Novel model-free predictive current control method and system

Abstract

The invention provides a novel model-free predictive current control method and system, belongs to the field of model-free predictive current control of an IPMSM, and provides a variable-speed double-power reaching law (VSDPRL) for solving the contradiction between rapid convergence and buffeting suppression of a traditional exponential reaching law (ERL). And then parameter deviation and unmodeled dynamics are uniformly regarded as lumped disturbance, dq-axis current and lumped disturbance are used as observation variables, a sliding-mode observer based on a variable-speed double-power reaching law is provided, and the steady-state performance of the motor running under parameter disturbance is improved to a certain extent. Furthermore, in order to enhance the robustness of the DPCC, a first-order hyper-local model is established, and a variable-speed sliding-mode observer combining the hyper-local model and a variable-speed double-power reaching law VSDPRL is designed, so that the steady-state performance of the motor under parameter disturbance is further improved, and the control robustness of the IPMSM system during parameter mismatch is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of motor control technology and is a model-free predictive current control method of IPMSM, specifically involving a novel model-free predictive current control method and system. Background Technology

[0002] Deadbeat Predictive Current Control (DPCC), with its rapid dynamic response and flexible control structure, has become a research hotspot in high-performance control of permanent magnet synchronous motors. However, this control strategy relies on an accurate mathematical model of the controlled object, and its control performance will significantly deteriorate in actual operation if motor parameter mismatch occurs. Therefore, how to effectively improve the robustness of DPCC to parameter changes has become a highly concerned topic in this field. Currently, common methods for improving the parameter robustness of DPCC can be mainly summarized into three categories: parameter identification, model-free predictive control, and compensation strategies based on disturbance observers.

[0003] Among these methods, parameter identification identifies and corrects motor parameters online or offline to address the inconsistency between actual parameters and preset nominal parameters in the controller. However, in practical applications, it is susceptible to interference from unmodeled disturbances during motor operation, and the algorithm itself is often complex, exhibiting certain limitations. Model-Free Predictive Control (MFPC) is a data-driven control strategy that does not rely on a precise mathematical model of the controlled object. Instead, it directly constructs a predictive model of the system using real-time or historical data, thereby achieving multi-objective optimization control. Although MFPC avoids dependence on traditional motor parameters and improves the system's robustness to parameter changes, it requires storing and processing large amounts of voltage and current data, resulting in a large computational load and placing high demands on the controller's hardware performance. The disturbance observer compensation method treats various disturbances such as parameter mismatch and model uncertainty as lumped disturbances, estimating them in real time by designing an observer and introducing feedforward compensation to eliminate or weaken their impact on system performance. Commonly used disturbance observers include the Romberg observer, the Extended State Observer (ESO), and the Sliding Mode Observer. Sliding mode observers (SMOs), as a nonlinear state estimation method based on sliding mode variable structure theory, are widely used in disturbance compensation due to their strong robustness to parameter perturbations and external disturbances. However, traditional sliding mode control often struggles to achieve a good balance between convergence speed and chatter suppression. Summary of the Invention

[0004] This invention aims to address the technical challenge of achieving a good balance between convergence speed and chatter suppression in traditional sliding mode control. To this end, it provides a novel model-free predictive current control method and system. This method integrates disturbance observation compensation and sliding mode control concepts, proposing a sliding mode observer based on the Variable Speed ​​Double Power Reaching Law (VSDPRL). The dq-axis current and lumped disturbance are used as observed variables, and their estimated values ​​are obtained through the VSDPRL-based sliding mode observer. Furthermore, the proposed VSDPRL effectively improves the contradiction between fast convergence and chatter suppression in traditional exponential reaching laws (ERL). Applying the VSDPRL-based sliding mode observer to lumped disturbance estimation can also improve the steady-state performance of the motor under parameter disturbances.

[0005] Therefore, the technical solution provided by the present invention is as follows:

[0006] On the one hand, the present invention provides a novel model-free predictive current control method, applied to an integrated permanent magnet synchronous motor (IPMSM), which is executed in each sampling period:

[0007] Step 1: Real-time acquisition of dq-axis current and dq-axis voltage of permanent magnet synchronous motor;

[0008] Step 2: Input the collected dq-axis current and dq-axis voltage into the sliding mode observer based on the variable speed double power-law VSDPRL to obtain the estimated values ​​of the dq-axis current and lumped disturbance at the next moment;

[0009] Step 3: Based on the estimated dq-axis current and lumped disturbance values ​​at the next time step, introduce a combination of deadbeat current prediction control and one-beat delay compensation to back-calculate the required dq-axis voltage reference vector for the current time step.

[0010] Step 4: Based on the current dq axis voltage reference vector, use SVPWM modulation to generate the corresponding pulse width modulation signal to drive the motor.

[0011] Optionally, the variable speed double power-reaching law VSDPRL mentioned in step 2 is:

[0012]

[0013] In the formula, s is the sliding mode function. is the first derivative of the sliding mode function s, k1, k2, and α are positive real numbers; k1 is the constant-velocity gain, k2 is the power-law gain, and α is the power-law adjustment term; x is the system state variable, i.e., the error between the observed current and the reference current along the dq axis; t is time. Let be the reaching law coefficient of the sliding mode function s.

[0014] Optionally, the values ​​of parameters k1, k2, and α satisfy: 1≤k2 / k1≤2, 0.5≤α<1, and the initial position s0 of the sliding mode function s≥42.

[0015] Optionally, a hyperlocal model is introduced. In step 2, the sliding mode observer based on the variable-speed double-power-reaching law VSDPRL is a variable-speed sliding mode observer that combines the hyperlocal model and the variable-speed double-power-reaching law VSDPRL. The corresponding discrete prediction equation is:

[0016]

[0017] In the formula, k represents the k-th sampling time, and T s The sampling period of the control system, This represents the estimated dq-axis current at the (k+1)th sampling time. and Let represent the dq-axis current and dq-axis voltage at the k-th sampling time, respectively. This represents the estimated value of the stator inductance along the dq axis. and Let represent the lumped disturbance estimate at the k-th sampling time and the (k+1)-th sampling time. Let be the sliding mode control function for the dq axis at the k-th sampling time, and β<0 represent the error feedback coefficient.

[0018] Optionally, a sliding surface s is constructed based on the dq-axis current estimation error and an integral sliding surface is adopted, such that at any sampling time, the dq-axis sliding control function... for:

[0019]

[0020] Among them, the dq-axis current estimation error , These represent the estimated and actual values ​​of the dq-axis current, respectively, and the corresponding dq-axis current estimation error. sliding mode function Characterized as: , where c is the integral sliding surface parameter. Optionally, the lumped disturbance includes resistance, inductance, and flux linkage.

[0021] Secondly, the present invention provides a control system based on the above method for controlling an embedded permanent magnet synchronous motor (IPMSM), the control system comprising:

[0022] The data acquisition module is used to collect the dq-axis current and dq-axis voltage of the permanent magnet synchronous motor in real time.

[0023] A sliding mode observer is used to observe the estimated values ​​of the dq-axis current and lumped disturbance at the next moment by using the collected dq-axis current and dq-axis voltage.

[0024] The reference voltage calculation module is used to back-calculate the required dq-axis voltage reference vector at the current moment based on the estimated dq-axis current and lumped disturbance values ​​at the next moment, by combining deadbeat current prediction control and one-beat delay compensation.

[0025] The SVPWM modulation module is used to generate corresponding pulse width modulation signals to drive the motor based on the current dq axis voltage reference vector.

[0026] In three aspects, the present invention provides a motor system, which includes the above-mentioned control system and an embedded permanent magnet synchronous motor (IPMSM). The control system is connected to the embedded permanent magnet synchronous motor (IPMSM) to control the operation of the embedded permanent magnet synchronous motor (IPMSM).

[0027] In four aspects, the present invention provides an experimental platform system, including:

[0028] An oscilloscope is used to record the speed, three-phase current, and dq-axis current of an integrated permanent magnet synchronous motor (IPMSM).

[0029] The dSPACE system is used to convert the MATLAB / Simulink modeled motor control model into C code and upload it to the dSPACE control box, thereby generating an SVPWM wave and sending it to the inverter. The motor control model is used to implement steps 1-4 of the above method.

[0030] Load loader, used to test the loading and unloading performance of built-in permanent magnet synchronous motor (IPMSM);

[0031] An inverter connected to the dSPACE system, which receives SVPWM waves from the dSPACE control box and drives the built-in permanent magnet synchronous motor IPMSM to operate.

[0032] The permanent magnet synchronous motor is connected to the load, inverter, and load loader.

[0033] In five aspects, the present invention provides a computer-readable storage medium storing a computer program that is invoked by a processor to implement:

[0034] The steps of the novel model-free predictive current control method described above.

[0035] Compared with the prior art, the present invention achieves the following beneficial effects:

[0036] This invention proposes a variable-speed double-power approach law, which, compared to the traditional sliding mode approach law using the exponential approach law (ERL), enables the system to converge quickly while reducing chattering. Specifically, firstly, the proposed approach law allows the system state to converge within a finite time, unaffected by the initial value s0, and this convergence speed is faster than that of the exponential approach law (ERL). Secondly, the proposed variable-speed double-power approach law exhibits stronger chattering suppression capabilities than the ERL method. Therefore, this invention restricts the sliding mode observer to use the variable-speed double-power approach law, enabling the system to resolve the contradiction between rapid convergence and chattering suppression in the traditional exponential approach law (ERL).

[0037] The sliding mode observer proposed in this invention, based on the variable-speed double power-reaching law VSDPRL, treats the dq-axis current and lumped disturbance as observed variables. Estimates are obtained through observation using the VSDPRL-based sliding mode observer, which can improve the steady-state performance of the motor under parameter disturbances. In particular, this invention further establishes a first-order hyperlocal model and preferably uses a variable-speed sliding mode observer (VSSMO) combining the hyperlocal model and VSDPRL. Experimental comparisons with traditional DPCC and a sliding mode observer-enhanced DPCC (SMO-DPCC) further verify that the proposed VSSMO-DPCC method can effectively improve the steady-state performance of the motor under parameter disturbances and significantly enhance the control robustness of the IPMSM system under parameter mismatch. Attached Figure Description

[0038] Figure 1 This is a block diagram of a VSSMO-DPCC control system provided in an embodiment of the present invention;

[0039] Figure 2 This is a control block diagram of a variable speed sliding mode observer (VSSMO) provided in an embodiment of the present invention;

[0040] Figure 3 It is a comparison diagram of two convergence rate phase trajectories;

[0041] Figure 4 This is a graph showing the amplitude of VSDPRL jitter.

[0042] Figure 5 This is a schematic diagram of the experimental platform. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0044] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0046] This invention provides a novel model-free predictive current control method and system. It proposes applying a variable-speed double-power-law (VSDPRL) to the sliding mode observer, improving the contradiction between rapid convergence and chatter suppression in the traditional exponential-power-law (ERL). To enhance the robustness of DPCC, parameter deviations and unmodeled dynamics are uniformly treated as lumped disturbances, establishing a first-order hyperlocal model. This leads to the construction of a sliding mode observer combining the hyperlocal model and the VSDPRL, effectively improving the steady-state performance of the motor under parameter disturbances and significantly enhancing the control robustness of the IPMSM system under parameter mismatch. To enable those skilled in the art to better understand the technical solution of this invention, a further detailed description is provided below with reference to the accompanying drawings.

[0047] This invention provides a novel model-free predictive current control method applied to an integrated permanent magnet synchronous motor (IPMSM). Its core lies in proposing a variable-speed double power-law reaching law (VSDPRL), and then constructing a sliding mode observer based on VSDPRL, effectively improving the contradiction between rapid convergence and chatter suppression in the traditional exponential reaching law (ERL). Secondly, the sliding mode observer is further optimized by selecting a variable-speed sliding mode observer combining a hyperlocal model and VSDPRL to estimate lumped disturbances in the hyperlocal model, improving control accuracy and effectively enhancing the motor's steady-state performance under parameter disturbances.

[0048] Therefore, the technical concept of this invention is to perform the following steps in each sampling period:

[0049] Step 1: Real-time acquisition of dq-axis current and dq-axis voltage of permanent magnet synchronous motor;

[0050] Step 2: Input the collected dq-axis current and dq-axis voltage into the sliding mode observer based on the variable speed double power-law VSDPRL to obtain the estimated values ​​of the dq-axis current and lumped disturbance at the next moment;

[0051] Step 3: Based on the estimated dq-axis current and lumped disturbance values ​​at the next time step, introduce a combination of deadbeat current prediction control and one-beat delay compensation to back-calculate the required dq-axis voltage reference vector for the current time step.

[0052] Step 4: Based on the current dq axis voltage reference vector, use SVPWM modulation to generate the corresponding pulse width modulation signal to drive the motor.

[0053] It should be understood that the core of the technical concept of this invention is to apply the variable speed double power-law VSDPRL in the sliding mode observer. The observation model of the sliding mode observer is not limited. Any sliding mode observation model suitable for observing the estimated values ​​of dq-axis current and lumped disturbances meets the requirements of the technical solution of this invention.

[0054] Regarding the variable-speed double-power-law approaching law VSDPR:

[0055] The traditional sliding mode reaching law uses the exponential reaching law (ERL), which is expressed as: In the formula, , Let be the reaching law coefficient, and s be the sliding mode function. By integration, the reaching time of the sliding surface can be obtained as: k1 is the constant velocity gain, and k2 is the power gain.

[0056] As shown above, the convergence time of the system depends on the initial state s(0) and the convergence rate coefficient k. In sliding mode control, increasing the gain k1 of the constant velocity term can shorten the time to reach the sliding surface and enhance the system's anti-interference capability. However, when there is a large error between the initial state and the desired state, if the approaching law is still driven by a fixed gain to approach the sliding surface without adaptive adjustment based on the system state, it is easy to cause significant chattering and steady-state deviation. On the other hand, although increasing the gain k2 of the power term also helps to shorten the convergence time to approach the sliding surface, without proper constraints, it may also lead to excessively large control signal amplitudes, which will adversely affect actual execution.

[0057] To compensate for the shortcomings of the exponential reaching law and enable the system to converge quickly while reducing chattering, this invention proposes a Variable Speed ​​Double Power Reaching Law (VSDPRL):

[0058]

[0059] In the formula, s is the sliding mode function. is the first derivative of the sliding mode function s, k1, k2, and α are positive real numbers; k1 is the constant-velocity gain, k2 is the power-law gain, and α is the power-law adjustment term; x is the system state variable, i.e., the error between the observed current and the reference current along the dq axis; t is time. Let be the reaching law coefficient of the sliding mode function s.

[0060] Compared to the exponential reaching law ERL, the variable speed double power reaching law VSDPRL proposed in this invention introduces a power function in the constant speed reaching term. Introducing the system state variable x into the constant velocity term allows the sliding mode velocity to be correlated with the system state, enabling the approach process to adaptively adjust to changes in the system state; introducing a secondary variable function into the exponential term... This can accelerate the convergence speed.

[0061] If the sliding mode function s is large (far from s=0), i.e. |s|>1, then λ(s)=2. , This means that the proposed variable-speed double-power convergence rate (VSDPRL) has a faster convergence speed than ERL in this stage. As |s| decreases, the variable-power term and the exponential term converge to... and .

[0062] If the sliding mode function s is close to the sliding surface (s=0), i.e., |s|<1, λ(s)=1-α, then there exists and index part The approach velocity is approximately zero. The constant velocity coefficient... It decreases as the system state x decreases, greatly reducing chattering.

[0063] Figure 3 The figure shows the phase trajectory of the proposed reaching rate. For ease of comparison, let |x| = |s|. As can be seen from the figure, when s is far from the equilibrium point, the variable-speed double-power reaching rate VSDPRL with system state variable x has the steepest slope, followed by the VSDPRL without system state variable x, while the exponential reaching rate ERL has the gentlest slope. This indicates that the convergence speed of the variable-speed double-power reaching rate VSDPRL is faster than that of the exponential reaching rate ERL when far from the equilibrium point. When s is close to the equilibrium point, the slope of the VSDPRL with system state variable x gradually flattens until it is less than that of the exponential reaching rate ERL, indicating that the variable-speed double-power reaching rate VSDPRL can reduce chattering. Therefore, compared with the exponential reaching rate ERL, the variable-speed double-power reaching rate VSDPRL has a faster convergence speed and less system chattering.

[0064] The variable-speed double-power-law approaching-rate (VSDPRL) method proposed in this invention enables the system to achieve finite-time... Inside, forcing the initial position Upon reaching the sliding surface, the convergence time is: .

[0065] when At that time, convergence time It is a constant, that is, it exists: .

[0066] In the formula, the parameter .

[0067] when At that time, there is a convergence time. < ( In the parameter selection, in order to make the system have a faster convergence speed and less chattering, the value of k2 is generally greater than or equal to k1. Therefore, the preferred values ​​of parameters k1, k2, and α satisfy: 1≤k2 / k1≤2, 0.5≤α<1, and the initial position of the sliding function s is s0≥42.

[0068] Figure 4 The image shows a comparison of the jitter amplitudes of the variable double power approach rate (VSDPRL) and the exponential approach rate (ERL). Figure 4 In the diagram, the yellow area represents the switching band of the method proposed in this invention, and the gray area represents the switching band of the exponential reaching rate (ERL). As can be seen from the figure, the variable speed double power reaching rate (VSDPRL) method proposed in this invention can effectively reduce system chattering.

[0069] In summary, the variable-speed double-power reaching law VSDPRL has the following advantages: 1) The proposed reaching law can make the system state converge in a finite time, and the convergence time is not affected by the initial value s0. Moreover, the convergence speed is faster than the exponential reaching law ERL method; 2) The proposed VSDPRL method has stronger chattering suppression capability than the exponential reaching law ERL method.

[0070] It should be understood that in the embodiments of the present invention, the sliding mode observer in step 2 can achieve the above-mentioned effect by using the variable speed double power-reaching law VSDPRL. Therefore, in some embodiments, under the constraint of the variable speed double power-reaching law VSDPRL, the observation model of the sliding mode observer is not limited, and any model that can predict the dq axis current estimate and the lumped disturbance estimate is applicable. In some embodiments, in order to further improve the steady-state performance of the motor under parameter disturbances and significantly improve the control robustness of the IPMSM system under parameter mismatch, the preferred sliding mode observer is the variable speed sliding mode observer VSSMO, which combines the hyperlocal model and VSDPRL, and it is introduced into deadbeat predictive current control (DPCC) to form the VSSMO-DPCC method.

[0071] In actual operation, motor parameters often change with external environmental conditions such as temperature and core saturation. If the motor parameters used in the prediction equation deviate from the actual parameters, it will negatively impact the performance of DPCC (Direct Predictive Current Control). The following section will describe the impact of parameter mismatch on DPCC by constructing mathematical expressions and using three-dimensional graphs.

[0072] The discretized current prediction model under parameter mismatch can be expressed as follows:

[0073]

[0074] In the formula, , They are respectively under parameter mismatch d, q Components of the predicted shaft current. , , , These represent the estimated motor parameters used in the controller, corresponding to the estimated values ​​of stator resistance, q-axis stator inductance, d-axis stator inductance, and flux linkage. , , , This represents the difference between the estimated motor parameters and the actual motor parameters; , , , These are the actual values ​​of stator resistance, q-axis stator inductance, d-axis stator inductance, and flux linkage, respectively. Expressed as the electric angular velocity of the motor, i d i q Represented as dq-axis current, u d u q Represented as dq-axis voltage, k represents the sampling time, and T s This is the sampling period of the control system.

[0075] Therefore, when parameter mismatch occurs, the prediction errors for the d-axis and q-axis are as follows:

[0076]

[0077] As can be seen from the formula, any mismatch in parameters will cause the actual current to fail to track the reference current, thus leading to prediction errors.

[0078] To further improve the steady-state performance of the motor under parameter disturbances, the first-order hyperlocal model selected in this embodiment of the invention is as follows:

[0079]

[0080] In the formula, G is the derivative of the perturbation, which is assumed to be time-varying. F dq F represents the lumped disturbance along the dq axis (considering parameter deviations and unmodeled dynamics as lumped disturbances). dq The expression is:

[0081]

[0082] In this embodiment, the lumped disturbance includes resistance, inductance, and flux linkage; therefore, accurate estimation of the lumped disturbance will achieve robustness to the parameters of inductance, resistance, and flux linkage. To this end, this invention proposes a variable-speed sliding mode observer to estimate the lumped disturbance F in the hyperlocal model. dq To improve control accuracy, a variable speed sliding mode observer (VSSMO) combining a hyperlocal model and VSDPRL is used.

[0083] This invention selects i dq F dq As observed variables, a VSSMO based on a hyperlocal model is established:

[0084]

[0085] In the formula, β<0 represents the error feedback coefficient. For F dq The estimated value, For i dq The estimated value, Represented as the dq-axis sliding mode control function, the error equation is:

[0086]

[0087] In the formula, These represent the dq-axis current estimation error and the disturbance estimation error, respectively.

[0088] According to sliding mode control theory, sliding mode control design mainly includes two key aspects: sliding surface design and sliding control law design. By properly designing these two parts, it can be ensured that the system state stably converges to the sliding surface within a finite time. This embodiment uses an integral sliding surface, which is specifically defined as follows: c>0, where c is the integral sliding surface parameter.

[0089] Combining the variable-speed double-power approach law, , will eF dq As a disturbance to the control function, the final sliding mode control function design is as follows:

[0090]

[0091] Furthermore, it can be deduced that the discrete prediction equation corresponding to VSSMO is:

[0092]

[0093] In the formula, k represents the k-th sampling time, and T s The sampling period of the control system, This represents the estimated dq-axis current at the (k+1)th sampling time. and Let represent the dq-axis current and dq-axis voltage at the k-th sampling time, respectively. This represents the estimated value of the stator inductance along the dq axis. and Let represent the lumped disturbance estimate at the k-th sampling time and the (k+1)-th sampling time. Let be the sliding mode control function for the dq axis at the k-th sampling time, and β<0 represent the error feedback coefficient.

[0094] Thus, by using the discrete prediction equation of VSSMO mentioned above, the estimated values ​​of the dq-axis current and lumped disturbance at the next moment can be accurately obtained using the collected dq-axis current and dq-axis voltage.

[0095] In some embodiments, step 3 incorporates the theoretical concept of deadbeat current prediction control, ensuring that the current at the next moment keeps pace with the current setpoint. Combined with one-beat delay compensation, the required output voltage reference vector at the current moment can be deduced.

[0096] .

[0097] In the formula, The voltage reference vector along the dq axis. Reference current for the dq axis

[0098] In summary, as shown above, Figure 1 In this embodiment, the sampled voltage and current of the dq axis at time k are first applied to the discrete prediction equation of VSSMO to obtain the estimated value of the dq axis current and the lumped disturbance at time k+1. Then, combined with the deadbeat control concept and the one-step delay compensation method, the obtained estimated value is applied to the voltage reference vector formula to calculate the reference value of the dq axis voltage. Finally, SVPWM modulation is used to generate the corresponding pulse width modulation signal to drive the motor.

[0099] In some embodiments, the present invention provides a control system based on the above method for controlling an embedded permanent magnet synchronous motor (IPMSM). The control system includes at least a data acquisition module, a sliding mode observer, a reference voltage calculation module, and an SVPWM modulation module that are connected in sequence or interconnected.

[0100] The system includes: a data acquisition module for real-time acquisition of the dq-axis current and dq-axis voltage of the permanent magnet synchronous motor; a sliding mode observer for using the acquired dq-axis current and dq-axis voltage to observe and obtain the estimated values ​​of the dq-axis current and lumped disturbance at the next moment; a reference voltage calculation module for back-calculating the required dq-axis voltage reference vector at the current moment based on the estimated values ​​of the dq-axis current and lumped disturbance at the next moment, incorporating deadbeat current predictive control and one-beat delay compensation; and an SVPWM modulation module for generating a corresponding pulse width modulation signal to drive the motor based on the dq-axis voltage reference vector at the current moment using SVPWM modulation.

[0101] It should also be understood that the specific implementation process of each module is described in the above method. This invention will not repeat it here. The above division of functional modules is only for illustrative purposes. In some embodiments, some functional modules can be combined and some functional modules can be separated. Each functional module can be implemented in software, hardware, or a combination of software and hardware. The software and hardware devices include, but are not limited to, general-purpose computer equipment, programmable gate arrays, digital signal processors, microprocessors and their corresponding programming or burning software.

[0102] In some embodiments, the present invention provides a motor system comprising the above-described control system and an embedded permanent magnet synchronous motor (IPMSM). The control system is connected to the embedded permanent magnet synchronous motor (IPMSM) for controlling the operation of the embedded permanent magnet synchronous motor (IPMSM).

[0103] like Figure 5 As shown, in some embodiments, the present invention provides an experimental platform system based on the above method. This experimental platform system includes: an oscilloscope, a dSPACE system, a load loader, an inverter, a permanent magnet synchronous motor (IPMSM), and a load. The oscilloscope is used to record the speed, three-phase current, and dq-axis current of the built-in IPMSM. The dSPACE system is used to convert the MATLAB / Simulink-modeled motor control model into C code and upload it to the dSPACE control box, thereby generating an SVPWM wave and sending it to the inverter. This motor control model is used to implement steps 1-4. The load loader is used to test the load loading and unloading performance of the built-in IPMSM. The inverter receives the SVPWM wave from the dSPACE control box and drives the built-in IPMSM. The permanent magnet synchronous motor and load are connected to the inverter and the load loader.

[0104] In some embodiments, the present invention provides a computer-readable storage medium storing a computer program that is invoked by a processor to implement the steps of the novel model-free predictive current control method described above.

[0105] Specific implementation:

[0106] Step 1: Real-time acquisition of dq-axis current and dq-axis voltage of permanent magnet synchronous motor;

[0107] Step 2: Input the collected dq-axis current and dq-axis voltage into the sliding mode observer based on the variable speed double power-law VSDPRL to obtain the estimated values ​​of the dq-axis current and lumped disturbance at the next moment.

[0108] Step 3: Based on the estimated dq-axis current and lumped disturbance values ​​at the next time step, introduce a combination of deadbeat current prediction control and one-beat delay compensation to back-calculate the required dq-axis voltage reference vector for the current time step.

[0109] Step 4: Based on the current dq axis voltage reference vector, use SVPWM modulation to generate the corresponding pulse width modulation signal to drive the motor.

[0110] For details on the implementation of each step, please refer to the description in the aforementioned control method embodiment.

[0111] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the hardware and software device described in any of the foregoing embodiments, such as the hard drive or memory of the controller. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both internal storage units and external storage devices of the controller. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0112] 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application refers to flowchart illustrations and / or instructions executed by a processor of a method, apparatus (system), and computer program product according to embodiments of this application to create means for implementing the functions specified in one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowchart illustrations and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of a block diagram.

[0114] It should be emphasized that the examples described in this invention are illustrative rather than limiting. Therefore, this invention is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solutions of this invention, without departing from the spirit and scope of this invention, whether modifications or substitutions, are also within the protection scope of this invention.

Claims

1. A novel model-free predictive current control method, applied to an embedded permanent magnet synchronous motor (IPMSM), characterized in that: Execute in each sampling period: Step 1: Real-time acquisition of dq-axis current and dq-axis voltage of permanent magnet synchronous motor; Step 2: Input the collected dq-axis current and dq-axis voltage into the sliding mode observer based on the variable speed double power-law VSDPRL to obtain the estimated values ​​of the dq-axis current and lumped disturbance at the next moment. Step 3: Based on the estimated dq-axis current and lumped disturbance values ​​at the next time step, introduce a combination of deadbeat current prediction control and one-beat delay compensation to back-calculate the required dq-axis voltage reference vector for the current time step. Step 4: Based on the current dq axis voltage reference vector, use SVPWM modulation to generate the corresponding pulse width modulation signal to drive the motor.

2. The method according to claim 1, characterized in that: The variable speed double power-reaching law VSDPRL mentioned in step 2 is: ； In the formula, s is the sliding mode function. is the first derivative of the sliding mode function s; k1 is the constant velocity gain, k2 is the power gain, α is the power function adjustment term, and k1, k2, and α are positive real numbers; x is the system state variable, i.e., the error between the observed current and the reference current along the dq axis; t is time. Let be the reaching law coefficient of the sliding mode function s.

3. The method according to claim 2, characterized in that: The values ​​of parameters k1, k2, and α satisfy: 1≤k2 / k1≤2, 0.5≤α<1, and the initial position of the sliding mode function s is s0≥42.

4. The method according to claim 2, characterized in that: Introducing the hyperlocal model, the sliding mode observer based on the variable-speed double-power-reaching law VSDPRL in step 2 is a variable-speed sliding mode observer that combines the hyperlocal model and the variable-speed double-power-reaching law VSDPRL. The corresponding discrete prediction equation is: ； In the formula, k represents the k-th sampling time, and T s The sampling period of the control system, This represents the estimated dq-axis current at the (k+1)th sampling time. and Let represent the dq-axis current and dq-axis voltage at the k-th sampling time, respectively. This represents the estimated value of the stator inductance along the dq axis. and Let represent the lumped disturbance estimate at the k-th sampling time and the (k+1)-th sampling time. Let be the sliding mode control function for the dq axis at the k-th sampling time, and β<0 represent the error feedback coefficient.

5. The method according to claim 4, characterized in that: Based on the dq-axis current estimation error, a sliding mode function s is constructed and an integral sliding surface is adopted, so that at any sampling time, the dq-axis sliding mode control function... for: ； Among them, the dq-axis current estimation error , These represent the estimated and actual values ​​of the dq-axis current, respectively, and the corresponding dq-axis current estimation error. sliding mode function Characterized as: c represents the integral sliding surface parameter.

6. The method according to claim 1, characterized in that: Lumped disturbances include resistance, inductance, and magnetic flux.

7. A control system based on the method of any one of claims 1-6, characterized in that: The control system for controlling an integrated permanent magnet synchronous motor (IPMSM) includes: The data acquisition module is used to collect the dq-axis current and dq-axis voltage of the permanent magnet synchronous motor in real time. A sliding mode observer is used to observe the estimated values ​​of the dq-axis current and lumped disturbance at the next moment by using the collected dq-axis current and dq-axis voltage. The reference voltage calculation module is used to back-calculate the required dq-axis voltage reference vector at the current moment based on the estimated dq-axis current and lumped disturbance values ​​at the next moment, by combining deadbeat current prediction control and one-beat delay compensation. The SVPWM modulation module is used to generate corresponding pulse width modulation signals to drive the motor based on the current dq axis voltage reference vector.

8. A motor system, characterized in that: The system includes the control system as described in claim 7 and an integrated permanent magnet synchronous motor (IPMSM). The control system is connected to the integrated permanent magnet synchronous motor (IPMSM) for controlling the operation of the integrated permanent magnet synchronous motor (IPMSM).

9. An experimental platform system, characterized in that: include: An oscilloscope is used to record the speed, three-phase current, and dq-axis current of an integrated permanent magnet synchronous motor (IPMSM). The dSPACE system is used to convert the MATLAB / Simulink modeled motor control model into C code and upload it to the dSPACE control box, thereby generating an SVPWM wave and sending it to the inverter. The motor control model is used to implement steps 1-4 of the method described in any one of claims 1-6. Load loader, used to test the loading and unloading performance of built-in permanent magnet synchronous motor (IPMSM); An inverter connected to the dSPACE system, which receives SVPWM waves from the dSPACE control box and drives the built-in permanent magnet synchronous motor IPMSM to operate. The permanent magnet synchronous motor is connected to the load, inverter, and load loader.

10. A computer-readable storage medium, characterized in that: The computer program is stored and is invoked by the processor to implement: The steps of the method according to any one of claims 1-6.

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