as follows: The mathematical model of the permanent magnet synchronous motor is shown in the following formula (1): (1) In the formula, u d and i d They are respectively d Shaft stator voltage and current; u q , i q They are respectively q Shaft stator voltage and current; L s For stator inductance; R s Stator resistance; oh r This refers to the rotor speed of the motor. ψ f It is the magnetic flux of a permanent magnet.

[0007] The specific process of step 2 is as follows: Step 2.1: Establish a first-order hyperlocal model of a single-input single-output system: (2) In the formula, u and y These are the control variables and the output variables, respectively. It is an output variable y The first derivative; α This represents a designable scaling factor; F This represents the total disturbance of the system; Step 2.2, based on the mathematical model of the permanent magnet synchronous motor established in Step 1 and the hyperlocal model of the first-order system established in Step 2.1, in dq In the plane, the hyperlocal model of the permanent magnet synchronous motor is established as follows: (3) In the formula, s Indicates motor d shaft or q axis; i s and u sMotors dq Shaft current and voltage; α It can be set to the reciprocal of the inductance.

[0008] The specific process of step 3 is as follows: Step 3.1, establish a second-order linear ESO, the expression of which is: (4) Step 3.2, write the state observer in equation (4) in matrix form so that the system can be represented in state space: (5) Constructing a state-space model: (6) The characteristic equation of ESO is derived using the state-space model: (7) To ensure that all roots of the characteristic equation lie in - oh At point 0, ensure system stability, β 1=2 oh 0, β 2= oh 0 2 .

[0009] The specific process of step 4 is as follows: The discretized ESO mathematical model is: (8) In the formula, k This represents the current control cycle count; e rr For the first k The estimation error of the current within each control cycle; and For the first k Within each control cycle dq Estimates of shaft current and disturbance; and For the first k +1 control cycle dq Estimates of shaft current and disturbance; i s ( k ) is the first k Within each control cycle dq Actual value of shaft current; u s ( k )| j The eight switch states corresponding to the switch states from 000 to 111 dq Shaft input voltage, j =0,…,7; Tc The control cycle for the controller to generate switching signals based on the control algorithm; β 01 and β 02 Gain of the observer β 1 and β 2 and sampling period T sample The product of.

[0010] The specific process of step 5 is as follows: The event-triggered mechanism ESO updates the current estimate of the next control cycle according to formula (8) only when the triggering condition is met. If the triggering condition is not met, the current estimate of the next moment will maintain the estimate of the previous triggering. The triggering condition of the event-triggered mechanism ESO is designed as follows: First, define the estimation error of the event-triggered mechanism ESO: (9) In the formula, i This represents the current number of times the event has been triggered. The estimated error of the current control cycle event triggering mechanism ESO; For the event-triggered mechanism ESO in the first i Second trigger dq shaft current estimate; The current control cycle is calculated using formula (8) to obtain the time-triggered mechanism ESO. dq shaft current estimate; To ensure the stability of the system's input-output state, the estimation error of the current value satisfies the following theorem: (10) In the formula, m It is a positive scalar. d It is a K∞ class function of the state gauge in the ISS Lyapunov derivative inequality; Based on the expression (8) of the time-triggered mechanism ESO and the expression (9) of the error, the inequality satisfying equation (10) is derived as follows: (11) Therefore, the trigger condition is set as follows: (12) In the formula, , , and These represent the upper bounds of the estimated current value for the event-triggered ESO mechanism, the estimated current value for the time-triggered ESO mechanism, the estimated total system disturbance value, and the voltage value, respectively. t eThe time from the last time the event was triggered to the current time.

[0011] The specific process of step 6 is as follows: Configure the switching method for the triggering mechanism so that the hybrid triggering mechanism ESO can determine the current operating condition and switch automatically: Record. N Number of event triggers within a sampling period k Through this number and the set threshold k 1. Compare and use as a switching flag for the hybrid triggering mechanism. If k Less than the threshold k If the value is 1, event-triggered control is used; otherwise, time-triggered control is used. Set threshold k 1: (13) In the formula, m This indicates the number of historical triggers used to calculate the threshold; a Indicates from the first i The event was triggered to the first i - m The number of times a specific event is triggered within a given event trigger. a= 0 , 1 ,…,m ; T e ( i - a ) indicates the first i - a Next and first i - a- The time interval between one event triggers.

[0012] Step 7 is as follows: Based on the hybrid triggering mechanism ESO designed in Step 6, the estimated current values ​​corresponding to eight voltage vectors are output in each control cycle. The value function value corresponding to each voltage vector is calculated based on the current estimated values. The voltage vector corresponding to the smallest value function value is selected as the voltage vector for the current control cycle. The value function is designed as follows: When the ESO is under time-triggered control, the value function is used... Select voltage vector: (14) In the formula, for dq Shaft current reference value; For hybrid triggering mechanism ESO dq shaft current estimate; When ESO operates under an event-triggered mechanism, design the value function. , Introduction d - q Angular change value of shaft current: (15) Δ i s ( k +1) is: (16) In the formula, l The weighting factor represents the angle.

[0013] The beneficial effects of this invention are as follows: While the PMSM model-free predictive control method based on the traditional time-triggered oscillation (ESO) mechanism can achieve control accuracy similar to model predictive control without motor parameters, its control effect relies on the ESO calculating the system's current and disturbance values ​​in each control cycle. Frequent updates introduce more disturbances, leading to larger current ripple. By rationally designing the hybrid triggering mechanism of the ESO and the value functions under different triggering mechanisms, the triggering mechanism can be switched appropriately. This avoids unnecessary state updates in steady state, suppresses current fluctuations caused by disturbances, and ensures the response speed of state estimation in dynamic state, thus achieving better control performance. Attached Figure Description

[0014] Figure 1 This is a structural diagram of the permanent magnet synchronous motor drive control system, the control object of the model-free predictive current control method for permanent magnet synchronous motors based on the hybrid triggering mechanism of this invention. Figure 2 This is a flowchart of the model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism according to the present invention. Figure 3 This is a schematic diagram illustrating the calculation of the current angle change at the next moment in the value function of the model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism according to the present invention. Figures 4(a) to 4(d) are curves comparing the control effects of the model-free predictive current control method for permanent magnet synchronous motors with the hybrid triggering mechanism of the present invention with the traditional method. Detailed Implementation

[0015] The following detailed description is provided in conjunction with specific implementation methods.

[0016] Example 1 This invention relates to a model-free predictive current control method for permanent magnet synchronous motors (PMSMs) using a hybrid triggering mechanism. The controlled object of this invention is a PMSM system, which consists of a DC power supply, a controller, an inverter, and a PMSM. Figure 1As shown in the diagram. First, a hyperlocal model without PMSM parameters is constructed to ensure the robustness and parameter stability of the designed control strategy. Second, a discrete ESO based on a hybrid triggering mechanism is designed. Under steady-state conditions, an event-triggered mechanism is used to avoid unnecessary estimation state updates and suppress current fluctuations caused by disturbances. Under dynamic conditions, a time-triggered mechanism is used to ensure the estimation response speed. A switching mechanism for triggering conditions is designed based on the number of event triggers. The flowchart of the control method of this invention is shown in the diagram. Figure 2 As shown.

[0017] Example 2 The model-free predictive current control method for permanent magnet synchronous motors with a hybrid triggering mechanism of the present invention specifically includes the following steps: Step 1, in dq A mathematical model of a permanent magnet synchronous motor in a plane is established, and its expression is: (1) In the formula, u d and i d They are respectively d Shaft stator voltage and current; u q , i q They are respectively q Shaft stator voltage and current; L s For stator inductance; R s Stator resistance; oh r This refers to the rotor speed of the motor. ψ f It is the magnetic flux of a permanent magnet; Step 2: Establish a hyperlocal model of the permanent magnet synchronous motor. Step 3: Based on the hyperlocal model of the permanent magnet synchronous motor in Step 2, design a second-order linear extended state observer (ESO) to estimate the current value of the permanent magnet synchronous motor. Step 4: Based on the ESO mathematical model in Step 3, perform discretization processing to obtain a discretized ESO mathematical model. The ESO that updates the current estimate in each control cycle using this model is the time-triggered ESO mechanism. Step 5: Based on the time-triggered ESO mechanism established in Step 4, design an event-triggered ESO mechanism. The event-triggered ESO mechanism updates the current estimate for the next control cycle only when the triggering condition is met, according to formula (8). If the triggering condition is not met, the current estimate at the next moment remains the estimate from the previous trigger. Step 6: Combining the time-triggered ESO designed in Step 4 and the event-triggered ESO designed in Step 5, a hybrid triggering mechanism ESO is designed. In steady state, the event-triggered ESO is used to estimate the current value, while in dynamic state, the time-triggered ESO is used. Step 7: Based on the hybrid triggering mechanism ESO designed in Step 6, in each control cycle, the estimated current values ​​corresponding to eight voltage vectors are output. The value function value corresponding to each voltage vector is calculated based on the current estimates, and the voltage vector corresponding to the smallest value function value is selected as the voltage vector for the current control cycle. Step 8: Based on the voltage vector determined in Step 7, the controller outputs a control signal to control the switching transistors, thereby controlling the motor. dq The actual value of the shaft current follows the reference value.

[0018] Example 3 The specific steps of step 2 are as follows: Step 2.1, establish a first-order hyperlocal model of a single-input single-output system: (2) In the formula, u and y These are the control variables and the output variables, respectively. It is an output variable y The first derivative; α This represents a designable scaling factor; F This represents the total disturbance of the system; Step 2.2, based on the mathematical model of the permanent magnet synchronous motor established in Step 1 and the hyperlocal model of the first-order system established in Step 2.1, in dq In the plane, the hyperlocal model of a permanent magnet synchronous motor can be established as follows: (3) In the formula, s Indicates motor d shaft or q axis; is and u s Motors dq Shaft current and voltage; α It can be set to the reciprocal of the inductance.

[0019] Example 4 The specific steps of step 3 are as follows: Step 3.1, establish a second-order linear ESO, the expression of which is: (4) In the formula, e rr It is an estimation error; z 1 represents the stator current. i s The estimated value, z 2 represents the total disturbance. F The estimated value; and It is an estimated value. z 1 and z The derivative of 2; β 1 and β 2 represents the gain of the observer; Step 3.2, design the gain of the ESO in Step 3.1 using the bandwidth method. β 1 and β 2: Write the state observer in equation (4) in matrix form so that the system can be represented in state space: (5) Constructing a state-space model: (6) The characteristic equation of ESO can be derived using the state-space model: (7) To ensure that all roots of the characteristic equation lie in - oh At point 0, ensure system stability, β 1=2 oh 0, β 2= oh 0 2 . oh 0 is called the bandwidth parameter of this ESO, which only requires... oh By designing 0 pairs, the gain of the ESO can be determined. oh The value of 0 is usually chosen within the range of 4000 to 10000, and fine-tuned within this range based on simulation and experimental results. Example 5 Step 4 is as follows: The discretized ESO mathematical model is: (8) In the formula, kThis represents the current control cycle count; e rr For the first k The estimation error of the current within each control cycle; and For the first k Within each control cycle dq Estimates of shaft current and disturbance; and For the first k +1 control cycle dq Estimates of shaft current and disturbance; i s ( k ) is the first k Within each control cycle dq Actual value of shaft current; u s ( k )| j The eight switch states corresponding to the switch states from 000 to 111 dq Shaft input voltage, j =0,…,7; T c The control cycle for the controller to generate switching signals based on the control algorithm; β 01 and β 02 Gain of the observer β 1 and β 2 and sampling period T sample The product of.

[0020] Example 6 Step 5 involves designing the triggering conditions for the event-triggered mechanism (ESO): First, defining the estimation error of the ESO: (9) In the formula, i This represents the current number of times the event has been triggered. The estimated error of the current control cycle event triggering mechanism ESO; For the event-triggered mechanism ESO in the first i Second trigger dq shaft current estimate; The current control cycle is calculated using formula (8) to obtain the time-triggered mechanism ESO. dq Shaft current estimate; to ensure system input-output stability, the estimation error of the current value should satisfy the following theorem: (10) In the formula, m It is a positive scalar. d These are K∞-class functions of the state gauge in the ISS Lyapunov derivative inequality. To ensure stability, d and m The appropriate choice should be made based on the Lipschitz constant; based on the expression (8) of the time-triggered mechanism ESO and the expression (9) of the error, the inequality satisfying equation (10) is derived as follows: (11) In the formula, , , and These represent the upper bounds of the ESO estimated current value under the event-triggered mechanism, the ESO estimated current value under the time-triggered mechanism, the estimated total system disturbance value, and the voltage value, respectively; te is the time from the last event trigger moment to the current moment; therefore, the triggering condition can be set as follows: (12) Example 7 Step 6 involves setting the switching mode of the triggering mechanism so that the hybrid triggering mechanism ESO can determine the current operating condition and switch automatically. The state of the current loop can be effectively determined based on the number of triggers in the event triggering mechanism. The number of event triggers k within N sampling periods is recorded and compared with a set threshold k1 as a switching flag for the hybrid triggering mechanism. If k is less than the threshold k1, event triggering control is used; otherwise, a time triggering mechanism is used. The threshold k1 is then set. (13) In the formula, m This indicates the number of historical triggers used to calculate the threshold; a Indicates from the first i The event was triggered to the first i - m The number of times a specific event is triggered within a given event trigger. a= 0 , 1 ,…,m ; T e ( i - a ) indicates the first i - a Next and first i - a- The time interval between one event triggers; N and m The value of should be selected reasonably based on the system's operating conditions. In situations with high noise levels, can be ... N Set it to 30-50 to extend the statistical window and reduce the impact of instantaneous fluctuations on the judgment results. m Take 3N This enhances the smoothness of the estimation and ensures the robustness of the system. When the system is under frequent dynamic conditions or requires rapid switching of control modes, it can... N Set to 10-20, and decrease accordingly. m Up to 2 N This is to reflect changes in the triggering frequency in a timely manner, thereby improving the dynamic response speed; Example 8 Step 7 involves the following process: designing the value function: when the ESO is under time-triggered control, the value function is used. Select voltage vector: (14) In the formula, for dq Shaft current reference value; For hybrid triggering mechanism ESO dq Shaft current estimation; design value function when ESO is in event-triggered mode. Introducing d - q Angular change value of shaft current: (15) In the formula, l The weighting factor representing the angle, to ensure that the dimensions of the current term and the angle change term in the value function are consistent, can be set to 0.2~0.3; Δ i s ( k +1) is: (16) In the value function d - q Shaft current angle change value Δ i s ( k +1) diagram as shown Figure 3 As shown, due to the first k The voltage vector is different in each control cycle, the first... k The current during +1 control cycle can exhibit eight different scenarios, as shown by the blue dashed line in the diagram, corresponding to eight different Δ values. i s ( k +1) value; Under steady-state conditions, to avoid due to d - q Frequent angular changes in the shaft current are incorporated into the value function. d - q The angular change value of the shaft current can be considered when selecting the voltage vector, taking into account each voltage vector pair. d -q The influence of shaft current angle change, thereby suppressing d - q Fluctuations in shaft current; Example 9 A simulation model was built in Matlab / Simulink to verify the feasibility and performance of the proposed control method. The parameters of the motor control system model are: DC bus voltage 270V, number of motor pole pairs 5, stator inductance 0.002H, stator resistance 0.62Ω, and permanent magnet flux linkage 0.0672Wb. The d-axis current reference value was set to 0A, the initial q-axis current reference value was 10A, and the step value was set to 20A at 0.2s. The proposed method was compared with the model-free predictive current method based on the time-triggered mechanism ESO (MFPCC) through simulation. The results are shown in Figure 4. Figure 4(a) and Figure 4(b) show the results of the proposed method and MFPCC, respectively. d The shaft current waveforms, Figure 4(c) and Figure 4(d) are respectively those of the method in this case and the MFPCC. q Shaft current waveform diagram. Simulation results show that, under steady-state conditions, the proposed method... dq The shaft current fluctuation is less than that of MFPCC. Under dynamic operating conditions, the switching conditions of the ESO triggering mechanism designed in this method can quickly identify the operating conditions and switch accordingly, ensuring a rapid response of current control under dynamic conditions.

Claims

1. A method for model-free predictive current control of permanent magnet synchronous motor with hybrid triggering mechanism, characterized in that: Specifically comprising the following steps: Step 1, in d-q Mathematical model of the permanent magnet synchronous motor is established in the plane; Step 2, establishing a hyper-local model of the permanent magnet synchronous motor; Step 3, designing a second-order linear extended state observer to estimate the current value of the permanent magnet synchronous motor; Step 4, discretizing the state observer; Step 5, designing an event-triggered mechanism ESO; Step 6, designing an ESO with a hybrid triggering mechanism; Step 7, obtaining the voltage vector of the current control cycle according to the ESO with a hybrid triggering mechanism; Step 8, according to the voltage vector determined in step 7, control the switch tube on-off, and then control the motor d-q The shaft current actual value follows the reference value change.

2. The hybrid trigger mechanism-based model-free predictive current control method of a permanent magnet synchronous motor according to claim 1, characterized in that: The specific process of step 1 is that the mathematical model of the permanent magnet synchronous motor is shown in the following formula (1): （1） wherein u d and i d are respectively d shaft stator voltage and current; u q , i q are respectively q shaft stator voltage, current; L s is the stator inductance; R s is the stator resistance; ω r is the motor rotor speed; ψ f is the flux of the permanent magnet.

3. The hybrid-flux mechanism of PMSM model-free predictive current control method according to claim 2, characterized in that: The specific process of step 2 is as follows: Step 2.1, a first-order hyper-local model of a single-input single-output system is established: （2） where u and y are control and output variables, respectively, is the first derivative of the output variable y ; and α represents a designable scaling factor; F represents the total disturbance to the system. Step 2.

2. Based on the mathematical model of the PMSM established in Step 1 and the hyperlocal model of the first-order system established in Step 2.1, the hyperlocal model of the PMSM is established in the plane as follows: d-q In the plane, the hyperlocal model of the PMSM is established as follows: （3） wherein s represents the motor d shaft or q shaft; i s and u s are the motor d-q shaft current and voltage, respectively; α may be set to the inverse of the inductance.

4. The hybrid-flux mechanism of PMSM model-free predictive current control method according to claim 3, characterized in that: The specific process of step 3 is as follows: Step 3.1, a second-order linear ESO is established, and the expression is as follows: （4） wherein e rr is an estimate error; z 1 is a stator current i s estimate value, z 2 is a total disturbance F estimate value; and is a derivative of the estimate values z 1 and z 2; β 1 and β 2 are gains of the observer; Step 3.2, the state observer in formula (4) is written in matrix form to represent the system through the state space: （5） Construct a state space model: （6） Through the state space model, the characteristic equation of the ESO is derived: （7） To make the roots of the characteristic equation all fall in ω 0 place, guarantee the stability of the system, make β 1 = 2 ω 0, β 2 = ω 0 2 .

5. The hybrid-flux mechanism of PMSM model-free predictive current control method according to claim 4, characterized in that: The specific process of step 4 is that the discretized ESO mathematical model is as follows: （8） wherein k is the number of current control cycles; e rr is the estimated error of the current in the k th control cycle; and is the estimated error of the current in the k th control cycle; d-q is the estimated value of the shaft current and disturbance in the u th control cycle; s k j is the input voltage of the shaft corresponding to the eight switching states from 000 to 111 d-q β 01 and β 02 is the product of the gain of the observer β 1 and β 2 and the sampling period T sample .​​ 6. The hybrid-flux mechanism model-free predictive current control method of permanent magnet synchronous motor according to claim 5, characterized in that: The specific process of step 5 is that the event-triggered mechanism ESO updates the current estimation value of the next control cycle according to formula (8) only when the triggering condition is met, and if the triggering condition is not met, the current estimation value at the next time is maintained at the estimation value at the last triggering time. The triggering condition of the event-triggered mechanism ESO is designed as follows: First, define the estimation error of the event-triggered mechanism ESO: （9） In the formula, i This represents the current number of times the event has been triggered. The estimated error of the current control cycle event triggering mechanism ESO; For the event-triggered mechanism ESO in the first i Second trigger d-q shaft current estimate; The current control cycle is calculated using formula (8) to obtain the time-triggered mechanism ESO. d-q shaft current estimate; To ensure the stability of the input-output state of the system, the estimation error of the current value satisfies the following theorem: （10） wherein μ is a positive scalar, δ is a K∞-like function of the state norm in the ISS Lyapunov derivative inequality; According to the expression (8) of the time-triggered mechanism ESO and the expression (9) of the error, the inequality satisfying formula (10) is derived as follows: （11） Therefore, the triggering condition is set as: （12） wherein, , , and respectively represent the event-triggered mechanism ESO estimated current value, the time-triggered mechanism ESO estimated current value, the system total disturbance estimated value and the upper bound of the voltage value; t e is the time from the last event-triggered time to the current time.

7. The hybrid-flux mechanism of PMSM model-free predictive current control method according to claim 6, characterized in that: The specific process of step 6 is to set the switching mode of the triggering mechanism, so that the hybrid triggering mechanism ESO can judge the current working condition and automatically switch: record the number of event triggers k in N sampling periods, and compare this number with the set threshold k1 as the switching flag of the hybrid triggering mechanism. If k is less than the threshold k1, event-triggered control is adopted, and if k is greater than the threshold, time-triggered mechanism is adopted; Set the threshold k1: （13） In the formula, μ represents the historical trigger times used to calculate the threshold value; a represents the time interval from the i th event trigger to the i th event trigger; μ a= , ,…,μ T e i a i a i a- represents the time interval from theth event trigger to theth event trigger.​​​​​​​​ 8. The hybrid-flux mechanism model-free predictive current control method of permanent magnet synchronous motor according to claim 7, characterized in that: The specific process of the step 7 is: according to the mixed trigger mechanism ESO designed in the step 6, in each control period, output eight voltage vector corresponding current estimation values, calculate the value function value corresponding to each voltage vector according to the current estimation value, select the voltage vector corresponding to the minimum value function value as the voltage vector of the current control period, and design the value function: when the ESO is in time trigger control, adopt the value function Select the voltage vector: （14） In the formula, is d-q Shaft current reference value; is the mixed trigger mechanism ESO d-q Shaft current estimation value; Designing the value function when ESO is in event-triggered mechanism , introducing d - q Angular variation of the shaft current: （15） Δ θ s ( k +1) is: （16） In the formula, λ a weight factor representing an angle.

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