A permanent magnet synchronous motor speed loop control method, device, medium and equipment

Abstract

The application discloses a permanent magnet synchronous motor rotating speed ring control method and device, medium and equipment, relates to the permanent magnet synchronous motor control technical field.The application first realizes accurate real-time estimation on the load torque by using a supercoil sliding mode load observer, then on-line self-adapting adjustment of the sliding mode controller parameters is realized according to the real-time rotating speed and load torque working condition based on a pre-trained neural network model, and finally the rotating speed is accurately controlled through the sliding mode controller after parameter adjustment.The application effectively solves the influence of load disturbance on the system performance, realizes self-adapting adjustment of the sliding mode controller parameters based on the working condition through the neural network model learning of the applicable relationship between the real-time rotating speed and load torque working condition and the sliding mode controller parameters, overcomes the defects that the traditional sliding mode control parameters are difficult to set, and significantly improves the rotating speed tracking precision, dynamic response speed and overall stability of the system of the permanent magnet synchronous motor under complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology, and in particular to a method, device, medium, and equipment for controlling the speed loop of a permanent magnet synchronous motor. Background Technology

[0002] Currently, permanent magnet synchronous motors are widely used in industrial servo systems, electric vehicles, and precision equipment due to their high efficiency, high power density, and fast response. These applications place extremely high demands on the tracking accuracy, dynamic response, and anti-interference capabilities of the speed loop.

[0003] In existing technologies, compared to traditional PI control, sliding mode control can suppress disturbances and mitigate the effects of parameter changes and torque abrupt changes. Therefore, recent research has mainly focused on speed loop sliding mode control of permanent magnet synchronous motors. Typically, a speed loop sliding mode control model is first established based on the error between the reference speed and the actual speed. Then, a sliding mode switching function and a reaching law for sliding mode control are selected to construct a speed loop sliding mode controller, enabling the speed error to quickly approach zero and achieving speed loop control of the permanent magnet synchronous motor.

[0004] However, since the control performance of a sliding mode controller is closely related to the selection of parameters, improper selection of sliding mode controller parameters can reduce the control performance of the system, such as causing overshoot and excessively long settling time. Therefore, when facing different operating conditions of a permanent magnet synchronous motor, the parameters of the sliding mode controller usually need to be manually adjusted. This process is relatively complex and tedious, resulting in poor dynamic performance and robustness of the speed loop control of the permanent magnet synchronous motor. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, medium, and equipment for controlling the speed loop of a permanent magnet synchronous motor in response to the above-mentioned technical problems.

[0006] The present invention adopts the following technical solution: This invention provides a speed loop control method for a permanent magnet synchronous motor (PMSM). First, the electromagnetic torque equation and kinematic equation of the PMSM are established. Then, based on these equations, a load observer based on a super-helical sliding mode is established to observe the load torque of the PMSM in real time, obtaining the real-time load torque. Next, the real-time speed and real-time load torque of the PMSM are input into a pre-trained neural network model to obtain the parameters of a preset sliding mode controller under real-time operating conditions. Finally, the obtained parameters are substituted into the preset sliding mode controller to construct a sliding mode controller for the PMSM speed loop, thereby controlling the speed of the PMSM.

[0007] This invention provides a speed loop control device for a permanent magnet synchronous motor, comprising: The modeling module is used to establish the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor. The observation module is used to establish a load observer based on the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor, and to observe the load torque of the permanent magnet synchronous motor in real time to obtain the real-time load torque. The parameter adaptive module is used to input the real-time speed and real-time load torque of the permanent magnet synchronous motor into a pre-trained neural network model to obtain the parameters of the preset sliding mode controller under real-time operating conditions. The control module is used to input the obtained parameters into a preset sliding mode controller to construct a sliding mode controller for the speed loop of the permanent magnet synchronous motor, and to control the speed of the permanent magnet synchronous motor.

[0008] The present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described permanent magnet synchronous motor speed loop control method.

[0009] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described permanent magnet synchronous motor speed loop control method.

[0010] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: This invention first utilizes a super-spiral sliding mode load observer to achieve accurate real-time estimation of load torque. Then, based on a pre-trained neural network model, it adaptively adjusts the sliding mode controller parameters online according to real-time speed and load torque conditions. Finally, it precisely controls the speed through the adjusted sliding mode controller. This invention effectively solves the problem of load disturbances affecting system performance. By learning the applicability relationship between real-time speed and load torque conditions and sliding mode controller parameters through a neural network model, it achieves adaptive adjustment of sliding mode controller parameters based on operating conditions. This overcomes the shortcomings of traditional sliding mode control parameters, which are difficult to tune, and significantly improves the speed tracking accuracy, dynamic response speed, and overall system stability of permanent magnet synchronous motors under complex operating conditions. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0012] Figure 1 A schematic flowchart of a speed loop control method for a permanent magnet synchronous motor provided by the present invention; Figure 2 A schematic diagram of a speed loop control for a permanent magnet synchronous motor provided by the present invention; Figure 3This invention provides a flowchart illustrating the training of an RBF neural network using gradient descent. Figure 4 A schematic diagram illustrating the control effect of a conventional speed slip mode controller at a reference speed of 700 rpm and a load torque of 7 Nm, provided by this invention. Figure 5 A schematic diagram illustrating the control effect of the speed slip mode controller of the present invention at a reference speed of 700 rpm and a load torque of 7 Nm; Figure 6 A schematic diagram of a speed loop control device for a permanent magnet synchronous motor provided by the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0014] Currently, the traditional method for speed loop control of permanent magnet synchronous motors (PMSMs) is PI control. However, since PMSM control systems are nonlinear systems, PI control suffers from poor robustness and dynamic performance. Compared to traditional PI control, sliding mode control can suppress disturbances and mitigate the effects of parameter variations and torque surges. To improve speed loop control performance, this invention employs speed loop sliding mode control.

[0015] Furthermore, since the control performance of a sliding mode controller is closely related to parameter selection, improper parameter selection can reduce system control performance, leading to issues such as overshoot and excessively long settling times. Therefore, the parameters of the sliding mode controller typically require manual adjustment for different motor speeds and load torques, a complex and tedious process. There is a need in this field for a method to adaptively adjust the parameters of the sliding mode controller according to different operating conditions, thereby reducing the difficulty of tuning the sliding mode controller parameters while maintaining good dynamic performance of the control system.

[0016] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of a speed loop control method for a permanent magnet synchronous motor according to the present invention, which specifically includes the following steps: S101: Establish the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor.

[0018] S102: Based on the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor, a load observer based on super-helical sliding mode is established to observe the load torque of the permanent magnet synchronous motor in real time and obtain the real-time load torque.

[0019] S103: Input the real-time speed and real-time load torque of the permanent magnet synchronous motor into the pre-trained neural network model to obtain the parameters of the preset sliding mode controller under real-time operating conditions.

[0020] S104: Substitute the obtained parameters into the preset sliding mode controller to construct the sliding mode controller of the permanent magnet synchronous motor speed loop, and control the speed of the permanent magnet synchronous motor.

[0021] For ease of explanation, the following description focuses solely on the server as the executing entity. The server mentioned in this invention can be a server set up on a business platform, or a device such as a desktop computer or laptop computer capable of executing the solution of this invention.

[0022] Figure 2 This is a schematic diagram of the speed loop control block for a permanent magnet synchronous motor according to the present invention. The present invention takes a surface-mounted permanent magnet synchronous motor as an example for illustration. The current equation in the dq coordinate system is: (1).

[0023] in, u d , u q They are respectively dq Voltage of the shaft, i d , i q They are respectively dq Stator current of the shaft, R s For stator resistance, L s For the stator inductance of a permanent magnet synchronous motor, ω e Let Φ be the electric angular velocity. f It is a permanent magnet flux linkage.

[0024] Discretize the above equation, perform one-beat delay compensation, and rearrange to obtain: dq Predicted voltage of the shaft: (2).

[0025] in, and Represent dq axis k The voltage that should be input at time +1 and They represent the predicted k+ 1 moment dqShaft stator current, and They represent dq Shaft reference current.

[0026] Since the motor used in this embodiment is a surface-mounted permanent magnet synchronous motor, the electromagnetic torque equation is: (3).

[0027] in, T e For electromagnetic torque, n This represents the number of pole pairs of the motor.

[0028] Kinematic equations of a permanent magnet synchronous motor (ignoring damping): (4).

[0029] in, J The moment of inertia of the rotor; ω m This refers to the rotor's mechanical angular velocity; T l This represents the load torque.

[0030] This invention establishes a load observer based on superspiral sliding mode control to estimate the load torque of a permanent magnet synchronous motor (PMSM), and uses the estimated load torque as input to an RBF neural network. Specifically, in one or more embodiments of this invention, the server first establishes the state equation of the PMSM using motor speed and load torque as state variables, based on the electromagnetic torque equation and kinematic equation of the PMSM. Then, based on the state equation of the PMSM, the state equation of the load observer based on superspiral sliding mode is established. The state equation of the load observer based on superspiral sliding mode is then discretized to obtain the load observer based on superspiral sliding mode.

[0031] For example, firstly, with the motor speed ω m and load torque T l Assuming the state variables are used, establish the state equations for the permanent magnet synchronous motor: (5).

[0032] State equations for load observers based on superspiral sliding mode: (6).

[0033] in, S It is a sliding mode switching function, and S This equals the error between the estimated rotational speed and the actual rotational speed. α and λ These are the parameters for superspiral sliding mode control. This represents the estimated motor speed. This represents the estimated load torque.

[0034] Discretizing the load observer state equations yields: (7).

[0035] in, This is the speed loop control cycle.

[0036] A speed loop adaptive sliding mode control model based on an RBF neural network is established. First, the speed loop sliding mode control model is established:

[0037] The state equation of the motor is: (8).

[0038] in, This is the reference speed for the motor. This represents the error between the motor's reference speed and its actual speed.

[0039] According to the motion equations of a permanent magnet synchronous motor, we can obtain: (9).

[0040] Select the sliding mode switching function; the expression is: (10).

[0041] in, c These are the parameters of the sliding mode switching function.

[0042] Differentiate the sliding mode switching function: (11).

[0043] According to the kinematic equations of an electric motor, we can obtain: (12).

[0044] The present invention selects an exponential reaching law for sliding mode control: (13).

[0045] Discretize the controller: (14).

[0046] Differentiating the above equation and rearranging it, we get: (15). In the formula, This is the current loop control cycle.

[0047] To determine the stability of the system, the Lyapunov function is the first choice: (16).

[0048] The stability of the system is analyzed by calculating the derivative of the Lyapunov function; if it is less than 0, the system is stable. (17).

[0049] As can be seen from the above equation, the derivative of the Lyapunov function is always less than 0, therefore the system is stable.

[0050] After designing the speed ring sliding mode observer, the RBF neural network is trained. Specifically, in one or more embodiments of this invention, the server can first acquire multiple sets of reference speeds and load torques under historical operating conditions as sample data. For each set of historical operating conditions, the waveforms of speed and current under the parameters of multiple sliding mode controllers are compared, and the parameters of the sliding mode controller with the smallest overshoot, smallest steady-state error, and shortest settling time are used as the annotation of the sample data. Then, the sample data is input into the neural network model to obtain the predicted parameters of the sliding mode controller under the corresponding operating conditions. The neural network model is trained with the goal of minimizing the deviation between the predicted parameters and the annotation.

[0051] The parameters of sliding mode control include: constant velocity reaching term coefficient ( k ), exponential coefficient ( λ ), parameters of the sliding mode switching function ( c Based on simulation results at different speeds and torques, λ and c The numerical change of this factor has a relatively small impact on the speed and current of the permanent magnet synchronous motor. However, the constant speed approaching term coefficient ( k The constant velocity approach term coefficient (VRF) has a significant impact on the dynamic performance of permanent magnet synchronous motors. Therefore, this invention focuses on studying the constant velocity approach term coefficient (VRF). k The adaptive adjustment of ) will λ and c The parameters to be optimized in sliding mode control can be one or more of the following: constants. In practical applications, the parameters to be optimized in sliding mode control can be the constant velocity approaching term coefficients, exponential term coefficients, and parameters of the sliding mode switching function.

[0052] Figure 3 The process of training an RBF neural network using gradient descent is demonstrated. In the RBF neural network of this invention, since the input variables are the motor reference speed and load torque, the dimension of the input layer is 2. The dimension of the hidden layer is set to 5. Since the output is the parameters of the sliding mode controller... k Therefore, the dimension of the output layer is 1.

[0053] The radial basis function in a neural network is: (18).

[0054] in, j Indicates the first hidden layer j In this neural network, there are 10 neurons. j =1,2,3,4,5. c Represents the center of the radial basis functions (RBF). σ This represents the width of the radial basis functions. x As the input matrix, in this neural network, the matrix [ω, T] of motor speed and load torque is normalized to obtain the input matrix. xThe weights from the hidden layer to the output layer are: w j Therefore, the output of this neural network k for: (19).

[0055] This invention uses gradient descent to train the RBF neural network: First, define the loss function: (20).

[0056] in, e This represents the deviation between the expected output and the output from the neural network. P Indicates the number of training samples. For the desired output, This is the output of the neural network.

[0057] when J When the error exceeds a preset threshold, the RBF neural network will backpropagate the error. This is based on the chain rule. J By taking the partial derivatives, we can obtain the output layer weights, the width of the radial basis function, and the gradient at the center of the radial basis function. (twenty one).

[0058] In the formula, σ j For the first j The width of each radial basis function, c j For the first j The center of each radial basis function.

[0059] Update RBF neural network parameters: (twenty two).

[0060] In the formula, , and For the updated RBF neural network parameters, , and These are the parameters of the RBF neural network before the update.

[0061] This invention creates a training set for an RBF neural network by selecting a series of motor speeds and load torques as inputs and choosing appropriate sliding mode controller parameters as outputs. The RBF neural network updates its parameters according to equation (22) until it converges or reaches the maximum number of iterations, thereby obtaining the parameters of the RBF neural network.

[0062] Specific methods for creating RBF neural network training sets: Select a series of different reference speeds and load torques, with reference speeds ranging from 0-2000 rpm and load torques ranging from 0-10 Nm. Under each operating condition, adjust the parameters of the sliding mode controller (…). k By comparing the waveforms of speed and current, the option with smaller overshoot, smaller steady-state error, and shorter settling time is selected. k The value is used as the output of the RBF neural network training set.

[0063] By combining the trained RBF neural network with the speed ring slip mode controller, the controller parameters can be adaptively adjusted according to different operating conditions.

[0064] Figure 4 and Figure 5 The images show the speed (left) and torque (right) of a traditional sliding mode controller and an adaptive sliding mode controller based on an RBF neural network under the conditions of a motor speed of 700 rpm and a load torque of 7 Nm. dq Shaft current (right). The results show that the traditional method is greatly affected by different operating conditions and has a large overshoot. The improved control strategy can adaptively change the controller parameters to adapt to different operating conditions. Under the premise of similar settling time as the traditional sliding mode control, the improved method has no overshoot and improves the dynamic performance and robustness of the system.

[0065] based on Figure 1 The speed loop control method for permanent magnet synchronous motors (PMSMs) presented in this invention first utilizes a super-spiral sliding mode load observer to achieve accurate real-time estimation of load torque. Then, based on a pre-trained neural network model, the sliding mode controller parameters are adaptively adjusted online according to real-time speed and load torque conditions. Finally, the adjusted sliding mode controller precisely controls the speed. This invention effectively solves the problem of load disturbances affecting system performance. By learning the applicability relationship between real-time speed and load torque conditions and sliding mode controller parameters through a neural network model, it achieves adaptive adjustment of sliding mode controller parameters based on operating conditions. This overcomes the shortcomings of traditional sliding mode control parameters, which are difficult to tune, and significantly improves the speed tracking accuracy, dynamic response speed, and overall system stability of the PMSM under complex operating conditions.

[0066] When applying the permanent magnet synchronous motor speed loop control method provided by this invention, it is not necessary to... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.

[0067] The above describes a permanent magnet synchronous motor speed loop control method provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding permanent magnet synchronous motor speed loop control device, such as... Figure 6 As shown.

[0068] Figure 6 A schematic diagram of a speed loop control device for a permanent magnet synchronous motor provided by the present invention includes: Modeling module 201 is used to establish the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor; The observation module 202 is used to establish a load observer based on the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor, and to observe the load torque of the permanent magnet synchronous motor in real time to obtain the real-time load torque. The parameter adaptive module 203 is used to input the real-time speed and real-time load torque of the permanent magnet synchronous motor into a pre-trained neural network model to obtain the parameters of the preset sliding mode controller under real-time operating conditions. The control module 204 is used to substitute the obtained parameters into a preset sliding mode controller to construct a sliding mode controller for the speed loop of the permanent magnet synchronous motor, and to control the speed of the permanent magnet synchronous motor.

[0069] Specific limitations regarding the permanent magnet synchronous motor speed loop control device can be found in the limitations of the permanent magnet synchronous motor speed loop control method described above, and will not be repeated here. Each module in the aforementioned permanent magnet synchronous motor speed loop control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0070] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for speed loop control of permanent magnet synchronous motors.

[0071] This invention also provides a computer device at the hardware level, which includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above-mentioned functions. Figure 1 The provided method for speed loop control of permanent magnet synchronous motors.

[0072] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.

Claims

1. A speed loop control method for a permanent magnet synchronous motor, characterized in that, include: Establish the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor; Based on the electromagnetic torque equation and kinematic equation of permanent magnet synchronous motor, a load observer based on super-helical sliding mode is established to observe the load torque of permanent magnet synchronous motor in real time and obtain the real-time load torque. The real-time speed and real-time load torque of the permanent magnet synchronous motor are input into a pre-trained neural network model to obtain the parameters of the preset sliding mode controller under real-time operating conditions. The obtained parameters are substituted into the preset sliding mode controller to construct the sliding mode controller of the permanent magnet synchronous motor speed loop, and the speed of the permanent magnet synchronous motor is controlled.

2. The speed loop control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, The establishment of a load observer based on super-helical sliding mode, based on the electromagnetic torque equation and kinematic equation of a permanent magnet synchronous motor, specifically includes: Using motor speed and load torque as state variables, the state equation of the permanent magnet synchronous motor is established based on the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor. Based on the state equation of the permanent magnet synchronous motor, the state equation of the load observer based on the super-helical sliding mode is established; The state equation of the load observer based on superhelical sliding mode is discretized to obtain the load observer based on superhelical sliding mode.

3. The speed loop control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, Training the neural network model specifically includes: The reference speed and load torque under multiple historical operating conditions are obtained as sample data. For each historical operating condition, the waveforms of speed and current under the parameters of multiple sliding mode controllers are compared. The parameters of the sliding mode controller with the smallest overshoot, the smallest steady-state error and the shortest settling time are used as the label of the sample data. The sample data is input into the neural network model to obtain the predicted parameters of the sliding mode controller under the corresponding working conditions. The neural network model is trained with the goal of minimizing the deviation between the predicted parameters and the label.

4. The speed loop control method for a permanent magnet synchronous motor as described in claim 1, characterized in that, Establishing the preset sliding mode controller specifically includes: The state equation for the speed loop of the permanent magnet synchronous motor is established using the following formula: ； Based on the state equation of the speed loop and the motion equation of the permanent magnet synchronous motor, we obtain: ； For the selected sliding mode switching function Taking the derivative, we get: ; Using the exponential law As a reaching law for sliding mode control, the sliding mode switching function is discretized and differentiated to obtain the sliding mode controller: ； in, and For different state variables, This represents the error between the reference speed and the actual speed of the permanent magnet synchronous motor. For reference speed, This refers to the actual rotational speed. State variables The derivative of State variables The derivative of For error The derivative of The moment of inertia of the rotor. This represents the number of pole pairs of the motor. It is a permanent magnet flux chain. Let be the stator current along the q-axis. For load torque, This is the sliding mode switching function. These are the parameters of the sliding mode switching function. For the coefficients of the isotropic approaching term, The coefficient of the exponential term, Let be the derivative of the stator current along the q-axis at time t. This is the speed loop control cycle. Let be the derivative of the sliding mode switching function at time t. For the current loop control cycle, Let be the derivative of the actual rotational speed at time t.

5. The speed loop control method for a permanent magnet synchronous motor as described in claim 4, characterized in that, The parameters of the sliding mode controller include: constant velocity approaching term coefficients; or, The parameters of the sliding mode controller include: constant velocity approach term coefficients, exponential term coefficients, and parameters of the sliding mode switching function.

6. A speed loop control device for a permanent magnet synchronous motor, characterized in that, include: The modeling module is used to establish the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor. The observation module is used to establish a load observer based on the electromagnetic torque equation and kinematic equation of the permanent magnet synchronous motor, and to observe the load torque of the permanent magnet synchronous motor in real time to obtain the real-time load torque. The parameter adaptive module is used to input the real-time speed and real-time load torque of the permanent magnet synchronous motor into a pre-trained neural network model to obtain the parameters of the preset sliding mode controller under real-time operating conditions. The control module is used to input the obtained parameters into a preset sliding mode controller to construct a sliding mode controller for the speed loop of the permanent magnet synchronous motor, and to control the speed of the permanent magnet synchronous motor.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method as described in any one of claims 1 to 5.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of claims 1 to 5.

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