Control method for series-connected motors

Abstract

The present invention discloses a control method for series-connected motors, which relates to the technical field of motor control and solves the problems of rotational speed synchronization, energy consumption, and fault protection for motors connected in series along their axes. The control method includes the steps of connecting n motors in series along their axes, collecting basic parameters of each motor, synthesizing voltage space vector reference values, obtaining a desired voltage output vector, achieving rotational speed synchronization and load balance for each of the motors connected in series along their axes, and monitoring the operation process of each motor in series along their axes in real time to resolve faults. The present invention maintains the desired voltage output vector using a hybrid control algorithm, achieves rotational speed synchronization for each of the motors connected in series along their axes using an inverse closed-loop rotational speed algorithm, maintains the load and energy consumption balance for each of the motors connected in series along their axes using an energy consumption balancing algorithm, and selects a fault resolution processing method using a combination of stake selection algorithms (the stake selection algorithm selects a combination of fault resolution processing methods), thereby significantly improving the motor coordination ability, improving fault detection and response capabilities, and reducing resource consumption costs.

Description

[Technical Field]

[0001] The present invention relates to the technical field of motor control, and in particular to a method for controlling a series-connected motor. [Background technology]

[0002] Control of motors connected in series is a technology that realizes acceleration, deceleration, and positioning control of motors independently from the mechanical transmission system. This control method divides the motor into multiple segments according to the load characteristics, and controls the rotation speed and torque of the motor in each segment to achieve control of the entire mechanical system.

[0003] The control of axial series-connected motors has been proposed to meet the requirements of modern industrial systems that require higher motion control precision and characteristics. Compared with traditional mechanical transmission control, the control of axial series-connected motors is more flexible, and by controlling the rotation speed and torque of each motor, it is possible to achieve higher precision motion control, and the control precision and stability are higher.

[0004] However, controlling motors connected in series has several drawbacks. First, it requires more motors and motor controllers, resulting in high control costs. Second, the coordination between the motors requires more appropriate adjustment and configuration; otherwise, interference and vibration problems between the motors are likely to occur. Finally, each motor requires an independent controller, which places higher requirements on the system's electromagnetic interference and interference resistance. Summary of the Invention

[0005] In response to the deficiencies of the above techniques, the present invention discloses a control method for series-connected motors, which uses a hybrid control algorithm to maintain a desired voltage output vector, an inverse closed-loop rotational speed algorithm to synchronize the rotational speeds of each motor connected in series with its axis, an energy consumption balancing algorithm to maintain a balance between the load and energy consumption of each motor connected in series with its axis, and a combination of a stake selection algorithm to select a fault resolution processing method, thereby greatly improving the coordination ability of the motors, improving fault detection and response capabilities, and reducing resource consumption costs.

[0006] In view of this, the present invention provides: A method for controlling a series-connected motor, comprising the following steps 1 to 5: Step 1: Connect n motor axes in series and collect the basic parameters of each motor. The basic parameters of each motor, including the angular displacement, angular velocity, rotational speed and phase current of the rotating shaft, are acquired by a resolver; Step 2: Synthesize the acquired motor basic parameters into a voltage space vector reference value; Calculating the voltage space vector reference value by a calculation module; Step 3: Obtain the desired voltage output vector through the hybrid control algorithm and the voltage space vector reference value; Utilizing an inverse transformation module to obtain the desired voltage output vector; Step 4: Synchronize the rotation speeds of the motors connected in series and balance the load. A synchronous balancing module is used to maintain synchronized rotation speeds and balanced loads of each of the motors connected in series with respect to the shafts, the synchronous balancing module includes a rotation speed synchronization unit and an energy-saving load unit, the rotation speed synchronization unit uses an inverse closed-loop rotation speed algorithm to synchronize the rotation speeds of each of the motors connected in series with respect to the shafts, the energy-saving load unit uses an energy consumption balancing algorithm to balance the loads and energy consumptions of each of the motors connected in series with respect to the shafts according to the basic parameters of each motor, the output terminal of the rotation speed synchronization unit is connected to the input terminal of the energy-saving load unit, Step 5: Monitor the operation process of each motor in series connection in real time, and respond promptly to emergencies. A method for controlling series-connected motors is provided, which utilizes a monitoring feedback module to realize real-time monitoring and fault response measures for each motor in series-connected axes.

[0007] In a further embodiment of the present invention, the calculation module includes a conversion unit and an adjustment unit, wherein the conversion unit converts the acquired actual value coordinates of the phase current of the motor into current components of each axis of the motor, and the adjustment unit converts the errors between the current components of each axis of the motor and the zero value into voltage components of each axis of the motor using a PID algorithm, and the output terminal of the conversion unit is connected to the input terminal of the adjustment unit.

[0008] In a further embodiment of the present invention, the monitoring feedback module includes a fault diagnosis unit, a countermeasure protection unit, an emergency braking unit, and an energy feedback unit. The fault diagnosis unit uses an abnormality detection algorithm to detect the acquired basic parameters and desired voltage output vector values ​​of each motor. The countermeasure protection unit combines and selects a fault resolution processing method using a stake selection algorithm. The emergency braking unit emergency brakes the faulty motor using a handbrake. The energy feedback unit recovers and reuses energy after emergency braking using a hybrid feedback algorithm based on FOC and MPPT. The output terminal of the fault diagnosis unit is connected to the input terminal of the countermeasure protection unit. The output terminal of the countermeasure protection unit is connected to the input terminal of the emergency braking unit. The output terminal of the emergency braking unit is connected to the input terminal of the energy feedback unit.

[0009] In another embodiment of the present invention, the inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit, wherein the rotation-stationary unit rotates the voltage components of each shaft of the motor by 6 / 2 and 2s / 2r and inversely transforms them into coordinate axis components of the two-phase stationary coordinate system, the amplitude-phase positioning unit calculates the amplitude and phase of the reference value in the two-phase stationary coordinate system using a hybrid control algorithm, and determines the sector and required voltage space vector according to the phase, and the output terminal of the rotation-stationary unit is connected to the input terminal of the amplitude-phase positioning unit.

[0010] In a further embodiment of the present invention, the inverse closed-loop rotation speed algorithm operates as follows: first, a mathematical model of the motor is created based on the back electromotive force, the inductance and resistance of the motor; then, the back electromotive force of the motor is used to predict the motor output characteristics, which are then used in the current control loop; subsequently, a speed control loop is added based on the current control loop; and finally, the output rotation speed of the motor is adjusted in real time by the speed control circuit.

[0011] In a further embodiment of the present invention, the hybrid control algorithm operates as follows: first, the actual current value is compared with the target current value to obtain a current error signal, which is used 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; this difference is then modulated with a triangular wave to form a PWM carrier signal; and finally, the PWM reference signal is compared with the PWM carrier signal to adjust the current value so that it is always within the error range.

[0012] In a further embodiment of the present invention, the method for operating the energy consumption balancing algorithm comprises: First, the basic parameters of each motor are converted into a matrix format. JPEG2025531007000002.jpg40136In equation (1), X * is the parameter determinant, U is the interference matrix, G is the composite coefficient matrix, σ is the main coefficient, G m is the composite effect value, m is the number of columns, Transforming surface features of the parameter data into depth features by a data transformation function, the data transformation function comprising: JPEG2025531007000003.jpg11146, In equation (2), Y # is the data depth feature, X * represents the data surface feature, B represents the transformation parameter, H represents the data dimension parameter, and T represents the error adjustment parameter. The acquired data depth features are trained 180 times to obtain the basic model weight parameters, and prediction classification is performed based on real-time data. The classification function is: It is represented by JPEG2025531007000004.jpg16144, In equation (3), E V represents the correlation between real-time data and forecast data, V represents the adjustment period, To avoid overfitting during the training process, we limit the training process parameters, and the limiting function is It is represented as JPEG2025531007000005.jpg12146, In equation (4), JPEG2025531007000006.jpg43 is the limit variable of the training process parameters, X V is the capture information of the training process during the training period, X T represents the data dynamic information within the training period, By comparing the change patterns of load and energy consumption between two adjacent periods, we determine whether the weights of the training process parameters are optimal. It is represented as JPEG2025531007000007.jpg16146, In equation (5), JPEG2025531007000008.jpg43 is the parameter information obtained during the training period. JPEG2025531007000009.jpg45Parameter information collected in adjacent periods, ε represents the difference coefficient between two adjacent periods.

[0013] In a further embodiment of the present invention, the operation method of the stake selection algorithm is as follows: first, the initial fault difficulty value is set to 0 to allow the node to generate a new solution; then the node obtains the right to add the new solution by calculating the fault response; when the hash value of the new solution meets the normal operating requirements of the motor and the trusted node detects an abnormality, it reaches a consensus through multiple negotiations, which are completed in a short time, and confirms the addition of the new solution; subsequently, after collecting enough new solutions, the node needs to select from these new solutions; and finally, it adjusts the subsequent fault difficulty value based on the stake selection result and the length of the stake selection time each time.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0015] The present invention uses a hybrid control algorithm to maintain the desired voltage output vector, an inverse closed-loop rotational speed algorithm to synchronize the rotational speeds of the motors connected in series with each other, an energy consumption balancing algorithm to maintain a balance between the load and energy consumption of the motors connected in series with each other, and a stake selection algorithm to select a fault resolution processing method, which greatly improves the coordination ability of the motors, improves fault detection and response capabilities, and reduces resource consumption costs. [Brief explanation of the drawings]

[0016] In order to more clearly describe the embodiments of the present invention or the technical solutions in the prior art, the following will briefly describe the drawings that need to be used in describing the embodiments or the prior art. It is obvious that the drawings described below are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings without any creative efforts. [Figure 1] 1 is a flowchart of the present invention. [Figure 2] FIG. 1 is a basic architecture diagram of the present invention. [Figure 3] FIG. 1 is an architecture diagram of a supervisory feedback module. [Figure 4] FIG. 1 is an architecture diagram of a synchronization balance module. [Figure 5] FIG. 1 is an architecture diagram of an inverse transformation module. DETAILED DESCRIPTION OF THE INVENTION

[0017]

[0023] Hereinafter, with reference to the drawings of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely. It is obvious that the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be understood that these descriptions are for illustrative purposes only and do not limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and techniques will be omitted to avoid unnecessary confusion of the concept of the present invention.

[0018] As shown in FIG. 1, the method for controlling a series-connected motor includes the following steps 1 to 5: Step 1: Connect n motor axes in series and collect the basic parameters of each motor. The basic parameters of each motor, including the angular displacement, angular velocity, rotational speed and phase current of the rotating shaft, are acquired by a resolver; Step 2: Synthesize the acquired motor basic parameters into a voltage space vector reference value; Calculating the voltage space vector reference value by a calculation module; Step 3: Obtain the desired voltage output vector through the hybrid control algorithm and the voltage space vector reference value; Utilizing an inverse transformation module to obtain the desired voltage output vector; Step 4: Synchronize the rotation speeds of the motors connected in series and balance the load. A synchronous balancing module is used to maintain synchronized rotation speeds and balanced loads of each of the motors connected in series with respect to the shafts, the synchronous balancing module includes a rotation speed synchronization unit and an energy-saving load unit, the rotation speed synchronization unit uses an inverse closed-loop rotation speed algorithm to synchronize the rotation speeds of each of the motors connected in series with respect to the shafts, the energy-saving load unit uses an energy consumption balancing algorithm to balance the loads and energy consumptions of each of the motors connected in series with respect to the shafts according to the basic parameters of each motor, the output terminal of the rotation speed synchronization unit is connected to the input terminal of the energy-saving load unit, Step 5: Monitor the operation process of each motor in series connection in real time, and respond promptly to emergencies. The monitoring feedback module is used to realize real-time monitoring and fault response measures for each motor connected in series with the shaft.

[0019] The output end of the resolver is connected to the input end of a calculation module, the output end of the calculation module is connected to the input end of an inverter module, the output end of the inverter module is connected to the input end of a synchronous balance module, and the output end of the synchronous balance module is connected to the input end of a monitoring feedback module.

[0020] Furthermore, the calculation module includes a conversion unit and an adjustment unit, wherein the conversion unit converts the acquired actual value coordinates of the motor phase current into current components of each axis of the motor, and the adjustment unit converts the errors between the current components of each axis of the motor and the zero value into voltage components of each axis of the motor using a PID algorithm, and the output terminal of the conversion unit is connected to the input terminal of the adjustment unit.

[0021] The monitoring and feedback module further includes a fault diagnosis unit, a countermeasure protection unit, an emergency braking unit, and an energy feedback unit. The fault diagnosis unit uses an abnormality detection algorithm to detect the acquired basic parameters and desired voltage output vector values ​​of each motor. The countermeasure protection unit combines and selects a fault resolution processing method using a stake selection algorithm. The emergency braking unit emergency brakes the faulty motor by handbrake. The energy feedback unit recovers and reuses energy after emergency braking using a hybrid feedback algorithm based on FOC and MPPT. The output terminal of the fault diagnosis unit is connected to the input terminal of the countermeasure protection unit. The output terminal of the countermeasure protection unit is connected to the input terminal of the emergency braking unit. The output terminal of the emergency braking unit is connected to the input terminal of the energy feedback unit.

[0022] In a specific embodiment, the operation principle of the monitoring feedback module is as follows: Detect the basic parameters and desired voltage output vector value of each motor using an abnormality detection algorithm and output the detection result; If a fault occurs, output corresponding fault information; Select the most appropriate method from the fault resolution processing methods using a stake selection algorithm based on the output result of the fault diagnosis unit; After selecting the method, output it to the next unit for processing; Receive the method output by the countermeasure protection unit and use the handbrake to brake the faulty motor immediately, thereby avoiding more serious motor failure; Finally, use a hybrid feedback algorithm based on FOC and MPPT to recover and reuse energy after emergency braking.

[0023] Furthermore, the inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit, wherein the rotation-stationary unit rotates the voltage components of each shaft of the motor by 6 / 2 and 2s / 2r and inversely transforms them into coordinate axis components of the two-phase stationary coordinate system, and the amplitude-phase positioning unit calculates the amplitude and phase of the reference value in the two-phase stationary coordinate system using a hybrid control algorithm, and determines the sector and required voltage space vector according to the phase, and the output terminal of the rotation-stationary unit is connected to the input terminal of the amplitude-phase positioning unit.

[0024] Furthermore, the operation method of the inverse closed-loop rotation speed algorithm is as follows: first, a mathematical model of the motor is created based on the back electromotive force, the inductance and resistance of the motor; then, the motor's back electromotive force is used to predict the motor's output characteristics, which are then used in the current control loop; subsequently, a speed control loop is added based on the current control loop; and finally, the motor's output rotation speed is adjusted in real time by the speed control circuit.

[0025] In a specific embodiment, the operating principle of the inverse closed-loop rotational speed algorithm is as follows: A mathematical model of the motor is created, including parameters such as back electromotive force and the motor's inductance and resistance. The mathematical model accurately analyzes the motor's characteristics and serves as the basis for creating a control algorithm. During the control process, the motor's back electromotive force is used to predict the motor's output characteristics, which are then used in the current control circuit to achieve predictable motor control. A speed control loop is added based on the current control loop, achieving double closed-loop control. The speed control circuit adjusts the motor's output rotational speed in real time to ensure accurate matching between the motor's output rotational speed and the target rotational speed. By adjusting the control parameters, the motor system is optimized and more accurate control results are achieved. As shown in Table 1, repeated experimentation and adjustment is required depending on the motor's actual conditions until the optimal control effect is achieved.

[0026] Table 1 Rotation speed adjustment control table JPEG2025531007000010.jpg8399

[0027] As can be seen from the table above, the three rotational speed regulation control algorithms are back electromotive force, double closed loop and reverse closed loop rotational speed, respectively. The target rotational speed of each of them is 3000 r / min, but there are certain differences in the adjustment time, synchronization rate, actual rotational speed, etc.

[0028] 1. Back electromotive force regulation control: The regulation time is relatively long, 186 ms. The synchronization rate is 81.3%, which indicates that the control effect is general. The actual rotation speed is 2439 r / min, which is far from the target rotation speed.

[0029] 2. Double closed-loop regulation control: The regulation time is relatively short at 152ms. The synchronization rate is 89.6%, which indicates a relatively good control effect. The actual rotation speed is 2688r / min, which is close to the target rotation speed.

[0030] 3. Reverse closed-loop speed regulation control: The regulation time is the shortest at 27 ms. The synchronization rate is the highest at 98.7%, which indicates the best control effect. The actual speed is closest to the target speed at 2961 r / min.

[0031] Overall, compared with the other two control algorithms, the inverse closed-loop rotation speed regulation control has advantages in terms of regulation time, synchronization rate, and actual rotation speed.

[0032] Furthermore, the hybrid control algorithm operates as follows: first, the actual current value is compared with the target current value to obtain a current error signal, which is used 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; this difference is then modulated with a triangular wave to form a PWM carrier signal; and finally, the PWM reference signal is compared with the PWM carrier signal to adjust the current value so that it is always within the error range.

[0033] In a specific embodiment, the principle of the hybrid control algorithm is as follows: First, current hysteresis control is performed, i.e., high-frequency adjustment is performed to ensure that the current always remains within a predetermined range without jumping or oscillating. Specifically, the actual current value is compared with the target current value to obtain an error signal, and then the controller outputs a corresponding control signal to ensure that the current value always remains within the error range. The AC power supply for driving the motor is controlled using a PWM method. Specifically, the power supply voltage is compared and sampled to obtain the difference between the reference voltage value and the actual voltage value. This difference is then modulated with a triangular wave to form a PWM carrier signal to control the current size and flow direction. Combining current hysteresis control and PWM carrier control to form a hybrid control strategy achieves more accurate motor control. In the hybrid control method, the error signal from current hysteresis control is used as the reference signal for the PWM controller, thereby achieving accurate motor drive within the error range. The hybrid control strategy is implemented by the control system to control the motor's operation. As a realization method, as shown in Table 2, an embedded unit or a DSP processor is used to realize a hybrid control algorithm of current hysteresis control and PWM carrier control through programming, which drives precise control of the output voltage and current of the motor power supply.

[0034] Table 2 Motor parameter adjustment table JPEG2025531007000011.jpg4197

[0035] As can be seen from the table above, the three motor parameter adjustment control algorithms are current hysteresis, PWM and hybrid control, respectively. They have certain differences in current, voltage, desired voltage, etc.

[0036] 1. Current hysteresis adjustment control: The algorithm current is 24.6A, voltage is 30.5V, desired voltage is 30V, and adjustment time is 113ms.

[0037] 2. PWM regulation control: The algorithm current is 28.4A, voltage is 30.7V, desired voltage is 30V, and regulation time is 84ms.

[0038] 3. Adjustment control of hybrid control: The current of the algorithm is 20.8A, the voltage is 30.1V, the desired voltage is 30V, and the adjustment time is 28ms.

[0039] Overall, the hybrid control algorithm shows excellent results in terms of current, voltage and desired voltage, and has the shortest adjustment time, 28 ms.

[0040] Furthermore, the method of operation of the energy consumption balance algorithm is as follows: First, the basic parameters of each motor are converted into a matrix format. JPEG2025531007000012.jpg1652In equation (1), X * is the parameter determinant, U is the interference matrix, G is the composite coefficient matrix, σ is the main coefficient, G m is the composite effect value, m is the number of columns, Transforming surface features of the parameter data into depth features by a data transformation function, the data transformation function comprising: JPEG2025531007000013.jpg453, In equation (2), Y # is the data depth feature, X * represents the data surface feature, B represents the transformation parameter, H represents the data dimension parameter, and T represents the error adjustment parameter. The acquired data depth features are trained 180 times to obtain the basic model weight parameters, and prediction classification is performed based on real-time data. The classification function is: It is represented as JPEG2025531007000014.jpg547, In equation (3), E V represents the correlation between real-time data and forecast data, V represents the adjustment period, To avoid overfitting during the training process, we limit the training process parameters, and the limiting function is It is represented as JPEG2025531007000015.jpg556, In equation (4), JPEG2025531007000016.jpg43 is the limit variable of the training process parameters, X V is the capture information of the training process during the training period, X T represents the data dynamic information within the training period, By comparing the change patterns of load and energy consumption between two adjacent periods, we determine whether the weights of the training process parameters are optimal. It is represented as JPEG2025531007000017.jpg652, In equation (5), JPEG2025531007000018.jpg43 is the parameter information obtained during the training period. JPEG2025531007000019.jpg45Parameter information collected in adjacent periods, ε represents the difference coefficient between two adjacent periods.

[0041] In a specific embodiment, the energy consumption balancing algorithm operates as follows: the system detects and records the energy consumption of each motor, including its real-time energy consumption and historical average energy consumption. When the energy consumption of a motor in the system exceeds a set value or an abnormal situation such as overload occurs, the energy consumption balancing algorithm transfers the excess energy to other motors below the set value using a capacitor or the like. The energy consumption balancing algorithm monitors and analyzes the energy consumption situation to manage and adjust the energy consumption of the system in different ways, thereby achieving a balanced energy consumption of the system. In practical applications, the energy consumption balancing algorithm typically 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 transfers the excess energy to other motors, as shown in Table 3, thereby achieving a balanced energy consumption of the system.

[0042] Table 3: Energy consumption allocation comparison table JPEG2025531007000020.jpg63150

[0043] As can be seen from the table above, the system energy consumption allocation using different algorithms is compared in two cases: no energy consumption allocation and energy consumption balance. A, B, C, and D represent the energy consumption values ​​of the four motors, Frag / part represents the size of each fragment, and Effect / % represents the effectiveness improvement rate of the energy consumption balance algorithm compared to no energy consumption allocation.

[0044] 1. Without energy consumption allocation, the energy consumption values ​​of the four motors are 202, 173, 127, and 186, respectively, showing some imbalance. The size of each fragment is 75, and the effectiveness is 81.4%.

[0045] 2. In the case of energy consumption balancing, the energy consumption values ​​of the four motors are evenly allocated to 204, 198, 208, and 194 respectively, with an energy consumption balancing effect of 99.7%. It can be seen that by rationally allocating energy using the energy consumption balancing algorithm, a better balance can be achieved between the energy consumption values ​​of the motors, and the system energy utilization efficiency can be improved.

[0046] Furthermore, the operation method of the stake 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 the new solution by calculating the fault response; when the hash value of the new solution meets the normal operating requirements of the motor and the trusted node detects an abnormality, it reaches a consensus through multiple negotiations, which is completed in a short time, and confirms the addition of the new solution; subsequently, after collecting enough new solutions, the node needs to select from these new solutions; finally, it adjusts the subsequent fault difficulty value based on the stake selection result and the length of the stake selection time each time.

[0047] In a specific embodiment, the pseudocode for the stake selection algorithm is: / / Set the initial failure difficulty value to 0 difficulty=0 / / The node's right to calculate the fault response and generate a new solution if (calculate_error() == true){ privilege = true } / / When a trusted node detects an anomaly, it reaches a consensus through multiple negotiations, which are completed in a short time. if(detect_exception()== true){ for(i= 0;i <iterations; i++){ consensus = negotiate() if(consensus == true){ new_solution = generate_solution() } } add_solution(new_solution) } / / After collecting enough new solutions, the node needs to choose among these new solutions if(collect_solutions()== true){ selected_solution = select_solution() / / Adjust the subsequent failure difficulty value based on the outcome of each stake selection and the length of the stake selection time. adjust_difficulty(selected_solution, selection_time) }

[0048] While specific embodiments of the present invention have been described above, it should be understood by those skilled in the art that these specific embodiments are merely exemplary, and that those skilled in the art can make various omissions, substitutions, and changes to the details of the above-described method and system without departing from the principles and essence of the present invention. For example, it is within the scope of the present invention to combine steps of the above-described method to perform substantially the same function in substantially the same way so as to achieve substantially the same result. Accordingly, the scope of the present invention is limited only by the appended claims.

Claims

1. A method for controlling a series-connected motor, comprising the following steps 1 to 5: Step 1: Connect the axes of n motors in series and collect the basic parameters of each motor. The basic parameters of each motor, including the angular displacement, angular velocity, rotational speed and phase current of the rotating shaft, are acquired by a resolver; Step 2: Synthesize the acquired basic parameters of the motor into a voltage space vector reference value; Calculating the voltage space vector reference value by a calculation module; Step 3: Obtain the desired voltage output vector through the hybrid control algorithm and the voltage space vector reference value; Utilizing an inverse transformation module to obtain the desired voltage output vector; Step 4: Synchronize the rotation speeds of the motors connected in series and balance the load. A synchronous balancing module is used to maintain synchronized rotation speeds and balanced loads of each of the motors connected in series with respect to the shafts, the synchronous balancing module includes a rotation speed synchronization unit and an energy-saving load unit, the rotation speed synchronization unit uses an inverse closed-loop rotation speed algorithm to synchronize the rotation speeds of each of the motors connected in series with respect to the shafts, the energy-saving load unit uses an energy consumption balancing algorithm to balance the loads and energy consumptions of each of the motors connected in series with respect to the shafts according to the basic parameters of each motor, the output terminal of the rotation speed synchronization unit is connected to the input terminal of the energy-saving load unit, Step 5: Monitor the operation process of each motor connected in series in real time and respond promptly to emergencies. Real-time monitoring and fault response measures for each motor connected in series are realized using a monitoring feedback module. The energy consumption balance algorithm operates as follows: First, the basic parameters of each motor are converted into a matrix format. （１） In formula (1), X ＊ is the parameter determinant, U is the interference matrix, G is the composite coefficient matrix, σ is the main coefficient, G ｍ represents the composite effect value, m represents the number of columns, Transforming surface features of the parameter data into depth features by a data transformation function, the data transformation function comprising: (2) In formula (2), Y ＃ is the data depth feature, X ＊ represents the data surface feature, B represents the transformation parameter, H represents the data dimension parameter, and T represents the error adjustment parameter. The acquired data depth features are trained 180 times to obtain the basic model weight parameters, and prediction classification is performed based on real-time data. The classification function is: (3) In formula (3), E Ｖ represents the correlation between real-time data and forecast data, V represents the adjustment period, To avoid overfitting during the training process, we limit the training process parameters, and the limiting function is (4) In formula (4), is the limiting variable of the training process parameters, X Ｖ is the capture information of the training process during the training period, X Ｔ represents the data dynamic information within the training period, By comparing the change patterns of load and energy consumption between two adjacent periods, it is determined whether the weights of the training process parameters are optimal. (5) In formula (5), is the parameter information obtained during the training period, The method for controlling a series-connected motor, wherein the parameter information collected in adjacent time periods, ε represents a difference coefficient between two adjacent time periods.

2. 2. The method for controlling a series-connected motor according to claim 1, wherein the calculation module includes a conversion unit and an adjustment unit, the conversion unit converts the acquired actual value coordinates of the phase current of the motor into current components of each axis of the motor, the adjustment unit converts the errors between the current components of each axis of the motor and the zero value into voltage components of each axis of the motor using a PID algorithm, and the output terminal of the conversion unit is connected to the input terminal of the adjustment unit.

3. 2. The method for controlling series-connected motors according to claim 1, wherein the monitoring and feedback module includes a fault diagnosis unit, a countermeasure protection unit, an emergency braking unit, and an energy feedback unit, wherein the fault diagnosis unit uses an abnormality detection algorithm to detect the acquired basic parameters and desired voltage output vector values ​​of each motor, the countermeasure protection unit combines and selects a fault resolution processing method using a stake selection algorithm, the emergency braking unit uses a handbrake to emergency brake the faulty motor, and the energy feedback unit uses a hybrid feedback algorithm based on FOC and MPPT to recover and reuse energy after emergency braking, and the output terminal of the fault diagnosis unit is connected to the input terminal of the countermeasure protection unit, the output terminal of the countermeasure protection unit is connected to the input terminal of the emergency braking unit, and the output terminal of the emergency braking unit is connected to the input terminal of the energy feedback unit.

4. 2. The method for controlling a series-connected motor according to claim 1, wherein the inverse transformation module includes a rotation-stationary unit and an amplitude-phase positioning unit, the rotation-stationary unit rotates the voltage components of each shaft of the motor by 6 / 2 and 2s / 2r and inversely transforms them into coordinate axis components of the two-phase stationary coordinate system, the amplitude-phase positioning unit calculates the amplitude and phase of the reference value in the two-phase stationary coordinate system using a hybrid control algorithm, and determines the sector and required voltage space vector according to the phase, and the output terminal of the rotation-stationary unit is connected to the input terminal of the amplitude-phase positioning unit.

5. 2. The method for controlling a series-connected motor according to claim 1, wherein the inverse closed-loop rotation speed algorithm operates as follows: first, a mathematical model of the motor is created based on the back electromotive force, the inductance and resistance of the motor; second, the motor's back electromotive force is used to predict the motor's output characteristics, which are then used in the current control loop; subsequently, a speed control loop is added based on the current control loop; and finally, the motor's output rotation speed is adjusted in real time by the speed control circuit.

6. 5. The method for controlling a series-connected motor according to claim 4, wherein the hybrid control algorithm operates by first comparing the actual current value with the target current value to obtain a current error signal, which is used as a PWM reference signal; then comparing and sampling the power supply voltage to obtain the difference between the reference voltage value and the actual voltage value; and then modulating the difference with a triangular wave to form a PWM carrier signal. Finally, the PWM reference signal and the PWM carrier signal are compared and adjusted so that the current value is always within the error range.

7. 4. The method for controlling a series-connected motor according to claim 3, wherein the operation method of the stake selection algorithm is as follows: first, set the initial fault difficulty value to 0 to allow the node to generate a new solution; then, the node obtains the right to add the new solution by calculating a fault response; when the hash value of the new solution meets the normal operating requirements of the motor and the trusted node detects that an abnormality has been found, a consensus is reached through multiple negotiations, which are completed in a short time, and the addition of the new solution is confirmed; subsequently, after collecting enough new solutions, the node needs to select from these new solutions; finally, adjust the subsequent fault difficulty value according to the stake selection result and the length of the stake selection time each time.

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