Current gradient updating method and device based on super local model and sliding mode observer

Abstract

The present invention relates to the field of current control, and in particular to a current gradient updating method and device based on a hyperlocal model and a sliding mode observer. The method and device discretize a permanent magnet synchronous motor model based on a hyperlocal model, combine the voltage vector corresponding to the inverter, obtain the current gradient corresponding to the voltage vector, use the sliding mode observer to observe the parameters involved in the hyperlocal model, and update the current gradient in combination with the hyperlocal model. The updating frequency of the current gradient is increased, thereby improving the accuracy of current prediction and the control performance of the system.

Description

Technical Field

[0001] The present invention relates to the field of current control, and in particular to a current gradient updating method and device based on a super local model and a sliding mode observer. Background Art

[0002] In order to solve the delay of the digital signal processor and improve the current tracking accuracy of the control system, the permanent magnet synchronous motor adopts a predictive current control method, which reduces the impact of the delay of the digital signal processor through two-step prediction. However, parameter mismatch and nonlinearity of the controlled object will directly affect the control system and affect the current prediction accuracy. In addition to parameter identification, another method is to adopt a model-free predictive current control method. Since model-free predictive current control requires the current change gradient corresponding to the voltage vector, the update frequency of the current gradient will directly affect the result of the current prediction. The traditional current gradient update method has problems such as slow update frequency and application of non-optimal voltage vectors. These problems will increase the error of current prediction and increase the harmonics of the current loop, thereby affecting the stability of the system output torque. Therefore, it is necessary to study and design a new current gradient update method to improve the accuracy of current prediction, increase the update frequency of the current gradient and apply the optimal voltage vector, improve the current loop tracking accuracy of the control system, and realize high-performance current loop control of permanent magnet synchronous motors. Summary of the invention

[0003] The embodiment of the present invention provides a current gradient updating method and device based on a super local model and a sliding mode observer, so as to at least solve the technical problems of low current gradient updating frequency and low current loop control accuracy in model-free predictive current control of a permanent magnet synchronous motor.

[0004] According to an embodiment of the present invention, a current gradient updating method based on a hyperlocal model and a sliding mode observer is provided, comprising the following steps:

[0005] By discretizing the permanent magnet synchronous motor model based on the hyperlocal model and combining it with the voltage vector corresponding to the inverter, the current gradient corresponding to the voltage vector is obtained;

[0006] The sliding mode observer is used to observe the parameters involved in the hyperlocal model, and the current gradient is updated in combination with the hyperlocal model.

[0007] Furthermore, the sliding mode observer is used to observe the parameters involved in the hyperlocal model, and the current gradient is updated in combination with the hyperlocal model, including:

[0008] The current gradients corresponding to the eight voltage vectors are stored in a table. The voltage vector output at time k and the cost function are combined to select the voltage vector output at time k+2. At the same time, the current gradient corresponding to the output voltage vector in the table is updated.

[0009] Furthermore, in the method, the reference current of the q-axis is obtained by the speed loop controller, the reference current of the d-axis is set to 0, and the predicted current control will output the voltage vector acting on the inverter, denoted as S a , S b , S c , to realize model-free predictive current control of permanent magnet synchronous motor.

[0010] Furthermore, the discrete model of the permanent magnet synchronous motor is:

[0011]

[0012] Among them, i d (k) and i q (k) are the current values ​​of the d and q axes in the current sampling period; u d (k) and u q (k) are the voltage values ​​of the d and q axes in the current sampling period; i d (k+1) and i q (k+1) are the current values ​​of the d and q axes in the next sampling period respectively; R s , L s are the resistance and inductance of the permanent magnet synchronous motor respectively; ω r , are the electrical angular velocity and flux linkage of the permanent magnet synchronous motor respectively; T s is the sampling period of the permanent magnet synchronous motor current loop.

[0013] Furthermore, the control formula of model-free predictive current control is as follows:

[0014]

[0015] Among them, i s (k) is the current value of the current sampling period (k); i s (k+1) is the estimated current value of the next sampling period (k+1); i s (k+2) is the current prediction value of the next sampling cycle (k+2) of the next sampling cycle; Δi s (k)| u(k) and Δi s (k+1)| u(j) are the current gradients of the current sampling period (k) and the next sampling period (k+1), and the corresponding voltage vectors are u(k) and u(j), respectively.

[0016] Furthermore, the expression of the hyperlocal model is:

[0017]

[0018] Among them, U(t) and Y(t) are the input and output of the system respectively; α is the proportional factor; F(t) is the sum of the known and unknown terms of the system, which includes multiple parts of the system.

[0019] Furthermore, the permanent magnet synchronous motor model based on the hyperlocal model is expressed as:

[0020]

[0021] in,

[0022]

[0023] Discretizing formula (4) yields:

[0024]

[0025] Among them, F d (k) and F q (k) are the estimated values ​​of the known and unknown items of the permanent magnet synchronous motor d and q axes in the current sampling period;

[0026] Based on the simplification of formula (2) and formula (5), the following current gradient update can be obtained:

[0027]

[0028] Among them, Δi d (k)| n and Δi q (k)| n are the current gradients corresponding to different voltage vectors calculated in the current sampling period; u d (k)| n and u q (k)| n They are the projections of the seven voltage vectors corresponding to the inverter in the current sampling period on the d and q axes, which can be obtained by using u a 、u b and u c Obtained by Clark and Park transform.

[0029] Furthermore, the voltage vector u(k) applied to the inverter at time k is combined with the current gradient corresponding to the voltage vector at time k calculated by formula (6);

[0030] Combined with formula (2), the current i at time k+1 is s (k+1) is estimated;

[0031] In order to solve the delay of the digital signal processor, two-step prediction is used to compensate for the one-step delay. s (k+2) makes predictions;

[0032] The current gradient is calculated by using the prediction formula for the current at time k+2 in formula (2) and formula (6), and combined with the cost function, the optimal voltage vector output by the inverter at time k+2 is selected to achieve model-free predictive current control of the permanent magnet synchronous motor; the expression of the cost function Cf is as follows:

[0033] Cf=[i dref -i d (k+2)] 2 +i qref -i q (k+2)] 2 (7)

[0034] Among them, i dref is the reference current of the d-axis, and its value is set to 0; i qref is the reference current of the q-axis, and its value is the current output by the speed loop controller.

[0035] Furthermore, in order to improve the parameter F d and F q To ensure the accuracy and speed of estimation and the anti-interference ability of parameter estimation, the sliding mode observer is designed as follows:

[0036]

[0037] in, and are the estimated values ​​of the d-axis and q-axis currents and the estimated value of the hyperlocal model parameter F, respectively; ξ is the gain coefficient of the control law; y is the function that ensures that the error between the actual current value and the observed value converges to 0, i.e., the reaching law;

[0038] The sliding surface design expression is as follows:

[0039]

[0040] Among them, e i is the error between the actual value of the d-axis and q-axis current and the observed value of the sliding mode observer; the reaching law design expression is as follows:

[0041] y=-βsign(e i ) (10)

[0042] Where, sign(·) is the sign function; β is the designed scaling factor;

[0043] According to the Lyapunov function, the parameters ξ and β are designed to realize the hyperlocal model parameter F d and F q Observation.

[0044] According to another embodiment of the present invention, a current gradient updating device based on a hyperlocal model and a sliding mode observer is provided, comprising:

[0045] A current gradient acquisition unit obtains a current gradient corresponding to a voltage vector by discretizing a permanent magnet synchronous motor model based on a super-local model and combining the voltage vector corresponding to the inverter;

[0046] The current gradient updating unit observes the parameters involved in the hyperlocal model using a sliding mode observer, and updates the current gradient in combination with the hyperlocal model.

[0047] The current gradient updating method and device based on the hyperlocal model and sliding mode observer in the embodiment of the present invention discretizes the permanent magnet synchronous motor model based on the hyperlocal model, combines the voltage vector corresponding to the inverter, obtains the current gradient corresponding to the voltage vector, uses the sliding mode observer to observe the parameters involved in the hyperlocal model, and combines the hyperlocal model to update the current gradient. The update frequency of the current gradient is improved, thereby improving the accuracy of current prediction and the control performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0049] Figure 1 The control block diagram of the permanent magnet synchronous motor with model-free predictive current control of the present invention;

[0050] Figure 2 This is a block diagram of the model-free predictive current control principle based on a super-local model and a sliding mode observer of the present invention;

[0051] Figure 3 A comparison diagram of current gradient updates corresponding to different voltage vectors of the current gradient update method proposed in the present invention and the traditional current gradient update method;

[0052] Figure 4 A comparison diagram of d-axis and q-axis current prediction errors between the current gradient updating method proposed in the present invention and the traditional current gradient updating method;

[0053] Figure 5Steady-state current tracking diagram of the motor running at 100 rpm and a load of 2 N·m under the current gradient updating method proposed in the present invention and the traditional current gradient updating method;

[0054] Figure 6 The three-phase current diagram of the motor under the working conditions of 100 rpm and 2 N·m load according to the current gradient updating method proposed in the present invention and the traditional current gradient updating method. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] Example 1

[0058] According to an embodiment of the present invention, a current gradient updating method based on a hyperlocal model and a sliding mode observer is provided, comprising the following steps:

[0059] By discretizing the permanent magnet synchronous motor model based on the hyperlocal model and combining it with the voltage vector corresponding to the inverter, the current gradient corresponding to the voltage vector is obtained;

[0060] The sliding mode observer is used to observe the parameters involved in the hyperlocal model, and the current gradient is updated in combination with the hyperlocal model.

[0061] The current gradient updating method based on the hyperlocal model and the sliding mode observer in the embodiment of the present invention discretizes the permanent magnet synchronous motor model based on the hyperlocal model, combines the voltage vector corresponding to the inverter, obtains the current gradient corresponding to the voltage vector, uses the sliding mode observer to observe the parameters involved in the hyperlocal model, and combines the hyperlocal model to update the current gradient. The update frequency of the current gradient is improved, thereby improving the accuracy of current prediction and the control performance of the system.

[0062] Among them, the sliding mode observer is used to observe the parameters involved in the hyperlocal model, and the current gradient is updated in combination with the hyperlocal model, including:

[0063] The current gradients corresponding to the eight voltage vectors are stored in a table. The voltage vector output at time k and the cost function are combined to select the voltage vector output at time k+2. At the same time, the current gradient corresponding to the output voltage vector in the table is updated.

[0064] In this method, the reference current of the q-axis is obtained by the speed loop controller, the reference current of the d-axis is set to 0, and the predicted current control will output the voltage vector acting on the inverter, which is denoted as S a , S b , S c , to realize model-free predictive current control of permanent magnet synchronous motor.

[0065] Among them, the discrete model of permanent magnet synchronous motor is:

[0066]

[0067] Among them, i d (k) and i q (k) are the current values ​​of the d and q axes in the current sampling period; u d (k) and u q (k) are the voltage values ​​of the d and q axes in the current sampling period; i d (k+1) and i q (k+1) are the current values ​​of the d and q axes in the next sampling period respectively; R s , L s are the resistance and inductance of the permanent magnet synchronous motor respectively; ω r , are the electrical angular velocity and flux linkage of the permanent magnet synchronous motor respectively; T s is the sampling period of the permanent magnet synchronous motor current loop.

[0068] Among them, the control formula of model-free predictive current control is as follows:

[0069]

[0070] Among them, i s (k) is the current value of the current sampling period (k); i s (k+1) is the estimated current value of the next sampling period (k+1); i s (k+2) is the current prediction value of the next sampling cycle (k+2) of the next sampling cycle; Δi s (k)| u(k) and Δi s (k+1)| u(j) are the current gradients of the current sampling period (k) and the next sampling period (k+1), and the corresponding voltage vectors are u(k) and u(j), respectively.

[0071] Among them, the expression of the hyperlocal model is:

[0072]

[0073] Among them, U(t) and Y(t) are the input and output of the system respectively; α is the proportional factor; F(t) is the sum of the known and unknown terms of the system, which includes multiple parts of the system.

[0074] Among them, the permanent magnet synchronous motor model based on the super local model is expressed as:

[0075]

[0076] in,

[0077]

[0078] Discretizing formula (4) yields:

[0079]

[0080] Among them, F d (k) and F q (k) are the estimated values ​​of the known and unknown items of the permanent magnet synchronous motor d and q axes in the current sampling period;

[0081] Based on the simplification of formula (2) and formula (5), the following current gradient update can be obtained:

[0082]

[0083] Among them, Δi d (k)| n and Δi q (k)| n are the current gradients corresponding to different voltage vectors calculated in the current sampling period; u d (k)| n and u q (k)|n They are the projections of the seven voltage vectors corresponding to the inverter in the current sampling period on the d and q axes, which can be obtained by using u a 、u b and u c Obtained by Clark and Park transform.

[0084] Among them, the voltage vector u(k) applied to the inverter at time k is combined with the current gradient corresponding to the voltage vector at time k calculated by formula (6);

[0085] Combined with formula (2), the current i at time k+1 is s (k+1) is estimated;

[0086] In order to solve the delay of the digital signal processor, two-step prediction is used to compensate for the one-step delay. s (k+2) makes predictions;

[0087] The current gradient is calculated by using the prediction formula for the current at time k+2 in formula (2) and formula (6), and combined with the cost function, the optimal voltage vector output by the inverter at time k+2 is selected to achieve model-free predictive current control of the permanent magnet synchronous motor; the expression of the cost function Cf is as follows:

[0088] Cf=[i dref -i d (k+2)] 2 +i qref -i q (k+2)] 2 (7)

[0089] Among them, i dref is the reference current of the d-axis, and its value is set to 0; i qref is the reference current of the q-axis, and its value is the current output by the speed loop controller.

[0090] Among them, in order to improve the parameter F d and F q To ensure the accuracy and speed of estimation and the anti-interference ability of parameter estimation, the sliding mode observer is designed as follows:

[0091]

[0092] in, and are the estimated values ​​of the d-axis and q-axis currents and the estimated value of the hyperlocal model parameter F, respectively; ξ is the gain coefficient of the control law; y is the function that ensures that the error between the actual current value and the observed value converges to 0, i.e., the reaching law;

[0093] The sliding surface design expression is as follows:

[0094]

[0095] Among them, e i is the error between the actual value of the d-axis and q-axis current and the observed value of the sliding mode observer;

[0096] The reaching law design expression is as follows:

[0097] y=-βsign(e i ) (10)

[0098] Where, sign(·) is the sign function; β is the designed scaling factor;

[0099] According to the Lyapunov function, the parameters ξ and β are designed to realize the hyperlocal model parameter F d and F q Observation.

[0100] The current gradient updating method based on the hyperlocal model and the sliding mode observer of the present invention is described in detail below with a specific embodiment:

[0101] The present invention proposes a current gradient updating method for model-free predictive current control of a permanent magnet synchronous motor, and relates to the field of current control of a permanent magnet synchronous motor. Aiming at the hysteresis problem of current gradient updating in the model-free predictive current control method, a hyperlocal model is combined with a sliding mode observer, and a current gradient updating method based on a hyperlocal model is designed. By calculating the current gradients corresponding to all voltage vectors in a single control cycle, the frequency of current gradient updating is increased. The present invention combines the advantages of a hyperlocal model and a sliding mode observer, and can not only increase the updating frequency of the current gradient, but also improve the accuracy of the prediction results and the tracking accuracy of the current loop.

[0102] The object of the present invention is to provide a current gradient updating method for model-free predictive current control of a permanent magnet synchronous motor, so as to increase the updating frequency of the current gradient corresponding to the voltage vector and improve the current tracking accuracy of the permanent magnet synchronous motor.

[0103] In the model-free predictive current control method, the current gradients corresponding to the eight voltage vectors are stored in a table, and the voltage vector output at time (k) and the cost function are combined to select the voltage vector output at time (k+2). At the same time, the current gradient corresponding to the output voltage vector in the table is updated. Since the current gradient corresponding to only one voltage vector can be updated in each control cycle, there is a problem of current gradient update lag, which affects the accuracy of the prediction result. In order to increase the update frequency of the current gradient, the present invention proposes a current gradient update method based on a super local model and a sliding mode observer. It is specifically implemented by the following technical solutions:

[0104] The control block diagram of permanent magnet synchronous motor with model-free predictive current control is shown in Figure 2. Figure 1 In this control system, the reference current of the q-axis is obtained by the speed loop controller, and the reference current of the d-axis is set to 0. Different from the traditional inverter control method based on space vector transformation, the predicted current control will output the voltage vector acting on the inverter, that is, S a , S b , S c , thereby realizing model-free predictive current control of permanent magnet synchronous motor.

[0105] The principle block diagram of the present invention based on the super local model and the sliding mode observer is as follows: Figure 2 As shown. By discretizing the permanent magnet synchronous motor model based on the hyperlocal model and combining it with the voltage vector corresponding to the inverter, the current gradient corresponding to the voltage vector is obtained. d and F q , the sliding mode observer is used to observe the parameters, combined with the hyperlocal model, to update the current gradient. Compared with the traditional update method, this method increases the update frequency of the current gradient, thereby improving the accuracy of current prediction and the control performance of the system.

[0106] The method specifically includes: firstly establishing a mathematical model of a permanent magnet synchronous motor and performing discretization processing on it. The discrete model of the permanent magnet synchronous motor is:

[0107]

[0108] Among them, i d (k) and i q (k) are the current values ​​of the d and q axes in the current sampling period; u d (k) and u q (k) are the voltage values ​​of the d and q axes in the current sampling period; i d (k+1) and i q (k+1) are the current values ​​of the d and q axes in the next sampling period respectively; R s , L s are the resistance and inductance of the permanent magnet synchronous motor respectively; ω r , are the electrical angular velocity and flux linkage of the permanent magnet synchronous motor respectively; T s is the sampling period of the permanent magnet synchronous motor current loop.

[0109] The control formula for model-free predictive current control is as follows:

[0110]

[0111] Among them, is (k) is the current value of the current sampling period (k); i s (k+1) is the estimated current value of the next sampling period (k+1); i s (k+2) is the current prediction value of the next sampling cycle (k+2) of the next sampling cycle; Δi s (k)| u(k) and Δi s (k+1)| u(j) are the current gradients of the current sampling period (k) and the next sampling period (k+1), and the corresponding voltage vectors are u(k) and u(j), which are obtained by looking up the table.

[0112] It can be seen from formula (2) that the current gradient plays a major role in model-free predictive current control. Therefore, the accuracy of the current gradient data will directly affect the predicted value of the current and the applied voltage vector, thereby directly affecting the tracking performance of the current loop of the permanent magnet synchronous motor control system.

[0113] The present invention utilizes the eight switch states corresponding to the inverter to calculate and update the corresponding current gradient in each sampling period, thereby increasing the update frequency of the current gradient. The expression of the super-local model is:

[0114]

[0115] Among them, U(t) and Y(t) are the input and output of the system respectively; α is a designable proportional factor; F(t) is the sum of the known and unknown terms of the system, which includes multiple parts of the system.

[0116] The permanent magnet synchronous motor model based on the hyperlocal model can be expressed as:

[0117]

[0118] in,

[0119]

[0120] Discretizing formula (4) yields:

[0121]

[0122] Among them, F d (k) and F q (k) are the estimated values ​​of the known and unknown items of the permanent magnet synchronous motor d and q axes in the current sampling period.

[0123] Based on the simplification of formula (2) and formula (5), the following current gradient update can be obtained:

[0124]

[0125] Among them, Δi d (k)| n and Δi q (k)| n are the current gradients corresponding to different voltage vectors calculated in the current sampling period; u d (k)| n and u q (k)| n They are the projections of the seven voltage vectors corresponding to the inverter in the current sampling period on the d and q axes, which can be obtained by using u a 、u b and u c Obtained by Clark and Park transform.

[0126] Since the inverter voltage vector u0 (000) and the voltage vector u7 (111) produce the same effect in the d and q axes, in order to reduce the switching frequency of the inverter switch, in the present invention, only seven voltage vectors u0-u6 are used, and u7 is not used for current prediction.

[0127] Since the sampling period of the current loop is T s Therefore, in adjacent sampling time intervals, it is approximately considered that the current gradient table corresponding to different voltage vectors is approximately unchanged, and can be used to predict the current at time k+2.

[0128] The voltage vector u(k) applied to the inverter at time k is combined with the current gradient corresponding to the voltage vector at time k calculated by formula (6) and formula (2) to obtain the current i at time k+1. s In order to solve the delay of the digital signal processor, a two-step prediction is used to compensate for the one-step delay, that is, the current i at time k+2 is estimated. s (k+2) is used for prediction. The prediction formula for the current at time k+2 in formula (2) and the current gradient calculated by formula (6) are used together with the cost function to select the optimal voltage vector output by the inverter at time k+2, thereby realizing model-free predictive current control of the permanent magnet synchronous motor. The expression of the cost function Cf is as follows:

[0129] Cf=[i dref -i d (k+2)] 2 +[i qref +i q (k+2)] 2 (7)

[0130] Among them, i dref is the reference current of the d-axis, and its value is set to 0; i qrefis the reference current of the q-axis, and its value is the current output by the speed loop controller.

[0131] In order to ensure the update frequency of the current gradient and the numerical accuracy of the current gradient, the parameter F in the hyperlocal model d and F q The accuracy of the estimation will directly affect the accuracy of the current gradient and the accuracy of the current estimation. d and F q In order to improve the accuracy and speed of estimation and the anti-interference ability of parameter estimation, a sliding mode observer is used to estimate the parameters. The design of the sliding mode observer is as follows:

[0132]

[0133] in, and are the estimated values ​​of the d-axis and q-axis currents and the estimated value of the hyperlocal model parameter F respectively; ξ is the gain coefficient of the control law; y is the function that ensures that the error between the actual current value and the observed value converges to 0, that is, the reaching law.

[0134] The sliding surface design expression is as follows:

[0135]

[0136] Among them, e i is the error between the actual value of the d-axis and q-axis current and the observed value of the sliding mode observer.

[0137] The reaching law design expression is as follows:

[0138] y=-βsign(e i ) (10)

[0139] Where sign(·) is the sign function and β is the designed scaling factor.

[0140] The parameters ξ and β are designed according to the Lyapunov function to ensure the stability of the sliding mode observer, thereby achieving the super-local model parameter F d and F q Observation.

[0141] By designing a current gradient updating method for model-free predictive current control based on a hyperlocal model and a sliding mode observer, compared with the traditional current gradient updating method, the present invention has at least the following advantages:

[0142] (1) The update frequency of the current gradient is improved. In the traditional method, the update frequency of the current gradient is limited by the voltage vector of the inverter. Since the inverter only outputs one voltage vector in each control cycle, the update of the current gradient is stagnant. In the present invention, the current gradient is recalculated in each sampling cycle and used for two-step prediction, avoiding the impact of the stagnation of the current gradient update on the current prediction and improving the accuracy of the current prediction.

[0143] (2) Avoid current harmonics caused by applying non-optimal voltage vectors. In traditional methods, in order to avoid the problem of stagnation in updating the current gradient, a voltage vector that has not been used for a long time is forcibly applied to the inverter. However, applying a non-optimal voltage vector will deteriorate the current tracking effect. In the present invention, since the voltage vector applied in each cycle is selected by the cost function, a non-optimal voltage vector will not be applied.

[0144] In order to verify the effect of the current gradient updating method based on the hyperlocal model and sliding mode observer proposed in the present invention, the tracking performance of the permanent magnet synchronous motor current loop is simulated and verified by using the model-free predictive current control based on the present invention. Figures 3 to 6 shown.

[0145] Figure 3 Comparison diagram of current gradient update corresponding to different voltage vectors of the proposed current gradient update method and the traditional current gradient update method. Figure 3 (1) is the traditional current gradient update method, Figure 3 (2) is the current gradient update method proposed in the present invention. For ease of comparison, the figure only shows the current gradients corresponding to the u0, u1, and u2 voltage vectors, and the current gradients corresponding to the remaining voltage vectors are similar to the results shown in the figure. By comparing the simulation results, it can be seen that since the traditional current gradient update method is based on a voltage vector applied in a control cycle, its current gradient update has a hysteresis problem, namely, the step change of the curve in 3(1). Different from the traditional update method, the update method proposed in the present invention performs calculations and updates in each cycle, which not only improves the current gradient update frequency, but also solves the update hysteresis problem and improves the current tracking performance.

[0146] Figure 4 Comparison diagram of d-axis and q-axis current prediction errors between the proposed current gradient updating method and the traditional current gradient updating method. Figure 4 (1) is the predicted current error of the traditional updating method, Figure 4 (2) is the predicted current error of the current gradient updating method proposed in the present invention. Figure 4(1) It can be seen that in the traditional method, in order to ensure the update frequency of the current gradient, a non-optimal voltage vector is applied to the inverter, which causes the current prediction error to become larger at certain moments. Compared with the traditional method, the update method used in the present invention selects the optimal voltage vector in each control cycle. Therefore, the current prediction error is relatively small and the predicted current is more stable.

[0147] Figure 5 The steady-state current tracking diagram of the proposed current gradient update method and the traditional current gradient update method when the motor is running at 100 rpm and the load is 2 N·m. Figure 5 (1) is the steady-state current of the traditional current gradient update method, Figure 5 (2) is the steady-state current of the current gradient updating method proposed in the present invention. Figure 5 (1) and Figure 5 It can be seen from the comparison of (2) that, compared with the traditional updating method, the updating method proposed in the present invention can achieve a smaller steady-state tracking error and a smoother steady-state current tracking effect, thereby improving the stability of the motor output torque.

[0148] Figure 6 Three-phase current diagram of the motor running at 100 rpm and 2 N·m load for the proposed current gradient update method and the traditional current gradient update method. Figure 6 (1) is the three-phase current diagram of the traditional updating method, Figure 6 (2) is the three-phase current diagram of the method proposed by the present invention. Figure 6 (1) and Figure 6 From the comparison of (2), it can be seen that, compared with the traditional updating method, the updating method proposed in the present invention generates a three-phase current with less harmonic content, a lower degree of distortion of the three-phase current, and a relatively better control effect of the motor.

[0149] Through simulation comparison and verification, the current gradient updating method based on the hyperlocal model and sliding mode observer proposed in the present invention can not only improve the updating frequency of the current gradient and solve the hysteresis problem of the current gradient updating, but also reduce the degree of current distortion and improve the tracking accuracy and tracking stability of the current loop, so that the system outputs a more stable torque and improves the control performance of the system.

[0150] Example 2

[0151] According to another embodiment of the present invention, a current gradient updating device based on a hyperlocal model and a sliding mode observer is provided, comprising:

[0152] A current gradient acquisition unit obtains a current gradient corresponding to a voltage vector by discretizing a permanent magnet synchronous motor model based on a super-local model and combining the voltage vector corresponding to the inverter;

[0153] The current gradient updating unit observes the parameters involved in the hyperlocal model using a sliding mode observer, and updates the current gradient in combination with the hyperlocal model.

[0154] The current gradient updating device based on the hyperlocal model and the sliding mode observer in the embodiment of the present invention discretizes the permanent magnet synchronous motor model based on the hyperlocal model, combines the voltage vector corresponding to the inverter, obtains the current gradient corresponding to the voltage vector, observes the parameters involved in the hyperlocal model using the sliding mode observer, and updates the current gradient in combination with the hyperlocal model. The updating frequency of the current gradient is improved, thereby improving the accuracy of current prediction and the control performance of the system.

[0155] The current gradient updating device based on the super local model and the sliding mode observer of the present invention is described in detail below with a specific embodiment:

[0156] The present invention proposes a current gradient updating device for model-free predictive current control of a permanent magnet synchronous motor, and relates to the field of current control of a permanent magnet synchronous motor. Aiming at the hysteresis problem of current gradient updating in the model-free predictive current control method, a hyperlocal model is combined with a sliding mode observer, and a current gradient updating device based on a hyperlocal model is designed. By calculating the current gradients corresponding to all voltage vectors in a single control cycle, the frequency of current gradient updating is increased. The present invention combines the advantages of a hyperlocal model and a sliding mode observer, and can not only increase the updating frequency of the current gradient, but also improve the accuracy of the prediction results and the tracking accuracy of the current loop.

[0157] The object of the present invention is to provide a current gradient updating device for model-free predictive current control of a permanent magnet synchronous motor, so as to increase the updating frequency of the current gradient corresponding to the voltage vector and improve the current tracking accuracy of the permanent magnet synchronous motor.

[0158] In the model-free predictive current control device, the current gradients corresponding to the eight voltage vectors are stored in a table, and the voltage vector output at time (k) and the cost function are combined to select the voltage vector output at time (k+2). At the same time, the current gradient corresponding to the output voltage vector in the table is updated. Since the current gradient corresponding to only one voltage vector can be updated in each control cycle, there is a problem of current gradient update lag, which affects the accuracy of the prediction result. In order to increase the update frequency of the current gradient, the present invention proposes a current gradient update device based on a super local model and a sliding mode observer. It is specifically implemented by the following technical solutions:

[0159] The control block diagram of permanent magnet synchronous motor with model-free predictive current control is shown in Figure 2. Figure 1In this control system, the reference current of the q-axis is obtained by the speed loop controller, and the reference current of the d-axis is set to 0. Different from the traditional inverter control method based on space vector transformation, the predicted current control will output the voltage vector acting on the inverter, that is, S a , S b , S c , thereby realizing model-free predictive current control of permanent magnet synchronous motor.

[0160] The principle block diagram of the present invention based on the super local model and the sliding mode observer is as follows: Figure 2 As shown. By discretizing the permanent magnet synchronous motor model based on the hyperlocal model and combining it with the voltage vector corresponding to the inverter, the current gradient corresponding to the voltage vector is obtained. d and F q , the sliding mode observer is used to observe the parameters, combined with the hyperlocal model, to achieve the update of the current gradient. Compared with the traditional update method, this device increases the update frequency of the current gradient, thereby improving the accuracy of current prediction and the control performance of the system.

[0161] The device specifically includes: firstly establishing a mathematical model of a permanent magnet synchronous motor and performing discretization processing on it. The discrete model of the permanent magnet synchronous motor is:

[0162]

[0163] Among them, i d (k) and i q (k) are the current values ​​of the d and q axes in the current sampling period; u d (k) and u q (k) are the voltage values ​​of the d and q axes in the current sampling period; i d (k+1) and i q (k+1) are the current values ​​of the d and q axes in the next sampling period respectively; R s , L s are the resistance and inductance of the permanent magnet synchronous motor respectively; ω r , are the electrical angular velocity and flux linkage of the permanent magnet synchronous motor respectively; T s is the sampling period of the permanent magnet synchronous motor current loop.

[0164] The control formula for model-free predictive current control is as follows:

[0165]

[0166] Among them, i s (k) is the current value of the current sampling period (k); i s(k+1) is the estimated current value of the next sampling period (k+1); i s (k+2) is the current prediction value of the next sampling cycle (k+2) of the next sampling cycle; Δi s (k)| u(k) and Δi s (k+1)| u(j) are the current gradients of the current sampling period (k) and the next sampling period (k+1), and the corresponding voltage vectors are u(k) and u(j), which are obtained by looking up the table.

[0167] It can be seen from formula (2) that the current gradient plays a major role in model-free predictive current control. Therefore, the accuracy of the current gradient data will directly affect the predicted value of the current and the applied voltage vector, thereby directly affecting the tracking performance of the current loop of the permanent magnet synchronous motor control system.

[0168] The present invention utilizes the eight switch states corresponding to the inverter to calculate and update the corresponding current gradient in each sampling period, thereby increasing the update frequency of the current gradient. The expression of the super-local model is:

[0169]

[0170] Among them, U(t) and Y(t) are the input and output of the system respectively; α is a designable proportional factor; F(t) is the sum of the known and unknown terms of the system, which includes multiple parts of the system.

[0171] The permanent magnet synchronous motor model based on the hyperlocal model can be expressed as:

[0172]

[0173] in,

[0174]

[0175] Discretizing formula (4) yields:

[0176]

[0177] Among them, F d (k) and F q (k) are the estimated values ​​of the known and unknown items of the permanent magnet synchronous motor d and q axes in the current sampling period.

[0178] Based on the simplification of formula (2) and formula (5), the following current gradient update can be obtained:

[0179]

[0180] Among them, Δi d(k)| n and Δi q (k)| n are the current gradients corresponding to different voltage vectors calculated in the current sampling period; u d (k)| n and u q (k)| n They are the projections of the seven voltage vectors corresponding to the inverter in the current sampling period on the d and q axes, which can be obtained by using u a 、u b and u c Obtained by Clark and Park transform.

[0181] Since the inverter voltage vector u0 (000) and the voltage vector u7 (111) produce the same effect in the d and q axes, in order to reduce the switching frequency of the inverter switch, in the present invention, only seven voltage vectors u0-u6 are used, and u7 is not used for current prediction.

[0182] Since the sampling period of the current loop is T s Therefore, in adjacent sampling time intervals, it is approximately considered that the current gradient table corresponding to different voltage vectors is approximately unchanged, and can be used to predict the current at time k+2.

[0183] The voltage vector u(k) applied to the inverter at time k is combined with the current gradient corresponding to the voltage vector at time k calculated by formula (6) and formula (2) to obtain the current i at time k+1. s In order to solve the delay of the digital signal processor, a two-step prediction is used to compensate for the one-step delay, that is, the current i at time k+2 is estimated. s (k+2) is used for prediction. The prediction formula for the current at time k+2 in formula (2) and the current gradient calculated by formula (6) are used together with the cost function to select the optimal voltage vector output by the inverter at time k+2, thereby realizing model-free predictive current control of the permanent magnet synchronous motor. The expression of the cost function Cf is as follows:

[0184] Cf=[i dref -i d (k+2)] 2 +[i qref -i q (k+2)] 2 (7)

[0185] Among them, i dref is the reference current of the d-axis, and its value is set to 0; i qref is the reference current of the q-axis, and its value is the current output by the speed loop controller.

[0186] In order to ensure the update frequency of the current gradient and the numerical accuracy of the current gradient, the parameter F in the hyperlocal model d and F q The accuracy of the estimation will directly affect the accuracy of the current gradient and the accuracy of the current estimation. d and F q In order to improve the accuracy and speed of estimation and the anti-interference ability of parameter estimation, a sliding mode observer is used to estimate the parameters. The design of the sliding mode observer is as follows:

[0187]

[0188] in, and are the estimated values ​​of the d-axis and q-axis currents and the estimated value of the hyperlocal model parameter F respectively; ξ is the gain coefficient of the control law; y is the function that ensures that the error between the actual current value and the observed value converges to 0, that is, the reaching law.

[0189] The sliding surface design expression is as follows:

[0190]

[0191] Among them, e i is the error between the actual value of the d-axis and q-axis current and the observed value of the sliding mode observer.

[0192] The reaching law design expression is as follows:

[0193] y=-βsign(e i ) (10)

[0194] Where sign(·) is the sign function and β is the designed scaling factor.

[0195] The parameters ξ and β are designed according to the Lyapunov function to ensure the stability of the sliding mode observer, thereby achieving the super-local model parameter F d and F q Observation.

[0196] By designing a current gradient updating device for model-free predictive current control based on a hyperlocal model and a sliding mode observer, compared with the traditional current gradient updating method, the present invention has at least the following advantages:

[0197] (1) The update frequency of the current gradient is improved. In the traditional method, the update frequency of the current gradient is limited by the voltage vector of the inverter. Since the inverter only outputs one voltage vector in each control cycle, the update of the current gradient is stagnant. In the present invention, the current gradient is recalculated in each sampling cycle and used for two-step prediction, avoiding the impact of the stagnation of the current gradient update on the current prediction and improving the accuracy of the current prediction.

[0198] (2) Avoid current harmonics caused by applying non-optimal voltage vectors. In traditional methods, in order to avoid the problem of stagnation in updating the current gradient, a voltage vector that has not been used for a long time is forcibly applied to the inverter. However, applying a non-optimal voltage vector will deteriorate the current tracking effect. In the present invention, since the voltage vector applied in each cycle is selected by the cost function, a non-optimal voltage vector will not be applied.

[0199] In order to verify the effect of the current gradient updating device based on the super local model and sliding mode observer proposed in the present invention, the tracking performance of the permanent magnet synchronous motor current loop is simulated and verified by using the model-free predictive current control based on the present invention. Figures 3 to 6 shown.

[0200] Figure 3 Comparison diagram of current gradient update corresponding to different voltage vectors of the proposed current gradient update method and the traditional current gradient update method. Figure 3 (1) is the traditional current gradient update method, Figure 3 (2) is the current gradient update method proposed in the present invention. For ease of comparison, the figure only shows the current gradients corresponding to the u0, u1, and u2 voltage vectors, and the current gradients corresponding to the remaining voltage vectors are similar to the results shown in the figure. By comparing the simulation results, it can be seen that since the traditional current gradient update method is based on a voltage vector applied in a control cycle, its current gradient update has a hysteresis problem, namely, the step change of the curve in 3(1). Different from the traditional update method, the update device proposed in the present invention performs calculations and updates in each cycle, which not only improves the current gradient update frequency, but also solves the update hysteresis problem and improves the current tracking performance.

[0201] Figure 4 Comparison diagram of d-axis and q-axis current prediction errors between the proposed current gradient updating method and the traditional current gradient updating method. Figure 4 (1) is the predicted current error of the traditional updating method, Figure 4 (2) is the predicted current error of the current gradient updating method proposed in the present invention. Figure 4(1) It can be seen that in the conventional method, in order to ensure the update frequency of the current gradient, a non-optimal voltage vector is applied to the inverter, which causes the current prediction error to become larger at certain moments. Compared with the conventional method, the updating device used in the present invention selects the optimal voltage vector in each control cycle, so the current prediction error is relatively small and the predicted current is more stable.

[0202] Figure 5 The steady-state current tracking diagram of the proposed current gradient update method and the traditional current gradient update method when the motor is running at 100 rpm and the load is 2 N·m. Figure 5 (1) is the steady-state current of the traditional current gradient update method, Figure 5 (2) is the steady-state current of the current gradient updating method proposed in the present invention. Figure 5 (1) and Figure 5 It can be seen from the comparison of (2) that, compared with the traditional updating method, the updating device proposed in the present invention can achieve a smaller steady-state tracking error and a smoother steady-state current tracking effect, thereby improving the stability of the motor output torque.

[0203] Figure 6 Three-phase current diagram of the motor running at 100 rpm and 2 N·m load for the proposed current gradient update method and the traditional current gradient update method. Figure 6 (1) is the three-phase current diagram of the traditional updating method, Figure 6 (2) is the three-phase current diagram of the method proposed by the present invention. Figure 6 (1) and Figure 6 From the comparison of (2), it can be seen that, compared with the traditional updating method, the three-phase current generated by the updating device proposed in the present invention has less harmonic content, lower distortion of the three-phase current, and relatively better control effect of the motor.

[0204] Through simulation comparison and verification, the current gradient updating device based on the hyperlocal model and sliding mode observer proposed in the present invention can not only improve the updating frequency of the current gradient and solve the hysteresis problem of the current gradient updating, but also reduce the degree of current distortion and improve the tracking accuracy and tracking stability of the current loop, so that the system outputs a more stable torque and improves the control performance of the system.

[0205] Example 3

[0206] A storage medium stores a program file capable of implementing any one of the above-mentioned current gradient updating methods based on a super-local model and a sliding mode observer.

[0207] Example 4

[0208] A processor is used to run a program, wherein when the program is run, any one of the current gradient updating methods based on a super-local model and a sliding mode observer is executed.

[0209] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0210] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0211] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0212] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0213] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0214] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0215] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A current gradient updating method based on a hyperlocal model and a sliding mode observer, characterized in that: The following steps are involved: By discretizing the permanent magnet synchronous motor model based on the hyperlocal model and combining it with the voltage vector corresponding to the inverter, the current gradient corresponding to the voltage vector is obtained; The sliding mode observer is used to observe the parameters involved in the hyperlocal model, and the current gradient is updated in combination with the hyperlocal model; The discrete model of permanent magnet synchronous motor is: Among them, i d (k) and i q (k) are the current sampling period d The current value of the u axis and q axis; d (k) and u q (k) are the voltage values ​​of the d-axis and q-axis in the current sampling period; i d (k+1) and i q (k+1) are the current values ​​of the d-axis and q-axis in the next sampling period respectively; R s , L s are the resistance and inductance of the permanent magnet synchronous motor respectively; ω r , are the electrical angular velocity and flux linkage of the permanent magnet synchronous motor respectively; T s is the sampling period of the permanent magnet synchronous motor current loop; The expression of the hyperlocal model is: Among them, U(t) and Y(t) are the input and output of the system respectively; α is the proportional factor; F(t) is the sum of the known and unknown terms of the system, including multiple parts of the system; The permanent magnet synchronous motor model based on the hyperlocal model is expressed as: in, Discretizing formula (4) yields: Among them, F d (k) and F q (k) are the estimated values ​​of the known items and unknown items of the permanent magnet synchronous motor d-axis and q-axis in the current sampling period; Based on the control formula of model-free predictive current control and formula (5), the following current gradient update can be obtained by simplifying: Among them, Δi d (k)| n and Δi q (k)| n are the current gradients corresponding to different voltage vectors calculated in the current sampling period; u d (k)| n and u q (k)| n They are the projections of the seven voltage vectors corresponding to the inverter in the current sampling period on the d-axis and q-axis, which can be obtained by using u a 、u b and u c Obtained by Clark and Park transform.

2. The current gradient updating method based on the hyperlocal model and sliding mode observer according to claim 1, characterized in that: The method of using a sliding mode observer to observe the parameters involved in the hyperlocal model and updating the current gradient in combination with the hyperlocal model includes: The current gradients corresponding to the eight voltage vectors are stored in a table. The voltage vector output at time k and the cost function are combined to select the voltage vector output at time k+2. At the same time, the current gradient corresponding to the output voltage vector in the table is updated.

3. The current gradient updating method based on the hyperlocal model and sliding mode observer according to claim 2 is characterized in that: In the method, the reference current of the q-axis is obtained by the speed loop controller, the reference current of the d-axis is set to 0, and the predicted current control will output the voltage vector acting on the inverter, which is denoted as S a , S b , S c , to realize model-free predictive current control of permanent magnet synchronous motor.

4. The current gradient updating method based on the hyperlocal model and sliding mode observer according to claim 3 is characterized in that: The control formula for model-free predictive current control is as follows: Among them, i s (k) is the current value of the current sampling period (k); i s (k+1) is the estimated current value of the next sampling period (k+1); i s (k+2) is the current prediction value of the next sampling cycle (k+2) of the next sampling cycle; Δi s (k)| u(k) and Δi s (k+1)| u(j) are the current gradients of the current sampling period (k) and the next sampling period (k+1), and the corresponding voltage vectors are u(k) and u(j), respectively.

5. The current gradient updating method based on the hyperlocal model and sliding mode observer according to claim 4, characterized in that: The voltage vector u(k) applied to the inverter at time k is combined with the current gradient corresponding to the voltage vector at time k calculated by formula (6); Combined with formula (2), the current i at time k+1 is s (k+1) is estimated; In order to solve the delay of the digital signal processor, two-step prediction is used to compensate for the one-step delay. s (k+2) makes predictions; The current gradient is calculated by using the prediction formula for the current at time k+2 in formula (2) and formula (6), and the voltage vector output by the inverter at time k+2 is selected in combination with the cost function, thereby realizing the model-free predictive current control of the permanent magnet synchronous motor; the expression Cf of the cost function Cost Function is as follows: Cf=[i dref -i d (k+2)] 2 +[i qref -i q (k+2)] 2 (7) Among them, i dref is the reference current of the d-axis, and its value is set to 0; i qref is the reference current of the q-axis, and its value is the current output by the speed loop controller.

6. The current gradient updating method based on the hyperlocal model and sliding mode observer according to claim 5, characterized in that: In order to improve the parameter F d and F q To ensure the accuracy and speed of estimation and the anti-interference ability of parameter estimation, the sliding mode observer is designed as follows: in, and are the estimated values ​​of the d-axis and q-axis currents and the estimated value of the hyperlocal model parameter F, respectively; ξ is the gain coefficient of the control law; y is the function that ensures that the error between the actual current value and the observed value converges to 0, that is, the reaching law; The sliding surface design expression is as follows: Among them, e i is the error between the actual value of the d-axis and q-axis current and the observed value of the sliding mode observer; The reaching law design expression is as follows: y=-βsign(e i ) (10) Where, sign(·) is the sign function; β is the designed scaling factor; According to the Lyapunov function, the parameters ξ and β are designed to realize the hyperlocal model parameter F d and F q Observation.

7. A current gradient updating device based on a hyperlocal model and a sliding mode observer, characterized in that: The current gradient updating method based on the super local model and the sliding mode observer as claimed in any one of claims 1 to 6 is adopted, and the updating device comprises: A current gradient acquisition unit obtains a current gradient corresponding to a voltage vector by discretizing a permanent magnet synchronous motor model based on a super-local model and combining the voltage vector corresponding to the inverter; The current gradient updating unit observes the parameters involved in the hyperlocal model using a sliding mode observer, and updates the current gradient in combination with the hyperlocal model.

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