Control method for electric motors in series connection

Abstract

The present invention relates to the technical field of electric motor control. Disclosed is a control method for electric motors in series connection, which control method solves the problems of rotation speed synchronization, energy consumption and fault protection of electric motors with axes in series connection. The control method comprises: connecting axes of n electric motors in series, and collecting basic parameters of each electric motor; obtaining a voltage space vector reference value by means of synthesis; acquiring a desired voltage output vector; implementing rotation speed synchronization and load balancing of the electric motors with axes in series connection; and monitoring, in real time, operating processes of the electric motors with axes in series connection, and resolving a fault. In the present invention, the desired voltage output vector is retained by means of a hybrid control algorithm, the rotation speed synchronization of the electric motors with axes in series connection is implemented by means of a counter closed-loop rotation speed algorithm, the load and energy consumption of the electric motors with axes in series connection are kept balanced by means of an energy consumption balancing algorithm, and a fault resolution processing scheme is selected in a combined manner by means of a rights-and-interests selection algorithm, thus greatly improving the collaboration capability of the electric motors, improving the fault detection and response capabilities, and reducing the resource consumption costs.

Description

A control method for series motor Technical Field

[0001] The present invention relates to the technical field of motor control, and more particularly to a control method for a series motor. Background Art

[0002] Tandem motor control is a technology that achieves motor acceleration, deceleration, and positioning control independent of the mechanical transmission system. This control method divides the motor into several segments based on load characteristics and ultimately controls the entire mechanical system by controlling the speed and torque of each segment.

[0003] Axis-tandem motor control was developed to meet the demands of modern industrial systems for higher motion control accuracy and performance. Compared to traditional mechanical transmission control, axis-tandem motor control offers greater flexibility, enabling more refined motion control by controlling the speed and torque of each motor, while also offering higher control accuracy and stability.

[0004] However, tandem motor control also has certain drawbacks. First, its control cost is relatively high because it requires a large number of motors and motor controllers. Second, the coordination and configuration of the motors require careful coordination, otherwise interference and oscillation between the motors are likely to occur. Finally, because each motor requires a separate controller, the system's electromagnetic interference and anti-interference capabilities must be improved.

[0005] Summary of the Invention

[0006] In response to the deficiencies of the above-mentioned technologies, the present invention discloses a control method for series motors. The present invention maintains the desired voltage output vector through a hybrid control algorithm, achieves speed synchronization of each motor in series through an inverse closed-loop speed algorithm, balances the load and energy consumption of each motor in series through an energy consumption balancing algorithm, and selects fault resolution solutions through a combination of equity selection algorithms, thereby greatly improving the motor collaboration capability, improving the fault detection and response capabilities, and reducing resource consumption costs.

[0007] In view of this, the present invention provides a control method for a series motor, comprising the following steps:

[0008] Step 1: Connect the axes of n motors in series and collect the basic parameters of each motor;

[0009] Obtaining basic parameters of each motor through a rotary transformer, including shaft angular displacement, angular velocity, rotational speed, and phase current;

[0010] Step 2: synthesize the obtained basic motor parameters into a voltage space vector reference value;

[0011] Calculate the voltage space vector reference value through the calculation module;

[0012] Step 3: Obtain the desired voltage output vector by using the hybrid control algorithm and the voltage space vector reference value;

[0013] The inverse transformation module is used to obtain the desired voltage output vector;

[0014] Step 4: Realize the speed synchronization and load balancing of the motors connected in series;

[0015] A synchronous balancing module is used to achieve synchronous speed and balanced load for each motor connected in series; the synchronous balancing module includes a speed synchronization unit and an energy-saving load unit. The speed synchronization unit uses an inverse closed-loop speed algorithm to achieve speed synchronization for each motor connected in series, and the energy-saving load unit uses an energy consumption balancing algorithm to balance the load and energy consumption of each motor connected in series according to the basic parameters of each motor. The output end of the speed synchronization unit is connected to the input end of the energy-saving load unit.

[0016] Step 5: Monitor the working process of each motor in series in real time and respond to emergencies in a timely manner;

[0017] A monitoring feedback module is used to implement real-time monitoring and fault response strategies for each motor in series connection.

[0018] As a further embodiment of the present invention, the calculation module includes a transformation unit and an adjustment unit. The transformation unit transforms the acquired actual value coordinates of the motor phase current into the current components of each motor axis. The adjustment unit uses a PID algorithm to convert the error between the current components of each motor axis and the zero value into the voltage components of each motor axis. The output end of the transformation unit is connected to the input end of the adjustment unit.

[0019] As a further embodiment of the present invention, the monitoring feedback module includes a fault diagnosis unit, a strategy protection unit, an emergency braking unit and an energy feedback unit. The fault diagnosis unit adopts an abnormality detection algorithm to detect the basic parameters and expected voltage output vector values ​​of each motor. The strategy protection unit selects a combination of fault resolution solutions through an equity selection algorithm. The emergency braking unit performs emergency braking on the faulty motor through braking processing. The energy feedback unit adopts a hybrid feedback algorithm based on FOC and MPPT to recover and reuse the energy after emergency braking. The output end of the fault diagnosis unit is connected to the input end of the strategy protection unit, the output end of the strategy protection unit is connected to the input end of the emergency braking unit, and the output end of the emergency braking unit is connected to the input end of the energy feedback unit.

[0020] As a further embodiment of the present invention, the inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit. The rotation-stationary unit rotates and inversely transforms the voltage components of each shaft of the motor into coordinate axis components in a two-phase stationary coordinate system through 6 / 2 and 2s / 2r. The amplitude-phase positioning unit uses a hybrid control algorithm to calculate the amplitude and phase of the reference value in the two-phase stationary coordinate system, and determines the sector and the required voltage space vector based on the phase. The output end of the rotation-stationary unit is connected to the input end of the amplitude-phase positioning unit.

[0021] As a further embodiment of the present invention, the working method of the inverse closed-loop speed algorithm is: first, a mathematical model of the motor is established based on the back electromotive force, motor inductance and resistance, then the motor's back electromotive force is used to predict the motor's output characteristics and used for the current control loop, then a speed control loop is added on the basis of the current control loop, and finally the output speed of the motor is adjusted in real time through the speed control loop.

[0022] As a further embodiment of the present invention, the working method of the hybrid control algorithm is: first, the actual current value is compared with the target current value to obtain a current error signal and use it as a PWM reference signal; then, the power supply voltage is compared and sampled to obtain the difference between the reference voltage value and the actual voltage value; then, the difference is modulated by a triangular wave to form a PWM carrier signal; finally, the PWM reference signal and the PWM carrier signal are compared and adjusted to keep the current value within the error range at all times.

[0023] As a further embodiment of the present invention, the working method of the energy consumption balancing algorithm is:

[0024] First, convert the basic parameters of each motor into matrix form.

[0025] In formula (1), X * represents the parameter matrix, U represents the interference matrix, G represents the combination coefficient matrix, σ represents the main coefficient, G m represents the combined effect value, and m represents the number of columns;

[0026] The surface features of the parameter data are converted into deep features through the data conversion function, and the data conversion function is:

[0027] In formula (2), Y # Represents the data depth feature, X * represents the surface characteristics of the data, B represents the conversion parameter, H represents the data dimension parameter, and T represents the error adjustment parameter;

[0028] The acquired data depth features are trained iteratively 180 times to obtain the basic model weight parameters, and prediction and recognition are performed based on real-time data. The recognition function is expressed as:

[0029] In formula (3), E V It indicates the correlation between real-time data and forecast data, and V indicates the adjustment period;

[0030] In order to avoid overfitting during training, the training process parameters are restricted. The restriction function is expressed as:

[0031] In formula (4), Represents the training process parameter restriction variable, X V Indicates the capture of training process information during the training cycle, X T Represents data dynamic information during the training cycle;

[0032] By comparing the load and energy consumption changes in two adjacent cycles, we can determine whether the training process parameter weights are optimal. The comparison results are expressed as:

[0033] In formula (5), Indicates the parameter information obtained during the training cycle. It represents the parameter information collected in adjacent periods, and ε represents the difference coefficient between two adjacent periods.

[0034] As a further embodiment of the present invention, the working method of the equity selection algorithm is: first, the initial fault difficulty value is set to 0 so that the node can generate a new solution. Then, the node obtains the right to add a new solution by calculating the fault answer. The hash value of the new solution meets the normal operation requirements of the motor. When the trust node detects an abnormality, it reaches a consensus through multiple negotiations and completes it in a short time to confirm the addition of the new solution. Then, after collecting enough new solutions, the node must choose between these new solutions. Finally, the subsequent fault difficulty value is adjusted according to the result of each equity selection and the length of the equity selection time.

[0035] The present invention has the following positive and beneficial effects compared to the prior art:

[0036] The present invention maintains the desired voltage output vector through a hybrid control algorithm, achieves speed synchronization of each motor in series through an inverse closed-loop speed algorithm, balances the load and energy consumption of each motor in series through an energy consumption balancing algorithm, and selects fault resolution solutions through a combination of equity selection algorithms, thereby greatly improving the motor collaboration capability, improving the fault detection and response capabilities, and reducing resource consumption costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0038] Fig. 1 is a flow chart of the present invention;

[0039] FIG2 is a basic structural diagram of the present invention;

[0040] Figure 3 is a diagram of the monitoring feedback module architecture;

[0041] Figure 4 is a diagram of the synchronous equalization module architecture;

[0042] Figure 5 is a diagram of the inverse transform module architecture. DETAILED DESCRIPTION

[0043] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0044] As shown in FIG1 , a control method for a series motor includes the following steps:

[0045] Step 1: Connect the axes of n motors in series and collect the basic parameters of each motor;

[0046] Obtaining basic parameters of each motor through a rotary transformer, including shaft angular displacement, angular velocity, rotational speed, and phase current;

[0047] Step 2: synthesize the obtained basic motor parameters into a voltage space vector reference value;

[0048] Calculate the voltage space vector reference value through the calculation module;

[0049] Step 3: Obtain the desired voltage output vector by using the hybrid control algorithm and the voltage space vector reference value;

[0050] The inverse transformation module is used to obtain the desired voltage output vector;

[0051] Step 4: Realize the speed synchronization and load balancing of the motors connected in series;

[0052] A synchronous balancing module is used to achieve synchronous speed and balanced load for each motor connected in series; the synchronous balancing module includes a speed synchronization unit and an energy-saving load unit. The speed synchronization unit uses an inverse closed-loop speed algorithm to achieve speed synchronization for each motor connected in series, and the energy-saving load unit uses an energy consumption balancing algorithm to balance the load and energy consumption of each motor connected in series according to the basic parameters of each motor. The output end of the speed synchronization unit is connected to the input end of the energy-saving load unit.

[0053] Step 5: Monitor the working process of each motor in series in real time and respond to emergencies in a timely manner;

[0054] A monitoring feedback module is used to implement real-time monitoring and fault response strategies for each motor in series connection.

[0055] The output end of the rotary transformer is connected to the input end of the calculation module, the output end of the calculation module is connected to the input end of the inverse transformation module, the output end of the inverse transformation module is connected to the input end of the synchronous balancing module, and the output end of the synchronous balancing module is connected to the input end of the monitoring feedback module.

[0056] Furthermore, the calculation module includes a transformation unit and an adjustment unit. The transformation unit transforms the acquired actual value coordinates of the motor phase current into the current components of each motor shaft. The adjustment unit uses a PID algorithm to convert the error between the current components of each motor shaft and the zero value into the voltage components of each motor shaft. The output end of the transformation unit is connected to the input end of the adjustment unit.

[0057] Furthermore, the monitoring feedback module includes a fault diagnosis unit, a strategy protection unit, an emergency braking unit and an energy feedback unit. The fault diagnosis unit adopts an abnormality detection algorithm to detect the basic parameters and expected voltage output vector values ​​of each motor. The strategy protection unit selects a combination of fault resolution solutions through an equity selection algorithm. The emergency braking unit performs emergency braking on the faulty motor through braking processing. The energy feedback unit adopts a hybrid feedback algorithm based on FOC and MPPT to recover and reuse the energy after emergency braking. The output end of the fault diagnosis unit is connected to the input end of the strategy protection unit, the output end of the strategy protection unit is connected to the input end of the emergency braking unit, and the output end of the emergency braking unit is connected to the input end of the energy feedback unit.

[0058] In a specific embodiment, the working principle of the monitoring feedback module is as follows: the basic parameters and expected voltage output vector values ​​of each motor are detected through an abnormality detection algorithm, and the detection results are output. If a fault occurs, the corresponding fault information will be output. According to the output result of the fault diagnosis unit, the equity selection algorithm is used to select the best solution from the fault resolution scheme. After the solution is selected, it is output for processing by the next unit. The solution output by the receiving strategy protection unit is used to perform emergency braking on the faulty motor through the brake processing. This can avoid more serious faults in the motor. Finally, a hybrid feedback algorithm based on FOC and MPPT is used to recover and reuse the energy after emergency braking.

[0059] Furthermore, the inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit. The rotation-stationary unit rotates and inversely transforms the voltage components of each shaft of the motor into coordinate axis components in a two-phase stationary coordinate system through 6 / 2 and 2s / 2r. The amplitude-phase positioning unit uses a hybrid control algorithm to calculate the amplitude and phase of the reference value in the two-phase stationary coordinate system, and determines the sector and the required voltage space vector based on the phase. The output end of the rotation-stationary unit is connected to the input end of the amplitude-phase positioning unit.

[0060] Furthermore, the working method of the inverse closed-loop speed algorithm is: first, a mathematical model of the motor is established based on the back electromotive force, motor inductance and resistance, and then the motor's back electromotive force is used to predict the motor's output characteristics and used for the current control loop, and then a speed control loop is added on the basis of the current control loop, and finally the output speed of the motor is adjusted in real time through the speed control loop.

[0061] In a specific embodiment, the working principle of the inverse closed-loop speed algorithm is as follows: a mathematical model of the motor is established, including back electromotive force and parameters such as motor inductance and resistance. The performance of the motor is accurately analyzed through the mathematical model, laying the foundation for the establishment of the control algorithm. During the control process, the back electromotive force of the motor is used to predict the output characteristics of the motor, and it is used in the current control loop to achieve predictability of motor control. On the basis of the current control loop, a speed control loop is added to achieve dual closed-loop control. Through the speed control loop, the output speed of the motor is adjusted in real time to ensure that the output speed of the motor is accurately matched with the target speed. By adjusting the control parameters, the motor system is optimized to achieve more accurate control results. It is necessary to perform multiple experiments and adjustments according to the actual situation of the motor until the control effect reaches the optimal state, as shown in Table 1.

[0062] Table 1 Speed ​​regulation control table

[0063] As can be seen from the table above, the three speed control algorithms are back-EMF, dual closed-loop, and inverse closed-loop speed. Their target speed is 3000 rpm, but there are some differences in adjustment time, synchronization rate, and actual speed.

[0064] 1. Back-EMF Control: The control time was relatively long, at 186ms. The synchronization rate was 81.3%, indicating fair control effectiveness. The actual speed was 2439 rpm, still somewhat below the target speed.

[0065] 2. Dual closed-loop control: The control time is relatively short, at 152ms. The synchronization rate is 89.6%, indicating good control performance. The actual speed is 2688 rpm, close to the target speed.

[0066] 3. Inverse closed-loop speed regulation control: The control time is the shortest at 27ms. The synchronization rate is the highest at 98.7%, demonstrating excellent control effectiveness. The actual speed is closest to the target speed at 2961r / min.

[0067] In summary, the inverse closed-loop speed regulation control has advantages over the other two control algorithms in terms of regulation time, synchronization rate and actual speed.

[0068] Furthermore, the working method of the hybrid control algorithm is: first, the actual current value is compared with the target current value to obtain a current error signal and use it as a PWM reference signal; then, the power supply voltage is compared and sampled to obtain the difference between the reference voltage value and the actual voltage value; then, the difference is modulated by a triangular wave to form a PWM carrier signal; finally, the PWM reference signal and the PWM carrier signal are compared and adjusted to keep the current value within the error range at all times.

[0069] In a specific embodiment, the hybrid control algorithm operates as follows: first, current hysteresis control is performed, i.e., high-frequency current regulation is performed to maintain the current within a certain range without jumps or oscillations. Specifically, the actual current value is compared with the target current value. After obtaining an error signal, a controller outputs a corresponding control signal to maintain the current value within the error range. The AC power supply driving the motor is controlled using PWM. Specifically, the power supply voltage is compared and sampled to obtain the difference between the reference voltage and the actual voltage. This difference is then modulated using a triangular wave to form a PWM carrier signal, which controls the magnitude and direction of current flow. Combining current hysteresis control with PWM carrier control creates a hybrid control strategy, achieving more precise motor control. The hybrid control method uses the error signal from current hysteresis control as the reference signal for the PWM controller, enabling precise motor drive within the error range. The hybrid control strategy is implemented through a control system to control motor operation. This can be achieved using an embedded unit or a DSP processor, programming a hybrid control algorithm combining current hysteresis control and PWM carrier control to precisely control the output voltage and current of the motor power supply, as shown in Table 2.

[0070] Table 2 Motor parameter adjustment table

[0071] As can be seen from the table above, the three motor parameter adjustment control algorithms are current hysteresis, PWM, and hybrid control. They differ in terms of current, voltage, and expected voltage.

[0072] 1. Current hysteresis regulation control: The algorithm has a current of 24.6A, a voltage of 30.5V, a desired voltage of 30V, and a regulation time of 113ms.

[0073] 2. PWM regulation control: The algorithm has a current of 28.4A, a voltage of 30.7V, a desired voltage of 30V, and a regulation time of 84ms.

[0074] 3. Hybrid control regulation control: The current of this algorithm is 20.8A, the voltage is 30.1V, the expected voltage is 30V, and the regulation time is 28ms.

[0075] Overall, the hybrid control algorithm shows excellent results in terms of current, voltage, and expected voltage, and has the shortest adjustment time of 28ms.

[0076] Furthermore, the working method of the energy consumption balancing algorithm is:

[0077] First, convert the basic parameters of each motor into matrix form.

[0078] In formula (1), X * represents the parameter matrix, U represents the interference matrix, G represents the combination coefficient matrix, σ represents the main coefficient, G m represents the combined effect value, and m represents the number of columns;

[0079] The surface features of the parameter data are converted into deep features through the data conversion function, and the data conversion function is:

[0080] In formula (2), Y # Represents the data depth feature, X * represents the surface characteristics of the data, B represents the conversion parameter, H represents the data dimension parameter, and T represents the error adjustment parameter;

[0081] The acquired data depth features are trained iteratively 180 times to obtain the basic model weight parameters, and prediction and recognition are performed based on real-time data. The recognition function is expressed as:

[0082] In formula (3), E V It indicates the correlation between real-time data and forecast data, and V indicates the adjustment period;

[0083] In order to avoid overfitting during training, the training process parameters are restricted. The restriction function is expressed as:

[0084] In formula (4), Represents the training process parameter restriction variable, X V Indicates the capture of training process information during the training cycle, X T Represents data dynamic information during the training cycle;

[0085] By comparing the load and energy consumption changes in two adjacent cycles, we can determine whether the training process parameter weights are optimal. The comparison results are expressed as:

[0086] In formula (5), Indicates the parameter information obtained during the training cycle. It represents the parameter information collected in adjacent periods, and ε represents the difference coefficient between two adjacent periods.

[0087] In a specific embodiment, the working principle of the energy consumption balancing algorithm is as follows: the system will detect and record the energy consumption of each motor, including real-time energy consumption and historical average energy consumption values. When the energy consumption value of a motor in the system exceeds the set value or an abnormal situation such as overload occurs, the energy consumption balancing algorithm will transfer the excess energy to other motors that have not exceeded the set value through capacitors and other means. The energy consumption balancing algorithm monitors and analyzes the energy consumption situation and adopts different solutions to manage and adjust the system energy consumption to achieve energy consumption balance of the system. In actual applications, the energy consumption balancing algorithm usually sets the total energy consumption of the system. When the energy consumption of a motor in the system exceeds the set value, the energy consumption balancing algorithm will transfer the excess energy to other motors to achieve the purpose of energy consumption balance of the system, as shown in Table 3.

[0088] Table 3 Energy consumption distribution comparison table

[0089] The table above compares the energy allocation of different algorithms for the system using both no energy allocation and energy balancing. A, B, C, and D represent the energy consumption of the four motors, Frag / share represents the size of each fragment, and Effect / % represents the improvement in effectiveness of the energy balancing algorithm compared to no energy allocation.

[0090] 1. Without energy allocation, the energy consumption values ​​of the four motors are 202, 173, 127, and 186, respectively, which is somewhat unbalanced. The size of each fragment is 75, and the effect is 81.4%.

[0091] 2. In the case of energy balancing, the energy consumption values ​​of the four motors are evenly distributed, reaching 204, 198, 208, and 194, respectively, with an energy balancing effect of 99.7%. This shows that the energy balancing algorithm can achieve a better balance between the energy consumption values ​​of the motors, thereby improving the energy utilization efficiency of the system.

[0092] Furthermore, the working method of the equity selection algorithm is as follows: first, the initial fault difficulty value is set to 0 so that the node can generate a new solution. Then, the node obtains the right to add a new solution by calculating the fault answer. The hash value of the new solution meets the normal operation requirements of the motor. When the trust node detects an abnormality, it reaches a consensus through multiple negotiations and completes it in a short time to confirm the addition of the new solution. Then, after collecting enough new solutions, the node must choose between these new solutions. Finally, the subsequent fault difficulty value is adjusted according to the result of each equity selection and the length of the equity selection time.

[0093] In a specific embodiment, the pseudo code of the equity selection algorithm is implemented as follows:

[0094] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these specific embodiments are merely illustrative, and that those skilled in the art may omit, substitute, and modify the details of the methods and systems described above without departing from the principles and spirit of the present invention. For example, combining the above method steps to perform substantially the same functions in substantially the same manner to achieve substantially the same results falls within the scope of the present invention. Accordingly, the scope of the present invention is limited solely by the appended claims.

Claims

1. A control method for a series motor, characterized in that: The steps include: Step 1: Connect the axes of n motors in series and collect the basic parameters of each motor; Obtaining basic parameters of each motor through a rotary transformer, the basic parameters including shaft angular displacement, angular velocity, rotation speed and phase current; Step 2, synthesizing the obtained basic parameters of the motor into a voltage space vector reference value; Calculate the voltage space vector reference value through the calculation module; Step 3: Obtain the desired voltage output vector by using a hybrid control algorithm and a voltage space vector reference value; The inverse transformation module is used to obtain the desired voltage output vector; Step 4: Realize the speed synchronization and load balancing of the shaft-series motors; A synchronous balancing module is used to achieve synchronous speed and balanced load for each motor connected in series; the synchronous balancing module includes a speed synchronization unit and an energy-saving load unit, the speed synchronization unit uses an inverse closed-loop speed algorithm to achieve speed synchronization for each motor connected in series, the energy-saving load unit uses an energy consumption balancing algorithm to balance the load and energy consumption of each motor connected in series according to the basic parameters of each motor, and the output end of the speed synchronization unit is connected to the input end of the energy-saving load unit; Step 5: Monitor the working process of each motor in series in real time and respond to emergencies in a timely manner; use a monitoring feedback module to implement real-time monitoring and fault response strategies for each motor in series.

2. A control method for a series motor according to claim 1, characterized in that: The calculation module includes a transformation unit and an adjustment unit. The transformation unit transforms the acquired actual value coordinates of the motor phase current into the current components of each motor shaft. The adjustment unit uses a PID algorithm to convert the errors between the current components of each motor shaft and the zero value into the voltage components of each motor shaft. The output end of the transformation unit is connected to the input end of the adjustment unit.

3. The control method of a series motor according to claim 1, characterized in that: The monitoring feedback module includes a fault diagnosis unit, a strategy protection unit, an emergency braking unit and an energy feedback unit. The fault diagnosis unit uses an abnormality detection algorithm to detect the basic parameters and expected voltage output vector values ​​of each motor. The strategy protection unit selects a combination of fault resolution solutions through an equity selection algorithm. The emergency braking unit performs emergency braking on the faulty motor through a brake process. The energy feedback unit uses a hybrid feedback algorithm based on FOC and MPPT to recover and reuse the energy after emergency braking. The output end of the fault diagnosis unit is connected to the input end of the strategy protection unit, the output end of the strategy protection unit is connected to the input end of the emergency braking unit, and the output end of the emergency braking unit is connected to the input end of the energy feedback unit.

4. The control method of a series motor according to claim 1, characterized in that: The inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit. The rotation-stationary unit rotates and inversely transforms the voltage components of each shaft of the motor into coordinate axis components in a two-phase stationary coordinate system through 6 / 2 and 2s / 2r. The amplitude-phase positioning unit uses a hybrid control algorithm to calculate the amplitude and phase of the reference value in the two-phase stationary coordinate system, and determines the sector and the required voltage space vector according to the phase. The output end of the rotation-stationary unit is connected to the input end of the amplitude-phase positioning unit.

5. The control method of a series motor according to claim 1, characterized in that: The working method of the inverse closed-loop speed algorithm is as follows: first, a mathematical model of the motor is established based on the back electromotive force, motor inductance and resistance, and then the back electromotive force of the motor is used to predict the output characteristics of the motor and used for the current control loop, and then a speed control loop is added based on the current control loop, and finally the output speed of the motor is adjusted in real time through the speed control loop.

6. The control method of a series motor according to claim 4, characterized in that: The working method of the hybrid control algorithm is: first, the actual current value is compared with the target current value to obtain a current error signal and use it as a PWM reference signal; then the power supply voltage is compared and sampled to obtain the difference between the reference voltage value and the actual voltage value; then the difference is modulated by a triangular wave to form a PWM carrier signal; finally, the PWM reference signal and the PWM carrier signal are compared and adjusted to keep the current value within the error range at all times.

7. The control method of a series motor according to claim 1, characterized in that: The working method of the energy consumption balancing algorithm is: First, the basic parameters of each motor are converted into matrix form. In formula (1), X * represents the parameter matrix, U represents the interference matrix, G represents the combination coefficient matrix, σ represents the main coefficient, G m represents the combined effect value, and m represents the number of columns; The surface features of the parameter data are converted into deep features through a data conversion function, and the data conversion function is: In formula (2), Y # represents the data depth feature, X * represents the surface characteristics of the data, B represents the conversion parameter, H represents the data dimension parameter, and T represents the error adjustment parameter; The acquired data depth features are iterated through 180 times to obtain the basic model weight parameters, and prediction and recognition are performed based on real-time data. The recognition function is expressed as: In formula (3), E V Indicates the correlation between real-time data and predicted data, and V indicates the adjustment period. To avoid overfitting during training, the training process parameters are restricted, and the restriction function is expressed as: In formula (4), represents the training process parameter restriction variable, X V Indicates the information captured during the training cycle. T Represents data dynamic information during the training cycle; By comparing the changing patterns of load and energy consumption in two adjacent cycles, it is determined whether the training process parameter weights are optimal. The comparison results are expressed as: In formula (5), Indicates the parameter information obtained during the training cycle. It represents the parameter information collected in adjacent periods, and ε represents the difference coefficient between two adjacent periods.

8. The control method of a series motor according to claim 3, characterized in that: The working method of the equity selection algorithm is as follows: first, the initial fault difficulty value is set to 0 so that the node can generate a new solution. Then, the node obtains the right to add a new solution by calculating the fault answer. The hash value of the new solution meets the normal operation requirements of the motor. When the trusted node detects an abnormality, it reaches a consensus through multiple negotiations and completes it in a short time to confirm the addition of the new solution. Then, after collecting enough new solutions, the node must choose between these new solutions. Finally, the subsequent fault difficulty values ​​are adjusted according to the results of each equity selection and the length of the equity selection time.

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