A non-recursive optimization-based permanent magnet synchronous motor speed control method and system

Abstract

The application relates to a permanent magnet synchronous motor speed control method and system based on non-recursive optimization, which comprises the following steps: collecting the speed and d-q axis current of the permanent magnet synchronous motor; first, constructing a non-smooth disturbance estimator by using the speed deviation and the d-axis current deviation, accurately estimating and compensating the matched disturbance and the unmatched disturbance in the system; second, reconstructing the system model according to the non-smooth disturbance estimation, designing a speed regulation compound controller based on non-recursive optimization, and the controller gain can be online self-adaptively adjusted according to different working conditions. Compared with the prior art, the application improves the dynamic response speed, anti-interference ability and adaptive ability of the permanent magnet synchronous motor speed regulation system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of permanent magnet synchronous motor, and particularly relates to a permanent magnet synchronous motor speed control method and system based on non-recursive optimization. BACKGROUND

[0002] Permanent magnet synchronous motor has a very wide application in high performance demand occasions such as electric vehicle field, rail transit field, robot field, medical machinery field and the like due to its fast response speed, large starting torque, high power density, small size and the like. Speed control is an extremely important part of servo control, although there are many mature and reliable speed control methods at present, but with the increasing performance requirements of some industrial occasions on the motor, the traditional method based on cascade control can no longer meet the needs of industry. The reason is that according to different industrial occasions, various forms of interference will exist in the running process of the motor, thereby affecting the dynamic performance and steady-state performance of the motor speed tracking, such as load disturbance, model parameter drift, unmodeled dynamics and the like. For speed regulation system, these disturbances can be divided into two categories, i.e. matching disturbance and non-matching disturbance, so that the control performance is affected during speed control, and even the system becomes unstable when it is serious, thereby possibly causing the failure of the actuator. At present, in view of the above problems of permanent magnet synchronous motor disturbance suppression, the suppression measures taken are as follows:

[0003] 1. The iterative learning method is adopted by defining cost function or weight function to suppress the total disturbance existing in the system loop, such as the periodic disturbance double-loop prediction suppression method of permanent magnet synchronous motor disclosed in the publication No. CN109617484A and the literature "Permanent Magnet Linear Synchronous Motor Robust Iterative Learning Control Based on Smith Prediction and Performance Weighting Function" (Zhao Ximei et al., Transactions of Electrical Engineering Technology, 2016, 31(19)). This kind of method has a good effect in processing low frequency periodic or non-periodic disturbance, and does not need accurate system model. But the suppression ability for time-varying non-periodic disturbance is poor, and the calculation complexity of this kind of method is large, so the response speed of the system is not high.

[0004] 2. To address various disturbances in the system, a composite control method combining a disturbance observer or state observer with a controller is used to suppress disturbances in the system. For example, the invention disclosed in CN107241034A, a method for suppressing speed fluctuations in a permanent magnet synchronous motor, and the literature "Design of an Active Disturbance Rejection Controller for a Permanent Magnet Synchronous Motor Speed ​​Loop Based on a Variable Gain Extended State Observer" (Wang Jianliang et al., Control Theory and Applications, 2018, 45(11)). First, this type of method has a good effect on dealing with unmatched disturbances, i.e., disturbances on the input channel. However, in a motor speed system, there are various disturbances, such as external load disturbances, which are unmatched disturbances. This type of method can only equate unmatched disturbances to matched disturbances for estimation and compensation. This approach of treating matched and unmatched disturbances as a total disturbance and estimating them can only obtain a rough estimate of the disturbance. To a certain extent, it will increase the burden on the observer, making the convergence speed of the observer disturbance slower and unable to obtain precise information about the specific disturbance, resulting in low accuracy.

[0005] Furthermore, the aforementioned methods all employ a cascade control design with separate current and speed loops in their speed control design. While this design is structurally easy to understand and implement, its dynamic performance is lower compared to non-cascade control because the outer loop response is typically slower than the inner loop response. Secondly, most current methods are based on composite controllers with disturbance feedforward compensation. The selection of controller parameters relies on human experience and does not adaptively optimize the controller output based on the system model and the actual system conditions after disturbance elimination, thus failing to achieve adaptive control and improve system dynamic performance and adaptability. Therefore, designing a non-recursive speed controller capable of suppressing various disturbances and achieving adaptive dynamic optimization remains a challenging problem under current technologies and methods. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a non-recursive optimization-based speed control method and system for permanent magnet synchronous motors that improves anti-interference capability.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A speed control method for a permanent magnet synchronous motor based on non-recursive optimization includes: acquiring the measured angle and measured speed of the permanent magnet synchronous motor rotor, and comparing the measured speed with a preset given speed to obtain a speed deviation; sampling the current of the permanent magnet synchronous motor to obtain the q-axis measured current and the d-axis measured current, and comparing the d-axis measured current with a preset d-axis given current to obtain a d-axis current deviation; estimating non-smooth disturbances based on the speed deviation and the d-axis current deviation, and then performing composite control of the permanent magnet synchronous motor rotor based on disturbance estimation.

[0009] Further, the calculation expression of the control input of the compound control based on the disturbance estimation is:

[0010]

[0011] wherein Ω6 is the control input of the compound control based on the disturbance estimation, is the q-axis voltage of the compound control input in the d-q coordinate system, is the d-axis voltage of the compound control input in the d-q coordinate system, J is the rotational inertia, s is the stator inductance, n p is the number of motor magnetic poles, ψ f is the rotor flux, K 11 ,K 12 ,K 21 is the controller gain coefficient, T1 and T2 are respectively the adaptive update values containing G1 and G2, G1 and G2 are the transformation gain coefficients, the compound control system state δ = [δ1 δ2 δ3] T is the steady-state value of the compound control input ω ref is the given speed, the system state e ω is the speed deviation, i q is the q-axis measured current, B is the viscous friction coefficient,

[0012] Further, by constructing the non-smooth disturbance estimations Σ1, Σ2 and Σ3, the state estimation value of the system is obtained and the disturbance estimation value of the system is obtained so as to obtain the steady-state value of the system state and the steady-state value of the input and further obtain the system state δ = [δ1 δ2 δ3] T ,

[0013] Further, the calculation expression of the non-smooth disturbance estimations Σ1, Σ2 and Σ3 is:

[0014]

[0015] wherein, is the known nonlinear term of the system, ω ref is the given speed, i dref is the d-axis given current, l i,j , λ i (i = {1, 2, 3}, j = {0, 1, 2}) are all gain parameters of the estimator; Φ i,0= x i ; P 1,0 , P 1,1 , P 1,2 are the estimates of x1, d1, , respectively, denoted as P 2,0 , P 2,1 are the estimates of x2, d2, respectively, denoted as P 3,0 , P 3,1 are the estimates of x3, d3, respectively, denoted as

[0016] Further, the gain parameter λ i , l i,j is used to adjust the convergence rate of the interference estimator and the suppression ability of the time-varying interference, satisfying the constraint condition λ i > 0, l i,j > 0 (i = {1, 2, 3}, j = {0, 1, 2}).

[0017] Further, the gain parameter K 11 , K 12 , K 21 of the non-recursive optimization control is used to adjust the bandwidth of the compound control, satisfying K 11 > 0, K 12 > 0, K 21 > 0.

[0018] Further, the parameters T i (i = 1, 2) introduce a secondary adaptive dynamic updating mechanism:

[0019]

[0020] wherein G1(0) = G2(0) = 1, T 10 and T 20 are the initial values of the dynamic updating mechanism, ρ i and are the parameters of the updating mechanism (i = 1, 2), G max 1 , G max 2 are the saturation clipping thresholds of G1, G2, respectively.

[0021] Further, the dynamic optimization initial parameters T 10 , T 20 are used to adjust the initial bandwidth of the system, satisfying T 10 > 0, T 20 > 0; the secondary updating mechanism parameters ρ i are used to adjust the updating rate, satisfying ρ i> 0;

[0022] G max i for preventing G i The growth of the controller is too large to cause the system to be unstable, and the bandwidth of the controller is too large to satisfy G max i > 0 (i = 1, 2).

[0023] The application also provides a control system based on the non-recursive optimization-based permanent magnet synchronous motor speed control method, comprising a non-recursive compound controller, a three-phase bridge inverter circuit, an angle sensor and a motor, the angle sensor is installed on the rotor of the permanent magnet synchronous motor and is used to collect the measured angle θ and the measured speed ω, the non-recursive compound controller comprises a non-recursive optimization controller and a non-smooth disturbance estimator connected with each other, the non-smooth disturbance estimator is used to perform the non-smooth disturbance estimation, the non-recursive optimization controller is used to perform the compound control based on the disturbance estimation, the control input of the non-recursive optimization controller is the measured angle θ and the measured speed ω, the control output of the non-recursive optimization controller is the given speed ω, the given d-axis current i and the given q-axis current i, the control input of the non-smooth disturbance estimator is the measured angle θ, the measured speed ω, the given speed ω, the given d-axis current i, the given q-axis current i, the measured d-axis current i and the measured q-axis current i, and the control output of the non-smooth disturbance estimator is the given d-axis current i and the given q-axis current i. After Park inversion and SVPWM processing, the PWM wave is generated and transmitted to the three-phase bridge inverter circuit, the motor is connected with the three-phase bridge inverter circuit, the motor is also connected with an ADC for current sampling, and the q-axis measured current i and the d-axis measured current i are obtained through Clarke transformation and Park transformation. q d .

[0024] Further, under the condition that the control input of the given system is , the control system is asymptotically convergent, that is, e ω is the speed deviation, e id is the d-axis current deviation.

[0025] Compared with the prior art, the application has the following advantages:

[0026] (1) The application uses the controller output to give the speed ω ref , the speed deviation e ω , the q-axis current i q , the given d-axis current i dref and the d-axis current deviation e id to construct the non-smooth disturbance estimator, the states and disturbances in the system are reconstructed, the matched and non-matched disturbances in the system are processed respectively, and the anti-interference ability of the system is improved.

[0027] (2) The application is based on the non-recursive optimization control single closed loop design method, compared with the iterative learning method, the controller structure is simple, the multiple types of disturbances in the system are well suppressed, and the robustness of the system is improved.

[0028] ​(3) Compared with the existing cascade double closed loop speed control design strategy, the single closed loop design strategy based on the non-recursive optimal control of the application has higher bandwidth, and improves the dynamic response speed and anti-interference ability of the system.

[0029] (4) The non-recursive optimal control single closed loop design strategy provided by the application, compared with the composite controller based on disturbance estimation which is mostly used at present, the selection of control parameters no longer relies on artificial experience, but is dynamically updated through the actual system itself, and the adaptive ability of the system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The system block diagram of a non-recursive composite controller provided in the embodiment of the application is shown in the figure;

[0031] Figure 2 The speed tracking effect comparison chart of the application and the traditional cascade PI controller under constant disturbance is shown in the figure;

[0032] Figure 3 The speed tracking effect comparison chart of the application and the traditional cascade PI controller under sinusoidal disturbance is shown in the figure;

[0033] Figure 4 The d-axis current comparison chart of the application and the traditional cascade PI controller under constant disturbance is shown in the figure;

[0034] Figure 5 The d-axis current comparison chart of the application and the traditional cascade PI controller under sinusoidal disturbance is shown in the figure;

[0035] Figure 6 The q-axis current curve comparison chart of the application and the traditional cascade PI controller under constant disturbance is shown in the figure;

[0036] Figure 7 The q-axis current curve comparison chart of the application and the traditional cascade PI controller under sinusoidal disturbance is shown in the figure. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. The components of the embodiments of the application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0038] The following detailed description of embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the application as claimed, but merely represents selected embodiments of the application. Based upon the embodiments in the application, all other embodiments that a person of ordinary skill in the art obtains without creative work under the premise are within the scope of protection of the application.

[0039] It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0040] Embodiment 1

[0041] The embodiment provides a non-recursive optimization-based permanent magnet synchronous motor speed control method, comprising: obtaining a measured angle and a measured speed of a rotor of a permanent magnet synchronous motor, and comparing the measured speed with a preset given speed to obtain a speed deviation; sampling a current of the permanent magnet synchronous motor to obtain a q-axis measured current and a d-axis measured current, comparing the d-axis measured current with a preset d-axis given current to obtain a d-axis current deviation; performing non-smooth disturbance estimation according to the speed deviation and the d-axis current deviation, and then performing compound control of the rotor of the permanent magnet synchronous motor based on disturbance estimation.

[0042] The embodiment also provides a control system based on the non-recursive optimization-based permanent magnet synchronous motor speed control method, comprising a non-recursive compound controller, a three-phase bridge inverter circuit, an angle sensor and a motor, the angle sensor is installed on the rotor of the permanent magnet synchronous motor and used to collect a measured angle θ and a measured speed ω, the non-recursive compound controller comprises a non-recursive optimization controller and a non-smooth disturbance estimator connected with each other, the non-smooth disturbance estimator is used to perform disturbance estimation on matching and non-matching disturbances in the system, the non-recursive optimization controller is used to perform compound control based on disturbance estimation, control inputs of the non-recursive optimization controller are the measured angle θ and the measured speed ω, and the control output of the non-recursive optimization controller is a control signal of the motor. After Park inverse transformation and SVPWM processing, a PWM wave is generated and transmitted to the three-phase bridge inverter circuit, the motor is connected with the three-phase bridge inverter circuit, the motor is also connected with an ADC for current sampling, and q-axis measured current i q and d-axis measured current i d are obtained through Clarke transformation and Park transformation. q . d .

[0043] Under the condition that the control input of the given system is , the control system is asymptotically convergent, that is, e ω is the speed deviation, e id is the d-axis current deviation.

[0044] The specific construction process of the above scheme is as follows:

[0045] By collecting the speed and d-axis current of the permanent magnet synchronous motor, a non-smooth disturbance estimator is first constructed to accurately estimate and compensate the matched disturbance and non-matched disturbance in the system within a limited time, and a non-recursive control optimization controller is designed according to the system model, and the design block diagram is as shown in Figure 1 .

[0046] In specific examples, the electrical parameters of the permanent magnet synchronous motor selected here are shown in Table 1:

[0047] Table 1 Electrical parameters of permanent magnet synchronous motor

[0048]

[0049]

[0050] The specific steps of the controller design are as follows:

[0051] Step 1): Obtain the measured angle θ and the measured speed ω by the angle sensor installed on the rotor of the permanent magnet synchronous motor; compare with the given speed ω ref , to obtain the speed error e ω = ω ref - ω; sample the current through hardware ADC, and obtain the q-axis measured current i q and the d-axis measured current i d through Clarke transformation and Park transformation; compare the d-axis measured current i d with the d-axis given current i dref , to obtain the d-axis current error e id = i dref - i d ; establish the mathematical model Ω1 of the PMSM in the d-q coordinate system:

[0052]

[0053] Among them, B is the viscous friction coefficient, J is the moment of inertia, ψ f is the rotor flux, n p is the number of motor magnetic poles, R s is the stator resistance, L s is the stator inductance, T l is the load torque, u d , u q are the d-axis and q-axis voltages in the d-q coordinate system.

[0054] The system state is selected The system input is selected Substitute the mathematical model Ω1 to carry out algebraic operation, and get the error state space model Ω2 of the system:

[0055]

[0056] Wherein

[0057] d1, d2, d3 are the system matching disturbances, including unmodeled dynamics and model parameter drift, etc. is the known nonlinear term of the system,

[0058] Step 2): According to the error state space model Ω2 obtained in step 1), select a virtual control law

[0059]

[0060] Get a new system state space model Ω3:

[0061]

[0062] Step 3): The system state space model Ω3 obtained in step 2) is decomposed into three subsystems, and three non-smooth disturbance estimators are constructed to reconstruct the system disturbance, which are

[0063]

[0064] Wherein l i,j , λ i (i={1, 2, 3}, j={0, 1, 2}) are the gain parameters of the estimator; Φ i,0 =x i ; P 1,0 , P 1,1 , P 1,2 are the estimates of x1, d1, , respectively, denoted as P 2,0 , P 2,1 are the estimates of x2, d2, respectively, denoted as P 3,0 , P 3,1 are the estimates of x3, d3, respectively, denoted as By constructing the non-smooth disturbance estimator Σ1-Σ3, the state estimation value of the system and the disturbance estimation value of the system

[0065] Step 4): Obtain the steady-state value of the system state and input steady state value

[0066]

[0067] Step 5): State steady state value x obtained by step 4) * and input steady state value Make the following transformation Σ4:

[0068]

[0069] Combined with the state space model Ω3 in step 2) and the transformation Σ4, a new state space model Ω4 can be obtained:

[0070]

[0071] Where the estimation error Define the system state δ = [δ1 δ2 δ3] T , the system input η = [η1 η2] T .

[0072] And set the estimation error ξ i to 0, the state space model Ω4 can be further simplified to a compact model Ω5:

[0073] Step 6): Make mathematical transformation according to the state model Ω4 obtained in step 5): The state model Ω4 can be changed to Ω5:

[0074]

[0075] Where G1, G2 are transformation gain coefficients, by selecting appropriate G1, G2, the known dynamic term Can be ignored, the system becomes a standard series integral system;

[0076] Step 7): Combined with the model Ω5 obtained in step 6), design Where K 11 , K 12 , K 21 are controller gain coefficients, T1, T2 are adaptive update values containing G1, G2 respectively; According to the transformation Σ4 in step 5), the control input of the system is:

[0077] Step 8): When the permanent magnet synchronous motor operating condition is switched, combined with the system control input form obtained in step 7), a secondary adaptive dynamic update mechanism is introduced for parameters T i (i = 1, 2):

[0078]

[0079] Where G1(0)=G2(0)=1, T 10 and T 20 ρ is the initial value for the dynamic update mechanism. i and For the parameters of the update mechanism (i = 1, 2), G max 1 G max 2 These are the saturation limiting thresholds for G1 and G2, respectively.

[0080] Step 9): According to the definition in Step 1). Defined in step 2) Defined in step 4) and defined in step 8) The final control input Ω6 of the system is obtained as follows:

[0081]

[0082] Furthermore, the control parameter adjustment rules involved in the non-recursive optimization controller of this scheme are as follows:

[0083] 1. Regarding the gain parameter λ of the non-smooth disturbance estimator i l i,j The constraint λ needs to be satisfied. i >0, l i,j If the value is greater than 0, the parameter l should generally be adjusted first. i,j These are usually small positive numbers, and then the gain parameter λ is adjusted. i , λ i Generally, the parameter is a large positive number, and the larger the parameter, the faster the estimate converges and the stronger the ability to estimate time-varying disturbances. However, the estimation accuracy will also decrease, and the initial disturbance error will be large, which may damage the dynamic performance of the system. Generally, this parameter needs to be weighed according to the actual application (i = {1, 2, 3}, j = {0, 1, 2}). At the same time, the accuracy of the non-smooth disturbance estimator is positively correlated with the control frequency of the controller.

[0084] 2. Regarding the controller gain parameter K i,j The selection principle must first satisfy constraint K. i,j >0. Adjusting this parameter is similar to the pole configuration of state feedback. Generally, the larger the parameter, the further left the system poles are, and the higher the system bandwidth. However, excessively large parameters can amplify measurement noise, thus affecting the system's dynamic performance and stability. Therefore, a trade-off needs to be made based on the actual application requirements of the system. Regarding the controller gain parameter T... i0 The selection principle must first satisfy constraint T. i0> 0, which is the initial value of dynamic optimization, is used to adjust the initial bandwidth of the system, and is generally a small positive number. Generally, in a local interval T i0 The smaller the system response speed becomes faster, and the adjustment of the parameter beyond this local interval, the system change effect begins to be not obvious, which needs to be debugged according to the actual system needs. For the selection rule of the parameter of the quadratic update mechanism , it needs to meet The larger the parameter is, the larger the update rate is, and generally a positive number less than 1 can be set. The parameter ρ i needs to meet ρ i > 0, and the parameter has a similar effect, and generally the parameter is adjusted first, and then the parameter ρ i is fine-tuned. The parameter G max i needs to meet G max i > 0, which is used to adjust the maximum bandwidth upper limit of the dynamic optimization controller, to avoid that the controller bandwidth is too large to cause the system to be unstable in actual application (i = {1, 2}, j = {1, 2}), and generally it is selected according to the actual system and application occasion needs.

[0085] Here, two permanent magnet synchronous motor working conditions are set to illustrate the effectiveness of the scheme:

[0086] 1) The given step speed given signal ω ref = 1500 rpm, and the disturbance torque is set as d ω = 0.3 + 0.15 (t = 1s) ;

[0087] 2) The given step speed given signal ω ref = 1500 rpm, and the disturbance torque is set as d ω = 0.3sin (πt + 1.5π) + 0.15 (t = 1s).

[0088] According to the parameter selection rule, combined with the permanent magnet brushless direct current motor selected in this example, the control parameter of the application is set as:

[0089]

[0090] At the same time, in order to compare the effectiveness of the control scheme, the PI double closed loop controller design scheme widely used at present is compared, and the PI controllers of the two loops are designed as:

[0091] 1) The speed loop PI controller, i dref = 0

[0092] 2) The current loop PI controller

[0093] The control parameter is selected as:

[0094]

[0095] Figures 2-7 The speed tracking curves, q-axis current response curves and d-axis current response curves of the non-recursive optimization controller and the traditional cascade PI controller under constant load disturbance and sinusoidal load disturbance are respectively given. It can be obviously seen from the experimental results that the non-recursive optimization control design method provided by the application can well inhibit different disturbances in the system. Especially, it can be seen from the speed output curve of the sinusoidal load disturbance that the non-recursive optimization control method provided by the application has a significant improvement compared with the traditional cascade PI controller, and the control gain is dynamically updated according to the system model and the system itself, the controller parameter adaptive dynamic updating is realized, and the dynamic response and robustness of the servo system are improved.

[0096] The above describes the preferred embodiments of the application in detail. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology according to the concept of the application should be within the protection scope determined by the claims.

Claims

1. A speed control method for a permanent magnet synchronous motor based on non-recursive optimization, characterized in that, include: The measurement angle and speed of the permanent magnet synchronous motor rotor are obtained, and the measurement speed is compared with the preset given speed to obtain the speed deviation; The permanent magnet synchronous motor is sampled to obtain the q-axis measured current and the d-axis measured current. The d-axis measured current is compared with the preset d-axis given current to obtain the d-axis current deviation. Non-smooth disturbance estimation is performed based on the speed deviation and the d-axis current deviation, and then composite control based on disturbance estimation is performed on the rotor of the permanent magnet synchronous motor. The calculation expression for the control input of the composite control based on disturbance estimation is as follows: ： In the formula, For the control input of composite control based on disturbance estimation, The q-axis voltage in the dq coordinate system is the composite control input. The voltage along the d-axis in the dq coordinate system is the composite control input. For rotational inertia, For stator inductance, This represents the number of pole pairs of the motor. For rotor flux linkage, The controller gain coefficient. To contain respectively Adaptive update value, To change the gain coefficient, the state of the composite control system Composite control input steady-state value , , For a given speed, the system state , For speed deviation, For measuring current along the q-axis, The coefficient of viscous friction is... , , This represents the d-axis current deviation.

2. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 1, characterized in that, By constructing nonsmooth disturbance estimation , and The system state estimate is obtained. and system interference estimates Thus, the steady-state value of the system can be obtained. and input steady-state value Thus, the system state is obtained. , .

3. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 2, characterized in that, The non-smooth interference estimation , and The calculation expression is: ： ： ： In the formula, , , , , ; Given the known nonlinear terms of the system, , , , , , ; For a given speed, Given a current along the d-axis, , ( {1, 2, 3}, {0, 1, 2} are all gain parameters of the estimator; ; , , They are respectively , , The estimate is denoted as , , ; , They are respectively , The estimate is denoted as , ; , They are respectively , The estimate is denoted as , .

4. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 3, characterized in that, The gain parameter , This is used to adjust the convergence rate of the interference estimator and its ability to suppress time-varying interference, while satisfying the constraints. ( {1, 2, 3}, {0, 1, 2}).

5. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 1, characterized in that, The gain parameter of the non-recursive optimization control The bandwidth used to adjust the composite control to meet the requirements. .

6. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 1, characterized in that, parameter ( 1, 2) Introduce a quadratic adaptive dynamic update mechanism: ， In the formula, , and The initial value is for the dynamic update mechanism. and For the parameters of the update mechanism ( 1, 2). , They are respectively The saturation limiting threshold.

7. The method for controlling the speed of a permanent magnet synchronous motor based on non-recursive optimization according to claim 6, characterized in that, Dynamically optimize initial parameters Used to adjust the initial bandwidth of the system to meet the requirements. ; Secondary update mechanism parameters Used to adjust the update rate, to meet... ; Used to prevent Excessive growth leads to excessive controller bandwidth, causing system instability. ( 1, 2).

8. A control system based on a non-recursive optimization-based speed control method for permanent magnet synchronous motors as described in any one of claims 1-7, characterized in that, It includes a non-recursive composite controller, a three-phase bridge inverter circuit, an angle sensor, and a motor. The angle sensor is mounted on the rotor of the permanent magnet synchronous motor and is used to collect and measure the angle. and measuring speed The non-recursive composite controller includes interconnected non-recursive optimization controllers and non-smooth disturbance estimators. The non-smooth disturbance estimators are used to perform the non-smooth disturbance estimation, and the non-recursive optimization controllers are used to perform the composite control based on the disturbance estimation. The control input of the non-recursive optimization controllers... After Park inverse transform and SVPWM processing, a PWM wave is generated and transmitted to a three-phase bridge inverter circuit. The motor is connected to the three-phase bridge inverter circuit. The motor is also connected to an ADC for current sampling, and the q-axis measured current is obtained after Clark transform and Park transform. and d-axis current measurement .

9. The control system according to claim 8, characterized in that, In the control input of a given system, i.e. The control system described below is asymptotically convergent, that is, it has... , For speed deviation, This represents the d-axis current deviation.

Images