ESO-based sensorless pmsm model-free predictive control method, system and medium

Abstract

This invention relates to the field of motor speed control technology, and discloses a sensorless model-free predictive control method, system, and medium for a permanent magnet synchronous motor (PMSM) based on an extended state observer (ESO). The method includes: constructing a current model of the PMSM and discretizing it to obtain a discrete current model; constructing a discrete first-order single-input single-output hyperlocal model to obtain system-independent parameters and uncertainties; predicting the uncertainties and system-independent parameters using a model-free controller based on an extended state observer, thereby obtaining the control voltage for the PMSM; and using the control voltage to control the PMSM; estimating the back electromotive force (EMF) of the PMSM using the extended state observer, and extracting the electric angular velocity and electric angle of the motor from the estimated back EMF using a phase-locked loop (PLL) to achieve stable sensorless control of the PMSM. This invention can improve the estimation accuracy of the back EMF, enhance anti-interference capability, and improve stability.

Description

Technical Field

[0001] This invention relates to the field of motor speed control technology, and in particular to a sensorless PMSM model-free predictive control method, system and medium based on ESO. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) have advantages such as simple structure, small size, light weight, high efficiency and wide operating range. Therefore, PMSMs based on advanced control algorithms have been continuously developed and are widely used in various industrial fields such as home appliances, electric vehicles, rail transportation, and aerospace.

[0003] In the case of sensorless control of permanent magnet synchronous motors (PMSMs), the performance of traditional PI current control is easily affected by changes in parameters such as resistance, inductance, and environmental conditions. Traditional sliding mode observers are also sensitive to system parameters, and their back electromotive force estimation accuracy and anti-interference capability are both low. To improve the reliability of motor operation, anti-interference control is needed for sensorless PMSMs to reduce the impact of parameter changes on their speed regulation performance. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a sensorless PMSM model-free predictive control method, system and medium based on ESO, which can improve the estimation accuracy of back electromotive force, improve anti-interference ability and stability.

[0005] To address the aforementioned technical problems, this invention provides a sensorless PMSM model-free predictive control method based on ESO, comprising:

[0006] A current model of a permanent magnet synchronous motor is constructed, and the current model is discretized to obtain a discrete current model. Based on the discrete current model, a discrete first-order single-input single-output hyperlocal model is constructed to obtain parameters and uncertainties of the system that are independent of the system.

[0007] Based on the model-free controller based on the extended state observer, the uncertain part of the system is predicted and the parameters that are independent of the system are predicted. The control voltage of the permanent magnet synchronous motor is obtained by combining the parameters that are independent of the system and the uncertain part of the system. The control voltage is used to realize the control of the permanent magnet synchronous motor.

[0008] By estimating the back electromotive force of the permanent magnet synchronous motor using an extended state observer, and then extracting the electric angular velocity and electric angle of the motor from the estimated back electromotive force using a phase-locked loop, stable sensorless control of the permanent magnet synchronous motor is achieved.

[0009] Furthermore, the construction of the current model for the permanent magnet synchronous motor involves discretizing the current model to obtain a discrete current model, specifically as follows:

[0010] The principle of permanent magnet synchronous motor is based on the stator current differential equation under the dq axis:

[0011]

[0012] Among them, i d i q These are the d-axis current and the q-axis current, respectively. s It's an inductor, u d u q These are the d-axis voltage and q-axis voltage, respectively, where R is the stator resistance and ω is the d-axis voltage. e It is the electric angular velocity, ψ f It is a permanent magnet flux chain;

[0013] Discretizing the stator current differential equation yields the discrete current model as follows:

[0014]

[0015] Among them, i d(k) i represents the d-axis current sampling value at time k. q(k) T represents the q-axis current sampling value at time k. s It is the sampling time, ω e(k) U is the electric angular velocity at time k. d (k), u q(k) These are the d-axis voltage and the q-axis voltage at time k, respectively.

[0016] Furthermore, the discrete first-order single-input single-output hyperlocal model is as follows:

[0017] i s(k+1) =i s(k) +T s αu s(k) +T s F s(k) ,

[0018] Among them, i s(k) Let T be the stator current at time k. s It is the sampling time, u s(k) Let F be the stator voltage at time k, α be a parameter independent of the system, and F be the stator voltage at time k. s(k) Let k be the uncertain part of the system at time k.

[0019] Furthermore, the prediction method that does not depend on system parameters is as follows:

[0020] The difference equation for two consecutive periodic currents is constructed as follows:

[0021] Δi s(k) =i s(k) -i s(k-1) =T s (αu s(k-1) +F s(k) ),

[0022] Δi s(k-1) =i s(k-1) -i s(k-2) =T s (αu s(k-2) +F s(k-1) );

[0023] Where, Δi s(k) T is the difference between the current at time k and time k-1. s For sampling time, i s(k) Let u be the stator current at time k. s(k) Let F be the stator voltage at time k, α be a parameter independent of the system, and F be the stator voltage at time k. s(k) The uncertainty of the system at time k;

[0024] When the sampling frequency is high enough, the method for calculating parameters that are independent of the system is as follows:

[0025]

[0026] Furthermore, the method for predicting the uncertainties in the system is as follows:

[0027] By setting the stator current as a state variable and treating the system's uncertainty as an unknown in the state observer, the uncertainty of the system under the hyperlocal model is obtained by extending the state observer:

[0028]

[0029] Where e is the difference between the estimated and actual stator current, z 1(k) i is the estimated value of the stator current at time k. s(k) Let T be the stator current at time k. s It is the sampling time, z 2(k) This is the estimate of the uncertain part of the system under the hyperlocal model at time k, where α is a parameter independent of the system, β1 and β2 are the system gains, and u... s This is the stator voltage.

[0030] Furthermore, the control voltage of the permanent magnet synchronous motor is specifically as follows:

[0031] The control voltage for the d-axis of the permanent magnet synchronous motor is:

[0032]

[0033] Among them, i dref(k) Z represents the d-axis current output by the velocity loop at time k; 1(k+1) Z is the estimated value of the stator current at time k+1. 2(k=1) Let be the estimate of the uncertain part of the system under the hyperlocal model at time k+1, where α is a parameter independent of the system, and T is the value of the time interval. s Sampling time.

[0034] Furthermore, the estimation of the back electromotive force of the permanent magnet synchronous motor using the extended state observer specifically involves:

[0035] Based on the current model of the permanent magnet synchronous motor in the stationary coordinate system, which includes back electromotive force, the back electromotive force equation with information on rotational speed and electrical angle is obtained as follows:

[0036]

[0037] Among them, e α e is the back electromotive force along the α axis. β Let θ be the back electromotive force along the β axis. e Let ω be the electrical angle. e It is the electric angular velocity, ψ f It is a permanent magnet flux chain;

[0038] Based on the back electromotive force equation, the linear extended state observer equation is established as follows:

[0039]

[0040] Where e1 is the difference between the actual and estimated values ​​of the stator current. It is the derivative of the current estimate, L s It is the stator inductance along the d-axis. It is the derivative of the back electromotive force estimate, R is the stator resistance, z1 is the estimated current, and i s Z is the stator current, z2 is the estimated value of the back electromotive force, and u is the stator current. s β1 is the stator voltage, and β2 is the system gain;

[0041] The back electromotive force is accurately estimated by establishing a linear extended state observer through observation.

[0042] Furthermore, the step of using a phase-locked loop to extract the electric angular velocity and electric angle of the motor from the estimated back electromotive force to achieve stable sensorless control of the permanent magnet synchronous motor is as follows:

[0043] The electric angular velocity and electric angle of the motor are extracted from the estimated back electromotive force using a phase-locked loop. The electric angle deviation is corrected in real time using a PI controller so that the estimated electric angle converges to the reference angle. The estimated electric angular velocity is obtained by differentiating the estimated electric angle. The estimated electric angular velocity is connected to the speed loop and the estimated electric angle is connected to the Park transform to achieve stable sensorless control of the permanent magnet synchronous motor.

[0044] This invention also provides a sensorless PMSM model-free predictive control system based on ESO, comprising:

[0045] The discrete current model construction module is used to construct the current model of the permanent magnet synchronous motor and discretize the current model to obtain the discrete current model.

[0046] The system parameter acquisition module is used to construct a discrete first-order single-input single-output hyperlocal model based on the discrete current model, and obtain parameters and uncertainties of the system that are independent of the system.

[0047] The parameter prediction module is used to predict the uncertainties of the system and predict system-independent parameters based on the model-free controller based on the extended state observer.

[0048] The voltage control module is used to combine the predicted parameters that are independent of the system and the uncertainties of the system to obtain the control voltage of the permanent magnet synchronous motor, and to use the control voltage to control the permanent magnet synchronous motor.

[0049] The back EMF estimation module is used to estimate the back EMF of the permanent magnet synchronous motor through an extended state observer.

[0050] The electric angular velocity and electric angle control module is used to extract the electric angular velocity and electric angle of the motor from the estimated back electromotive force using a phase-locked loop, so as to achieve stable sensorless control of the permanent magnet synchronous motor.

[0051] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the described ESO-based sensorless PMSM model-free predictive control method.

[0052] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0053] This invention achieves model-free predictive current control by using an extended state observer based on a hyperlocal model. The predicted control voltage can reduce the impact of system parameter changes on the speed regulation performance of the permanent magnet synchronous motor, and improve stability and anti-interference capability. At the same time, by using the extended state observer to estimate the back EMF, the estimation accuracy of the back EMF can be effectively improved, further enhancing the anti-interference capability. Attached Figure Description

[0054] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0055] Figure 1 This is a flowchart of a method in a preferred embodiment of the present invention.

[0056] Figure 2 This is a block diagram of a model-free controller based on an extended state observer in a preferred embodiment of the present invention.

[0057] Figure 3 This is a structural block diagram of the phase-locked loop in a preferred embodiment of the present invention.

[0058] Figure 4 This is a system block diagram of a sensorless permanent magnet synchronous motor model-free control based on an extended state observer, constructed in a preferred embodiment of the present invention.

[0059] Figure 5 This is a simulation diagram of the rotational speed when the inductance and resistance parameters are 1.5 times the nominal values ​​in a simulation experiment in a preferred embodiment of the present invention.

[0060] Figure 6 This is a load torque diagram in a simulation experiment of a preferred embodiment of the present invention when the inductance and resistance parameters are at their nominal values ​​and then become 1.5 times their nominal values.

[0061] Figure 7 This is a q-axis current diagram in a simulation experiment of a preferred embodiment of the present invention, when the inductance and resistance parameters are at their nominal values ​​and then become 1.5 times their nominal values. Detailed Implementation

[0062] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0063] Reference Figure 1 As shown, in order to improve the anti-interference capability and dynamic performance of motor parameters, this invention discloses a sensorless PMSM model-free predictive control method based on Extended State Observer (ESO), which includes the following steps:

[0064] S1: Construct a current model of the permanent magnet synchronous motor under the dq axis, and discretize the current model to obtain a discrete current model.

[0065] S1-1: Stator voltage equations based on the principle of permanent magnet synchronous motors under the dq axis:

[0066]

[0067] S1-2: Transform the stator voltage equation into the stator current differential equation:

[0068]

[0069] Among them, i d i q These are the d-axis current and the q-axis current, respectively. s It's an inductor, u d u q These are the d-axis voltage and q-axis voltage, respectively, where R is the stator resistance and ω is the d-axis voltage. e It is the electric angular velocity, ψ f It is a permanent magnet flux linkage.

[0070] S1-3: To facilitate system design, the stator current differential equation is discretized, resulting in the discrete current model:

[0071]

[0072] Among them, i d(k) i represents the d-axis current sampling value at time k. q(k) T represents the q-axis current sampling value at time k. s It is the sampling time, ω e(k) U is the electric angular velocity at time k. d(k) u q(k) These are the d-axis voltage and q-axis voltage at time k, respectively. In this embodiment, the sampling time T is set. s It takes 0.1ms.

[0073] S2: Construct a discrete first-order single-input single-output hyperlocal model based on the discrete current model to obtain parameters and uncertainties that are independent of the system.

[0074] S2-1: Constructing a first-order single-input single-output hyperlocal model is as follows:

[0075]

[0076] Among them, i s For stator current, u s Let be the stator voltage, α be a non-physical scaling factor, which is a parameter independent of the system, and F be the uncertainty of the system.

[0077] S2-2: Substituting the discrete current model into the first-order single-input single-output hyperlocal model, we obtain the discrete first-order single-input single-output hyperlocal model as follows:

[0078] i s(k+1) =i s(k) +ts αu s(k) +T s F s(k) ,

[0079] Among them, i s(k) Let u be the stator current at time k. s(k) Let F be the stator voltage at time k, α be a parameter independent of the system, and F be the stator voltage at time k. s(k) Let k be the uncertain part of the system at time k.

[0080] Comparing the discrete first-order single-input single-output hyperlocal model with the discrete current model, the uncertainty of the system at time k in the d-axis stator current equation is obtained as follows:

[0081]

[0082] Where, Δ d(k) Let be the system uncertainty caused by the parameter change along the d-axis at time k.

[0083] It can be seen that the current loop control of permanent magnet synchronous motor can be simplified by using a discrete first-order single-input single-output hyperlocal model. In this way, the current at time k+1 can be accurately estimated by knowing only F and α without specific motor parameters. Therefore, by obtaining only the parameters F and α, model-free predictive current control can be designed using the hyperlocal model.

[0084] S3: Prediction does not depend on system parameters.

[0085] S3-1: To completely eliminate the influence of system parameters on the control system, the discrete difference equation method is used to derive the value of α; the difference equation for two consecutive cycles of current is constructed as follows:

[0086] Δi s(k) =i s(k) -i s(k-1) =T s (αu s(k-1) +F s(k) ),

[0087] Δi s(k-1) =i s(k-1) -i s(k-2) =T s (αu s(k-2) +F s(k-1) );

[0088] Where, Δi s(k) This is the difference between the current at time k and time k-1.

[0089] S3-2: When the sampling frequency is high enough, since the mechanical time constant is much larger than the electrical time constant, F s(k) and F s(k+1) They can be considered approximately equal, and the method for calculating parameters that do not depend on the system is as follows:

[0090]

[0091] S4: According to... Figure 2 The uncertainty of a model-free controller predicting a system based on an extended state observer is shown.

[0092] The uncertainties in the system consist of the resistance, inductance, and other uncertainties of the permanent magnet synchronous motor; therefore, by treating F as an uncertain variable, the extended state observer can observe the magnitude of F. The prediction method for F is as follows:

[0093] stator current i s The uncertainty F of the system is set as a state variable, and the uncertainty F of the system is treated as an unknown in the state observer. By extending the state observer, the uncertainty of the system under the hyperlocal model is obtained as follows:

[0094]

[0095] Where e is the difference between the estimated and actual stator current, z 1(k) Z is the estimated value of the stator current at time k. 2(k) These are estimates of the uncertainties in the system under the hyperlocal model at time k, where β1 and β2 are the system gains, and u... s This is the stator voltage.

[0096] The predictions in steps S3 and S4 are not sequential. In this embodiment, we take the example of first calculating the parameters that do not depend on the system and then predicting the uncertain parts of the system.

[0097] S5: Combining the calculated system-independent parameters and the uncertainties of the system, the control voltage of the permanent magnet synchronous motor is obtained, and the control voltage u is used. dref and u qref This enables control of the permanent magnet synchronous motor, thereby reducing the impact of system parameter variations on the speed regulation performance of the permanent magnet synchronous motor.

[0098] Taking the d-axis as an example, the control voltage for the d-axis of a permanent magnet synchronous motor is:

[0099]

[0100] Among them, i dref(k) Let d be the d-axis current output by the velocity loop at time k. In this embodiment, because i is used... d =0 control strategy, so i dref(k) =0; Z1(k+1) Z is the estimated value of the stator current at time k+1. 2(k+1) Let be the estimate of the uncertain part of the system under the hyperlocal model at time k+1, where α is a parameter independent of the system, and T is the value of the time interval. s Sampling time.

[0101] Use and calculate u dref The same method is used to obtain the q-axis control voltage u. qref .

[0102] S6: More accurate back EMF of permanent magnet synchronous motors can be estimated by using an extended state observer.

[0103] S6-1: Based on the current model of the permanent magnet synchronous motor in the stationary coordinate system, which includes back electromotive force, the back electromotive force equation with information on rotational speed and electrical angle is obtained as follows:

[0104]

[0105] Among them, e α e is the back electromotive force along the α axis. β Let θ be the back electromotive force along the β axis. e Let ω be the electrical angle. e It is the electric angular velocity, ψ f It is a permanent magnet flux linkage.

[0106] S6-2: Based on the back electromotive force equation, the linear extended state observer equation is established as follows:

[0107]

[0108] Where e1 is the difference between the actual and estimated values ​​of the stator current. It is the derivative of the current estimate, L s It is the stator inductance along the d-axis. It is the derivative of the back electromotive force estimate, R is the stator resistance, z1 is the estimated current, and i s Z is the stator current, z2 is the estimated value of the back electromotive force, and u is the stator current. s Let β be the stator voltage, and β1 and β2 be the system gain.

[0109] S6-3: Accurately estimate the back electromotive force using a linear extended state observer established through observation.

[0110] S7: Using a phase-locked loop, the electric angular velocity and electric angle of the motor are extracted from the estimated back electromotive force, so as to achieve stable sensorless control of the permanent magnet synchronous motor under the condition of changing resistance and inductance parameters.

[0111] Use such as Figure 3The phase-locked loop (PLL) extracts the electrical angular velocity and electrical angle of the motor from the estimated back electromotive force (EMF). A PI controller is used to implement closed-loop control, correcting the electrical angle deviation in real time to converge the estimated electrical angle to the reference angle. After obtaining the estimated electrical angle, the estimated electrical angular velocity is obtained by differentiating the estimated electrical angle. With the estimated electrical angular velocity and electrical angle, the estimated electrical angular velocity is connected to the speed loop, and the estimated electrical angle is connected to the Park transform, achieving stable sensorless control of the permanent magnet synchronous motor under varying resistance and inductance parameters.

[0112] For the current loop, this invention uses model-free predictive control based on a hyperlocal model instead of the traditional PI control strategy. For the unknown parameters in the hyperlocal model, an extended state observer and discrete difference equations are used to estimate the parameters required by the hyperlocal model, reducing the dependence of the permanent magnet synchronous motor control system on motor parameters. Compared with existing motor control methods, the advantages of this invention are:

[0113] 1. By using an extended state observer based on a hyperlocal model to achieve model-free predictive current control instead of the traditional PI current control strategy, the obtained control voltage u is obtained. dref with u qref This can reduce the impact of system parameter changes on the speed regulation performance of permanent magnet synchronous motors, and improve stability and anti-interference ability.

[0114] 2. In the sensorless section, the back EMF is estimated by using an extended state observer instead of a traditional sliding mode observer. This solves the problem of oscillation when estimating back EMF by the traditional sliding mode observer and reduces the parameter sensitivity problem in sensorless control. It can effectively improve the estimation accuracy of back EMF and further improve the anti-interference capability.

[0115] 3. By deriving the discrete difference equation and using the extended state observer, the parameters of the hyperlocal model can be effectively identified, reducing the dependence of the predictive control of the permanent magnet synchronous motor on the parameters.

[0116] This invention also discloses a sensorless PMSM model-free predictive control system based on ESO, comprising:

[0117] The discrete current model construction module is used to construct the current model of the permanent magnet synchronous motor and discretize the current model to obtain the discrete current model.

[0118] The system parameter acquisition module is used to construct a discrete first-order single-input single-output hyperlocal model based on the discrete current model, and obtain parameters and uncertainties of the system that are independent of the system.

[0119] The parameter prediction module is used to predict the uncertainties of the system and predict system-independent parameters based on the model-free controller based on the extended state observer.

[0120] The voltage control module is used to combine the predicted parameters that are independent of the system and the uncertainties of the system to obtain the control voltage of the permanent magnet synchronous motor, and to use the control voltage to control the permanent magnet synchronous motor.

[0121] The back EMF estimation module is used to estimate the back EMF of the permanent magnet synchronous motor through an extended state observer.

[0122] The electric angular velocity and electric angle control module is used to extract the electric angular velocity and electric angle of the motor from the estimated back electromotive force using a phase-locked loop, so as to achieve stable sensorless control of the permanent magnet synchronous motor.

[0123] The present invention also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a sensorless PMSM model-free predictive control method based on ESO.

[0124] The present invention also discloses a device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a sensorless PMSM model-free predictive control method based on ESO.

[0125] The system block diagram of the sensorless permanent magnet synchronous motor model-free control based on the extended state observer constructed in this invention is as follows: Figure 4 As shown, in this embodiment, when the permanent magnet synchronous motor is in normal operating condition, the system parameters are as follows: given speed N rref =1000rpm, number of pole pairs p=4, stator resistance R=0.6Ω, inductance L s =0.56mH, permanent magnet flux linkage ψ f =0.005753Ω.

[0126] To further demonstrate the beneficial effects of this invention, a model was built and simulation experiments were conducted in the Simulink environment in this embodiment. First, a simulation experiment was performed with the resistance and inductance parameters at their nominal values. Then, a load torque of 0.1 N·m was suddenly applied at 0.2 seconds, followed by an additional 0.2 N·m of load torque at 0.4 seconds; this made the resistance and inductance parameters 1.5 times their nominal values, and another simulation experiment was performed. The motor speed results obtained under the two sets of motor parameters are as follows: Figure 5 As shown, the motor load torque results obtained under the two sets of motor parameters are as follows: Figure 6 As shown, the q-axis current results of the motor are obtained under two sets of motor parameters. Figure 7 As shown.

[0127] from Figure 5 It can be seen that when the resistance and inductance of the motor increase to 1.5 times their nominal values, the output speed overshoot of the permanent magnet synchronous motor drive system increases to some extent. However, the system can still quickly recover to a stable state within a relatively short time, indicating that the invention has good dynamic performance. From Figure 6 It can be seen that when the motor's resistance and inductance are both increased to 1.5 times their nominal values, the system output torque can still accurately track the command value. Compared with the output torque operating based on the nominal values, the tracking accuracy is not significantly affected, and the output torque pulsation does not change significantly. From Figure 7 It can be seen that when the resistance and inductance of the motor are both increased to 1.5 times their nominal values, the current waveform is not significantly different from that at the nominal values. This proves that the present invention has good robustness in the face of changes in inductance and resistance parameters.

[0128] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0129] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0130] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0131] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0132] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An ESO-based sensorless PMSM model-free predictive control method, characterized in that, include: A current model of a permanent magnet synchronous motor is constructed, and the current model is discretized to obtain a discrete current model. Based on the discrete current model, a discrete first-order single-input single-output hyperlocal model is constructed to obtain parameters and uncertainties of the system that are independent of the system. Based on the model-free controller based on the extended state observer, the uncertain part of the system is predicted and the parameters that are independent of the system are predicted. The control voltage of the permanent magnet synchronous motor is obtained by combining the parameters that are independent of the system and the uncertain part of the system. The control voltage is used to realize the control of the permanent magnet synchronous motor. By estimating the back electromotive force of the permanent magnet synchronous motor using an extended state observer, and extracting the electric angular velocity and electric angle of the motor from the estimated back electromotive force using a phase-locked loop, stable sensorless control of the permanent magnet synchronous motor is achieved. The discrete first-order single-input single-output hyperlocal model is as follows: ， in, Let K be the stator current at time k. It is the sampling time. Let be the stator voltage at time k. To be independent of system parameters, The uncertainty of the system at time k; The prediction method that does not depend on system parameters is as follows: The difference equation for two consecutive periodic currents is constructed as follows: ， ； in, Let be the difference between the current at time k and time k-1. Sampling time, Let K be the stator current at time k. Let be the stator voltage at time k. To be independent of system parameters, The uncertainty of the system at time k; When the sampling frequency is high enough, the method for calculating parameters that are independent of the system is as follows: ； The method for predicting the uncertainty of the system is as follows: By setting the stator current as a state variable and treating the system's uncertainty as an unknown in the state observer, the uncertainty of the system under the hyperlocal model is obtained by extending the state observer: ， in, It is the difference between the estimated value and the actual value of the stator current. This is an estimated value of the stator current at time k. Let K be the stator current at time k. It is the sampling time. It is the estimate of the uncertain part of the system under the hyperlocal model at time k. To be independent of system parameters, and It is the system gain. Stator voltage; The control voltage of the permanent magnet synchronous motor is specifically: The control voltage for the d-axis of the permanent magnet synchronous motor is: ， in, Let d be the d-axis current output by the velocity loop at time k; This is the estimated value of the stator current at time k+1. This is the estimate of the uncertain part of the system under the hyperlocal model at time k+1. To be independent of system parameters, Sampling time.

2. The sensorless PMSM model-free predictive control method based on ESO according to claim 1, characterized in that: The current model for the permanent magnet synchronous motor is constructed by discretizing the current model to obtain a discrete current model, specifically as follows: The principle of permanent magnet synchronous motor is based on the stator current differential equation under the dq axis: ， in, , These are the d-axis current and the q-axis current, respectively. It's an inductor. , These are the d-axis voltage and q-axis voltage, respectively, and R is the stator resistance. It is electric angular velocity. It is a permanent magnet flux chain; Discretizing the stator current differential equation yields the discrete current model as follows: ， in, This represents the d-axis current sampling value at time k. This represents the q-axis current sampling value at time k. It is the sampling time. It is the electric angular velocity at time k. , These are the d-axis voltage and the q-axis voltage at time k, respectively.

3. The sensorless PMSM model-free predictive control method based on ESO according to claim 1, characterized in that: The estimation of the back electromotive force of the permanent magnet synchronous motor using the extended state observer specifically involves: Based on the current model of the permanent magnet synchronous motor in the stationary coordinate system, which includes back electromotive force, the back electromotive force equation with information on rotational speed and electrical angle is obtained as follows: ， in, Let be the back electromotive force along the α axis. Let be the back electromotive force along the β axis. For electrical angle, It is electric angular velocity. It is a permanent magnet flux chain; Based on the back electromotive force equation, the linear extended state observer equation is established as follows: ， in, It is the difference between the actual value and the estimated value of the stator current. It is the derivative of the current estimate. It is the stator inductance along the d-axis. It is the derivative of the back electromotive force estimate, R is the stator resistance, and in the middle It is an estimated value of the current. For stator current, It is an estimate of the back electromotive force. Stator voltage, and It is the system gain; The back electromotive force is accurately estimated by establishing a linear extended state observer through observation.

4. The sensorless PMSM model-free predictive control method based on ESO according to any one of claims 1-3, characterized in that: The method of using a phase-locked loop to extract the electric angular velocity and electric angle of the motor from the estimated back electromotive force to achieve stable sensorless control of the permanent magnet synchronous motor is as follows: The electric angular velocity and electric angle of the motor are extracted from the estimated back electromotive force using a phase-locked loop, and the electric angle deviation is corrected in real time using a PI controller so that the estimated electric angle converges to the reference angle. By differentiating the estimated electrical angle, the estimated electrical angular velocity is obtained; the estimated electrical angular velocity is connected to the speed loop, and the estimated electrical angle is connected to the Park transform, thereby realizing stable sensorless control of the permanent magnet synchronous motor.

5. A sensorless PMSM model-free predictive control system based on ESO, characterized in that, include: The discrete current model construction module is used to construct the current model of the permanent magnet synchronous motor and discretize the current model to obtain the discrete current model. The system parameter acquisition module is used to construct a discrete first-order single-input single-output hyperlocal model based on the discrete current model, and obtain parameters and uncertainties of the system that are independent of the system. The parameter prediction module is used to predict the uncertainties of the system and predict system-independent parameters based on the model-free controller based on the extended state observer. The voltage control module is used to combine the predicted parameters that are independent of the system and the uncertainties of the system to obtain the control voltage of the permanent magnet synchronous motor, and to use the control voltage to control the permanent magnet synchronous motor. The back EMF estimation module is used to estimate the back EMF of the permanent magnet synchronous motor through an extended state observer. The electric angular velocity and electric angle control module is used to extract the electric angular velocity and electric angle of the motor from the estimated back electromotive force using a phase-locked loop, so as to realize stable sensorless control of the permanent magnet synchronous motor. The discrete first-order single-input single-output hyperlocal model is as follows: ， in, Let K be the stator current at time k. It is the sampling time. Let be the stator voltage at time k. To be independent of system parameters, The uncertainty of the system at time k; The prediction method that does not depend on system parameters is as follows: The difference equation for two consecutive periodic currents is constructed as follows: ， ； in, Let be the difference between the current at time k and time k-1. Sampling time, Let K be the stator current at time k. Let be the stator voltage at time k. To be independent of system parameters, The uncertainty of the system at time k; When the sampling frequency is high enough, the method for calculating parameters that are independent of the system is as follows: ； The method for predicting the uncertainty of the system is as follows: By setting the stator current as a state variable and treating the system's uncertainty as an unknown in the state observer, the uncertainty of the system under the hyperlocal model is obtained by extending the state observer: ， in, It is the difference between the estimated value and the actual value of the stator current. This is an estimated value of the stator current at time k. Let K be the stator current at time k. It is the sampling time. It is the estimate of the uncertain part of the system under the hyperlocal model at time k. To be independent of system parameters, and It is the system gain. Stator voltage; The control voltage of the permanent magnet synchronous motor is specifically: The control voltage for the d-axis of the permanent magnet synchronous motor is: ， in, Let d be the d-axis current output by the velocity loop at time k; This is the estimated value of the stator current at time k+1. This is the estimate of the uncertain part of the system under the hyperlocal model at time k+1. To be independent of system parameters, Sampling time.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the sensorless PMSM model-free predictive control method based on any one of claims 1-4.

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