Multi-motor dynamic synchronous regulation method based on virtual total shaft state feedback

Abstract

The application discloses a kind of multi-motor dynamic synchronous regulation and control methods based on virtual total shaft state feedback, comprising: constructing multi-motor virtual total shaft control model, through virtual main shaft output setting expected traction torque value and setting each physical motor as follow motor;According to the state data of each motor collected, the state and disturbance of each motor are jointly probabilistically modeled using a pre-set modeling method, to generate the estimated mean and covariance of each motor state;Design a nonsingular terminal sliding surface and calculate the control signal;Set the soft saturation buffer range and construct a soft switching function, buffer the control signal and output the modified control signal;According to the modified control signal, the synchronization control of multi-motor traction output is realized.The multi-motor dynamic synchronous regulation and control method of the application can improve the synchronization stability, anti-interference ability and output smoothness in complex dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, specifically to a method and system for dynamic synchronous control of multiple motors based on virtual shaft state feedback. Background Technology

[0002] High-speed trains are a vital pillar of China's economy and the preferred mode of transportation for most people; therefore, their safety is paramount. Traction system control technology is crucial for the safe operation of high-speed trains. (Refer to...) Figure 1 As shown, the control hierarchy of a high-speed train traction system can be mainly divided into three levels: train-level control, vehicle-level control, and transmission-level control. Train-level control refers to the overall control of the entire train. Commands issued by the driver are transmitted to each power unit through train-level control, enabling control of the train's traction, braking, acceleration, and deceleration, while also providing safety protection and diagnostics. Vehicle-level control mainly optimizes adhesion control, brake force distribution, and provides diagnostics and protection. It primarily processes traction and braking forces before sending them to the four-quadrant control and inverter control devices. Transmission-level control involves the control, diagnosis, and monitoring of the four-quadrant control and inverter control devices.

[0003] For locomotives powered by multiple traction motors, maintaining dynamic stability of the total traction power during operation is a prerequisite for ensuring stable locomotive operation. To address train safety issues caused by sudden wheelset slippage or other malfunctions, a control strategy is needed that allows other healthy wheelsets to handle the traction power within their capacity when a wheelset malfunctions, preventing further failure and ensuring safe train operation. This necessitates research into a coordinated control strategy for the total traction power of multiple motors based on a virtual axle.

[0004] Currently, the key to the coordinated control strategy of multi-motor traction power based on virtual axle lies in utilizing the feedback mechanism of the virtual axle synchronous control strategy to ensure that the total traction power of each motor remains consistent with the system's given value when the train is running stably. When a fault such as wheelset slip occurs, the virtual axle can detect the fault in a timely manner through its feedback mechanism and adjust the total traction power of the motor accordingly. This ensures the dynamic balance of the total traction power while preventing each motor from operating under overload, thus minimizing the adverse effects of wheelset slip and other faults on train safety performance.

[0005] However, existing multi-motor cooperative control strategies mainly focus on making speed, position and other states consistent. Due to the complex system working environment, frequent load disturbances and inconsistent parameters between motors, problems such as fluctuations in the synchronization error of multi-motor states affecting the overall consistency of traction power, actuator limitations causing nonlinear saturation behavior, and traditional sliding mode control easily inducing chattering in the saturation region, reducing stability and response quality are easily caused.

[0006] Therefore, there is an urgent need for a multi-motor dynamic synchronization control method that can improve synchronization stability, anti-interference ability, and output smoothness in complex dynamic environments. Summary of the Invention

[0007] Purpose of the invention: In order to overcome the above shortcomings, the purpose of this invention is to provide a multi-motor dynamic synchronous control method and system based on virtual total shaft state feedback, which can improve the output consistency and synchronous control accuracy of multi-motors and enhance the anti-interference ability of the system. It can also realize soft buffer passivation processing of control signals near physical boundaries, effectively suppressing system oscillation and weakening nonlinear surge effects.

[0008] To address the aforementioned technical problems, this invention provides a multi-motor dynamic synchronous control method based on virtual total shaft state feedback, comprising:

[0009] S1: Construct a multi-motor virtual main shaft control model, set the desired traction torque value through the output of the virtual main shaft and set each physical motor as a follower motor, so as to achieve coordinated and consistent total traction.

[0010] S2: Based on the collected state data of each motor, a preset modeling method is used to perform joint probability modeling of the state and disturbance of each motor, and generate the estimated mean and covariance of each motor state;

[0011] S3: Design a non-singular terminal sliding surface and calculate the control signal based on the multi-motor virtual total shaft control model and feedback control law;

[0012] S4: Set the soft saturation buffer range and construct a soft switching function to buffer and correct the control signal and output the corrected control signal;

[0013] S5: Synchronous control of multi-motor traction output is achieved based on the corrected control signal.

[0014] In a preferred embodiment of the present invention, in S1, the virtual spindle in the multi-motor virtual spindle control framework is not an actual physical motor. It sets the traction reference torque trajectory through control logic and feeds back the torque values ​​of each following motor in real time as the virtual spindle load feedback.

[0015] In a preferred embodiment of the present invention, in S1, the method includes:

[0016] S11: Set the dynamics of the virtual spindle as follows: Then, based on the target trajectory, a control law is designed to make the output of the virtual spindle achieve: ,in, The equivalent moment of inertia of the virtual principal axis. For virtual spindle angular velocity, To output driving torque to the controller, This is the sum of the traction torques of all following motors. The set desired traction torque value;

[0017] S12: Make the sum of the outputs of all follower motors equal to the set desired traction torque value. And introduce a total error term: ,in Let be the traction torque of the j-th motor;

[0018] S13: Set the capability weight for each follower motor: Then the output of each motor is: And introduce control error: , Let be the error between the expected traction torque and the output torque of the j-th motor.

[0019] In a preferred embodiment of the present invention, in S13, the capability weight of each following motor is dynamically updated by combining the control error with the current estimated state of the motor. , ,in Let be the estimated torque error value of the j-th motor. The uncertainty in estimating the motor output torque. This is the weight decay factor. Let be the instantaneous availability index of the j-th motor.

[0020] In a preferred embodiment of the present invention, in S2, the method includes:

[0021] S21: Within each control cycle, collect the actual speed, output torque, and control error of each motor to construct a joint state vector: ,in, Let be the rotational speed of the j-th motor;

[0022] S22: Model the joint state vector using Bayesian or Gaussian process regression methods:

[0023] ,in The mean of the state estimate, , Let be the state covariance matrix. .

[0024] In a preferred embodiment of the present invention, in S3, the method includes:

[0025] S31: Define the sliding surface: ,in, The rate of change of error, For adjusting the sliding surface gain, To prevent odd positive integers;

[0026] S32: Feedback control law calculates the control signal: ,in For equivalent control items,

[0027] , To control the input gain, Given the nonlinear model terms, The derivative of the reference trajectory used for feedforward compensation;

[0028] This is a compensation term used to resist modeling errors and disturbances. , Based on fixed gain, An adaptive adjustment coefficient is used to balance the intensity of uncertainty with the strength of control. This represents the total uncertainty of all state variables.

[0029] In a preferred embodiment of the present invention, in S4, the method includes:

[0030] S41: Defines the width of the soft saturation buffer used to determine the transition zone near the boundary: This allows setting the soft saturation buffer range: ;

[0031] S42: Defines the saturation limit value used to determine the current saturation limit near the boundary:

[0032]

[0033] S43: Constructing a buffer weight function using a Sigmoid form: , where α is the buffer slope control coefficient;

[0034] S44: Buffer and correct the control signal and output the corrected control signal.

[0035] , among which when Near the border, As the value approaches 1, the output of the control signal gradually approaches the boundary but does not overshoot. Far from the border, When the value approaches 0, the control signal remains unaffected.

[0036] A method for dynamic synchronous control of multiple motors based on virtual total shaft state feedback, with soft saturation buffer width. Set to 5% to 20% of the maximum control signal amplitude, and the saturation limit value As one of the saturation boundaries that the control signal is about to reach, when hour, ,when hour, .

[0037] In a preferred embodiment of the present invention, in S41, the method includes:

[0038] S411: Extracting Compensation Items The total uncertainty of all state variables ;

[0039] S412: The total uncertainty of all state variables As a dynamic indicator, the buffer width is automatically expanded. ,in This is the initial buffer width. To dynamically expand the upper limit, To adjust the slope factor.

[0040] This application also provides a multi-motor dynamic synchronous control system based on virtual total shaft state feedback using the aforementioned method, comprising:

[0041] The overall framework module is used to build a multi-motor virtual master shaft control model. It sets the desired traction torque value through the output of the virtual master shaft and sets each physical motor as a follower motor, thereby achieving coordinated and consistent total traction.

[0042] The joint distribution module is used to perform joint probability modeling of the state and disturbance of each motor based on the collected state data of each motor and to generate the estimated mean and covariance of each motor state.

[0043] The non-singular control module is used to design the non-singular terminal sliding surface and calculate the control signal based on the multi-motor virtual total shaft control model and feedback control law.

[0044] The soft saturation buffer module is used to set the range of the soft saturation buffer and construct a soft switching function to buffer and correct the control signal and output the corrected control signal.

[0045] The control execution module is used to achieve synchronous control of the traction output of multiple motors based on the modified control signal.

[0046] The technical solution described in this application has the following advantages over the prior art:

[0047] 1. By constructing a virtual master shaft control framework, the desired traction output is uniformly set, and each actual motor is modeled as a following unit, enabling multi-motor collaborative drive to have global consistency scheduling capability; and by introducing a capability weight adaptive redistribution mechanism, the target output ratio of the motor is automatically adjusted in real time according to the motor health status and output stability, improving the system's dynamic balance capability and fault tolerance performance under individual motor performance degradation or limited working conditions.

[0048] 2. By adopting a non-singular terminal sliding mode design and combining the uncertainty information of system state estimation, the controller gain is dynamically adjusted so that the system can maintain stable and fast response performance under external disturbances or increased model deviations, thus avoiding the singularity problem of sliding mode control.

[0049] 3. A soft saturation buffer mechanism based on the Sigmoid function was constructed to achieve continuous and smooth signal passivation when the control signal approaches the boundary, effectively suppressing system oscillation, weakening nonlinear surge effects, extending actuator life, and improving system stability. Furthermore, using the joint state covariance matrix as the core index, it simultaneously drives sliding mode gain adjustment and soft saturation buffer adjustment to achieve dynamic adaptation based on uncertainty. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the hierarchical control framework for the existing high-speed train traction system.

[0052] Figure 2 This is a schematic diagram illustrating the basic working principle of the virtual main shaft provided in this embodiment of the invention.

[0053] Figure 3 This is a schematic diagram of the virtual total axis control framework provided in an embodiment of the present invention.

[0054] Figure 4 This is a schematic diagram of the sliding mode variable structure control phase plane provided in an embodiment of the present invention.

[0055] Figure 5 This is a schematic diagram of the multi-motor dynamic synchronous control method provided in the embodiments of the present invention.

[0056] Figure 6 This is a schematic diagram of the module connection of the multi-motor dynamic synchronous control system provided in an embodiment of the present invention.

[0057] Figure 7 This is a schematic diagram of the coordinated and consistent framework of the total traction of the virtual main shaft provided in the embodiment of the present invention.

[0058] Figure 8 This is a schematic diagram of the MATLAB simulation model provided in the embodiment of the present invention.

[0059] Figure 9 This is a schematic diagram of a pulse interference signal provided in an embodiment of the present invention.

[0060] Figure 10 This is a schematic diagram of a high-frequency interference signal provided in an embodiment of the present invention.

[0061] Figure 11 This is a schematic diagram illustrating the tracking effect and tracking error of the virtual controller provided in an embodiment of the present invention.

[0062] Figure 12 This is a schematic diagram illustrating the controller effect and tracking error of total coordination and consistency provided in the embodiments of the present invention.

[0063] Figure 13 This is a schematic diagram of the system output torque and tracking error provided in an embodiment of the present invention.

[0064] Figure 14 This is a schematic diagram of the system given torque, virtual controller output, and output torque of each motor provided in the embodiments of the present invention.

[0065] Figure 15 This is a schematic diagram of the total torque tracking and tracking error of the system under sudden load provided in an embodiment of the present invention.

[0066] Figure 16 This is a schematic diagram of the RT-LAB experimental platform provided in an embodiment of the present invention.

[0067] Figure 17 This is a schematic diagram of the virtual controller tracking effect provided in an embodiment of the present invention.

[0068] Figure 18 This is a schematic diagram illustrating the effect of the total coordination and consistency controller provided in an embodiment of the present invention.

[0069] Figure 19 This is a schematic diagram of the system tracking effect provided in an embodiment of the present invention. Detailed Implementation

[0070] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0071] Multi-motor synchronous control strategies mainly include parallel control, master-slave control, cross-coupling control, adjacent cross-coupling control, and virtual common shaft control. Parallel control was the earliest proposed strategy in multi-motor synchronous control research. In this strategy, all motors operate in parallel, ensuring that each motor can receive and track the command signal stably. Master-slave control uses one motor as the master motor and the others as slave motors. Cross-coupling control calculates the difference between the output values ​​of two motors and feeds the differential signals back to their respective feedforward channels, where they act on the two motors together with the input signal. Adjacent cross-coupling control improves upon cross-coupling control, making it applicable to multi-motor synchronous control systems. This strategy calculates the difference between the output values ​​of adjacent motors in the multi-motor system and feeds them back to their respective feedforward channels, where they act on the two adjacent motors together with the input signal. The Electronic Line Shafting (ELS) virtual spindle control strategy introduces overall closed-loop feedback control on top of the master-slave control structure. It replaces the real spindle with a simulated mechanical spindle, providing feedback on the speed or position of each motor while simultaneously applying the torque of each motor as total feedback to the virtual spindle. If a motor is disturbed, the virtual spindle can promptly detect this change through torque feedback. The virtual controller adjusts the output value of the virtual spindle, thereby adjusting the output values ​​of the other motors to achieve synchronous control.

[0072] In practical multi-motor control systems, the controller controls the controlled object through actuators. However, due to their physical limitations and the application environment, actuators can experience saturation, constraining their output. When an actuator saturates, the control signal cannot fully act on the controlled object, preventing proper feedback and leading to a decline in controller performance. Furthermore, the difference in controller state before and after actuator saturation can cause partial loss of control within the system, further degrading system performance, increasing settling time, and ultimately affecting the accuracy of the entire multi-motor system control. If the actuator remains in a saturated state for an extended period, it can even cause system collapse and other serious consequences. During normal system operation... When the controller output is fully applied to the controlled object, but when the actuator is saturated, At this time, the controller output cannot fully act on the controlled object, which will lead to system disorder, excessive adjustment time and other phenomena.

[0073] The basic working principle of the virtual spindle control method is as follows: Figure 2As shown, after a control signal is applied to the virtual axis simulated in the system, the virtual axis begins to move and drives n moving axes to move together through the driving torque. At the same time, the load torque borne by the n moving axes is also fed back to the virtual axis to balance the driving torque of the virtual axis.

[0074] The core of the virtual axis control strategy is its feedback mechanism. (By...) Figure 3 It can be seen that the closed-loop system is given by the system signal θ d Virtual main shaft, controller j (j=1,2,...,n) and multiple motors j (j=1,2,...,n), θ ref T represents the output angular displacement of the virtual spindle, and T represents the drive torque of the virtual controller. For the virtual total shaft load torque, θ j (j=1,2,...,n) represents the output angular displacement of the j-th motor, T Lj (j=1,2,...,n) represents the load torque of the j-th motor. The virtual master shaft control structure, while providing feedback on the speed or position of each slave motor, can also apply the total torque of all slave motors as total feedback to the virtual master shaft. When the multi-motor system is in steady state, each slave shaft follows the virtual master shaft, achieving excellent synchronous control. When one or more slave shafts deviate from the reference value output by the virtual master shaft due to external disturbances, the virtual master shaft can promptly sense this change through torque feedback. The virtual controller then adjusts the output value of the virtual master shaft, thereby adjusting the output values ​​of other slave shafts, achieving synchronous control. When the multi-motor control system is stable, the system's dynamic equilibrium equation is:

[0075] ,in Let be the rotational inertia of the virtual master shaft. As can be seen from the equation, the virtual master shaft control strategy senses and adjusts the output angular displacement of the virtual master shaft in a timely manner through total torque feedback, and then adjusts the angular displacement of each slave shaft to achieve rapid synchronization and tracking of the given angular displacement among the slave shafts, thus ensuring the stability and robustness of the control system.

[0076] Non-singular terminal sliding mode variable structure control utilizes a variable structure controller to allow the system state to move from any initial position to a hyperplane determined by the switching function within a finite time, and maintain asymptotic motion on this hyperplane, which is called the sliding surface. For example... Figure 4As shown, in conventional sliding mode variable structure control, a linear sliding plane is typically chosen such that the state tracking error asymptotically converges to zero after the system state reaches the sliding surface (s(x)=0) from any position within a finite time. The convergence rate can be adjusted by selecting different sliding surface parameters. The system exhibits strong anti-interference capability when moving on the sliding surface. However, because the system's state tracking error cannot converge to zero within a finite time when moving on the sliding surface, this leads to an uncertain system settling time, which adversely affects the dynamic performance of the control system. To address this uncertainty in the motion time on the sliding surface, a nonlinear function is typically introduced into the sliding plane to construct a terminal sliding surface. This ensures that the system's state tracking error converges to zero within a finite time when moving on the sliding surface, thus shortening the system settling time and improving the dynamic performance of the control system.

[0077] Therefore, for reference Figure 5 As shown, in some embodiments, the multi-motor dynamic synchronous control method based on virtual total shaft state feedback includes:

[0078] S1: Construct a multi-motor virtual main shaft control model, set the desired traction torque value through the output of the virtual main shaft and set each physical motor as a follower motor, so as to achieve coordinated and consistent total traction.

[0079] The virtual spindle in the multi-motor virtual spindle control framework is not an actual physical motor. It sets the traction reference torque trajectory through control logic and feeds back the torque values ​​of each following motor in real time as the virtual spindle load feedback.

[0080] Specifically, in S1, the method includes:

[0081] S11: Set the dynamics of the virtual spindle as follows: Then, based on the target trajectory, a control law is designed to make the output of the virtual spindle achieve: ,in, The equivalent moment of inertia of the virtual spindle is set by the designer; For virtual spindle angular velocity, To output driving torque to the controller, This is the sum of the traction torques of all following motors. The set desired traction torque value.

[0082] S12: Make the sum of the outputs of all follower motors equal to the set desired traction torque value. ,Right now: And introduce a total error term: ,in Let be the traction torque of the j-th motor; where the task of total coordinated control is to ensure that: .

[0083] S13: To ensure consistency in total quantity, it is also necessary to design an allocation mechanism for each motor and set the capability weight for each follower motor: Therefore, the expected reference output for each motor is: And introduce control error: , Let be the error between the expected traction torque and the output torque of the j-th motor; where the controller design objective is to ensure that each →0, thus achieving consistency between local tracking and global total.

[0084] In this embodiment of the application, the weight It can be set according to motor specifications, current limits, thermal load capacity, etc.; therefore, the overall process described above is: set the target traction torque value. Construct a virtual motor state model and feed back the sum of the outputs of each motor, and calculate the reference output of each motor. And introduce control error .

[0085] S2: Based on the collected state data of each motor, a preset modeling method is used to perform joint probability modeling of the state and disturbance of each motor, generating the estimated mean and covariance of each motor state.

[0086] Specifically, in S2, the method includes:

[0087] S21: Within each control cycle, collect the actual speed, output torque, and control error of each motor to construct a joint state vector: ,in, Let be the rotational speed of the j-th motor;

[0088] S22: Model the joint state vector using Bayesian or Gaussian process regression methods:

[0089] ,in The mean of the state estimate, , Let be the state covariance matrix. .

[0090] In small-scale systems with low-dimensional states, Bayesian filtering methods, such as Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF), can be used to recursively estimate the system state and generate the system state equation.

[0091] , ,in The motor state transition matrix can be modeled empirically or linearly. To control the input matrix, To measure the output matrix, To control process noise, it must satisfy a Gaussian distribution; For measuring noise, the following condition must be met: ~N(0,R) j EKF / UKF estimates the joint state mean in real time through a two-step loop from prediction to update. Covariance .

[0092] When used for highly nonlinear, discrete sampling systems, Gaussian process regression (GPR) is employed. GPR is a nonparametric Bayesian model that uses state variables as multidimensional inputs to regress the mapping relationships between the internal states of the system.

[0093] in, It is a mean function, usually set to 0 or a linear function; For kernel functions, the RBF (Radial Basis Function) kernel is commonly used; The distribution is a Gaussian process distribution used to generate the state prediction distribution; using GPR does not require an accurate system model, and the state distribution is predicted directly based on historical observations.

[0094] Therefore, regardless of whether EKF, UKF, or GPR is used, the joint state distribution is ultimately obtained: .

[0095] Therefore, the overall process described above is as follows: A state estimation submodule is set up for each motor in the control system; the state is collected during the control cycle. Using the EKF or GPR model, we obtain .

[0096] S3: Design a non-singular terminal sliding surface and calculate the control signal based on the multi-motor virtual shaft control model and feedback control law.

[0097] Specifically, in S3, the method includes:

[0098] S31: Define the sliding surface: ,in, The rate of change of error, For adjusting the sliding surface gain, To prevent singular positive integers, satisfy the following condition: ; The control error decays nonlinearly, and the sliding surface is nonsingular, i.e., in... There will be no division by zero or infinity problems in the vicinity, which ensures that the error converges to zero in a finite amount of time.

[0099] S32: Feedback control law calculates the control signal: ,in This is an equivalent control term used to offset the known dynamic components of the system.

[0100] , To control the input gain, For known nonlinear model terms (such as back electromotive force, load). This is the derivative of the reference trajectory used for feedforward compensation.

[0101] The gain is a compensation term used to combat modeling errors and disturbances.

[0102] , Based on fixed gain, An adaptive adjustment coefficient is used to balance the intensity of uncertainty with the strength of control. This represents the total uncertainty of all state variables; the gain is automatically adjusted according to changes in state uncertainty, thereby achieving adaptive robust control.

[0103] Therefore, the overall process described above is as follows: the joint distribution of the state estimator outputs within the control cycle is extracted. Calculate the uncertainty index of the state Calculation error The rate of change of its difference And construct the sliding surface Construct equivalent control terms ,according to Adjust the robust compensation term to generate a control signal. .

[0104] S4: Set the soft saturation buffer range and construct a soft switching function to buffer and correct the control signal and output the corrected control signal.

[0105] In multi-motor or actuator systems, control signals (such as voltage, current, and PWM duty cycle) have physical output limitations. When the controller output approaches or exceeds these limits, the following can easily occur: actuator saturation, preventing the output from increasing further; sudden nonlinear changes in the system, triggering high-frequency oscillations; and abrupt changes in the control signal, leading to a decrease in dynamic response quality.

[0106] Specifically, in S4, the method includes:

[0107] S41: Defines the width of the soft saturation buffer used to determine the transition zone near the boundary: This allows setting the soft saturation buffer range: Wherein, the width of the soft saturation buffer Set to 5% to 20% of the maximum control signal amplitude.

[0108] S42: Defines the saturation limit value used to determine the current saturation limit near the boundary:

[0109]

[0110] Wherein, the saturation limit value As one of the saturation boundaries that the control signal output is about to reach, when hour, ,when hour, .

[0111] S43: Constructing a buffer weight function using a Sigmoid form: Where α is the buffer slope control coefficient; where when That is, when approaching the boundary, ;when That is, when approaching the boundary, .

[0112] S44: Buffer and correct the control signal and output the corrected control signal.

[0113] , among which when Near the border, As the value approaches 1, the output of the control signal gradually approaches the boundary but does not overshoot. Far from the border, When the value approaches 0, the control signal remains unaffected.

[0114] Therefore, the overall process described above is as follows: the controller generates the original control signal, determines whether the output is close to the saturation range, and calculates... Generate transition weights and output correction control signals. The drive actuator executes the correction control signal. .

[0115] S5: Synchronous control of multi-motor traction output is achieved based on the corrected control signal.

[0116] Specifically, in this application, a multi-motor virtual total shaft control model is used as the overall framework, and distributed state estimation, non-singular terminal sliding mode and soft saturation buffer adjustment design are introduced into it. The control links of each motor are uniformly scheduled through the virtual total shaft, and the amplitude of the control signal is dynamically adjusted in the soft saturation region to avoid drastic changes in control nonlinearity.

[0117] In some embodiments of this application, in S13, the control error is utilized. Based on the current estimated state of the motor, dynamically update the capability weights of each follower motor:

[0118] , ,in Let be the estimated torque error value of the j-th motor. Uncertainty in estimating motor output torque; This is a weight decay factor used to adjust the impact of error and uncertainty on capability assessment; Let be the instantaneous availability index of the j-th motor.

[0119] Specifically, for the j-th motor, the torque error estimate of that motor is used. and the uncertainty estimate of its output torque. Construct its immediate availability index The larger the motor error and the more uncertain the estimate, the lower its capability index. The smaller the value, the less suitable it is to undertake more output tasks at present; thus, when obtaining the capability index of all motors... Then, calculate the capacity weight of each motor. Then, the traction output target for each motor in the current cycle can be updated to... .

[0120] Therefore, when a motor has a large output error or high state uncertainty, its capability weight... Automatic reduction reduces the load on the motor, preventing abnormal motor performance from affecting the overall system. Motors with reliable state estimation and small errors have high capacity indices and automatically take on more output tasks, maintaining the stability of the total traction of the system. This enables adaptive load redistribution of multi-motor systems under dynamic operating conditions.

[0121] In some embodiments of this application, in S41, the method includes:

[0122] S411: Extracting Compensation Items The total uncertainty of all state variables .

[0123] S412: The total uncertainty of all state variables As a dynamic indicator, the buffer width is automatically expanded. ,in The initial buffer width is typically set to 5% of the maximum control amplitude. The upper limit for dynamic expansion is typically set to 10% to 15% of the maximum control amplitude. To adjust the slope factor and control the sensitivity of uncertainty to expansion, it is usually set to 5-15; thus, when the state uncertainty is small, When uncertainty increases, Automatic scaling convergence The buffer is widened to suppress the risk of control signals jumping when approaching the boundary.

[0124] Specifically, through this strategy and compensation items By combining these technologies, a dual-channel adaptive control mechanism based on the uncertainty of state estimation is realized. As a result, when the system faces severe disturbances in motor state, increased estimation error, or sensor fluctuations, the buffer will be appropriately expanded to avoid actuator overshoot or jitter caused by sudden changes in the control signal entering the saturation region.

[0125] In some embodiments, reference Figure 6 As shown, a multi-motor dynamic synchronous control system based on virtual total shaft state feedback using the method is also provided, comprising:

[0126] Overall framework module 101 is used to construct a multi-motor virtual main shaft control model. It sets the desired traction torque value through the output of the virtual main shaft and sets each physical motor as a follower motor, thereby achieving coordinated consistency of total traction.

[0127] The joint distribution module 102 is used to perform joint probability modeling of the state and disturbance of each motor based on the collected state data of each motor and using a preset modeling method to generate the estimated mean and covariance of each motor state.

[0128] Non-singular control module 103 is used to design non-singular terminal sliding surfaces and calculate control signals based on the multi-motor virtual total shaft control model and feedback control law;

[0129] The soft saturation buffer module 104 is used to set the range of the soft saturation buffer and construct a soft switching function to buffer and correct the control signal and output the corrected control signal.

[0130] The control execution module 105 is used to realize synchronous control of the traction output of multiple motors according to the modified control signal.

[0131] In some embodiments, a computer medium is also provided, on which a computer program is stored, the computer program being executed by a processor to implement the multi-motor dynamic synchronous control method based on virtual total shaft state feedback.

[0132] In some embodiments, a computer is also provided, including the aforementioned computer medium.

[0133] Therefore, for reference Figure 7 and Figure 8 As shown in the table below. To verify the effectiveness of the synchronous control method described in this application, mathematical simulation and hardware-in-the-loop simulation experiments were conducted. The motor parameters used in the simulation and experiments are shown in the table below:

[0134]

[0135] In the table, R is the motor resistance, L is the inductance, and b is the resistance. eq J is the equivalent viscous friction coefficient. eq k is the equivalent moment of inertia of the motor. m k is the motor torque coefficient. e k is the back electromotive force constant. t The gearbox transmission ratio is given. In both simulation and experiment, motor 1 with the smallest moment of inertia is selected as the virtual spindle.

[0136] To verify the control performance of a multi-motor system under acceleration, constant speed, and deceleration conditions, a time-varying reference command signal is given to the system. When t < 0.3s, the multi-motor system starts and is in acceleration; when 0.3s ≤ t ≤ 0.7s, the multi-motor system is in constant speed; and when 0.7s < t ≤ 1s, the multi-motor system is in deceleration. It is assumed that the motors start under parameter perturbation, i.e., the system's composite disturbance d... 2j ≠0. At 0.2s, an application such as... is applied to motor 2. Figure 9 The pulse interference signal shown; at 0.5s, an application such as... is applied to motor 3. Figure 10 The high-frequency interference signal shown. After the motor is disturbed, the system's control effect changes as follows: Figures 11 to 14 It means that among them Figure 11 This indicates the tracking performance of the virtual controller and its tracking error; Figure 12 This indicates the tracking effect and tracking error of the total quantity coordination and consistency controller; Figure 13 The figure shows the output torque of each motor in the system and the total traction torque; the graph shows the system tracking error. Figure 14 This diagram represents the system's given torque, the virtual total shaft output torque, and the output torque of each motor.

[0137] analyze Figure 11 It can be seen that the tracking error between the given reference torque and the output torque of the virtual spindle occurs at 0.2s and 0.5s. When the system experiences uncertain disturbances, the error value does not exceed 0.2%, and the settling time of the virtual controller does not exceed 0.002s. Analysis Figure 12 It can be seen that when disturbances occur in the system at 0.2s and 0.5s, the total coordinated tracking error does not exceed 0.02%, and the settling time of the total coordinated controller does not exceed 0.0002s. Analysis Figure 13It can be seen that when motor 1 is interfered with by a pulse signal at 0.2s, its output torque decreases. To ensure consistent total traction power, and under the action of the virtual shaft, the output torques of motors 2 and 3 increase within a certain range. When motor 2 is interfered with by a high-frequency signal at 0.5s, its output torque decreases. Similarly, under the action of the virtual shaft, the output torques of motors 1 and 3 increase within a certain range. Due to the braking command, the output torques of all three motors show a decreasing trend after 0.8s. After 0.97s, the output torques of motors 1 and 2 are zero. At this time, motor 3 adjusts its output torque to achieve the given torque value, maintaining consistent total traction power. Analysis Figure 13 It can be seen that under different signal interference conditions, the system tracking error does not exceed 0.2%, and the tracking time does not exceed 0.002s. Analysis Figure 14 As can be seen, during system operation, the sum of the output torques of the three motors is equal to the output torque value of the virtual controller and the system's given torque value. The simulation results show that this application can guarantee that the multi-motor system still has good robustness and convergence performance under parameter perturbations and unknown disturbances, and the obtained simulation waveforms are consistent with the theoretical analysis.

[0138] To ensure that the system simulation can accurately reflect actual operating conditions, based on the simulation described above, we assume that the train is running stably under conditions such as... Figure 13 During the constant-speed operation segment shown, the original load is increased by 50% at t=0.5s for a duration of 0.2s. Figure 15 Provide waveforms of the system's given torque and the motor's total output torque, along with the error value between them. Figure 15 As can be seen, when a sudden load is applied, the total output torque of the motor decreases slightly but quickly recovers to the system's given torque value, with an error value not exceeding 2%, and the controller adjustment time not exceeding 0.004s. Simulation results show that this application can ensure that the system has strong anti-load interference capability.

[0139] To make the system simulation process as close as possible to the actual engineering environment, a hardware-in-the-loop simulation experiment was conducted. The equipment used in the experiment was... Figure 16 The RT-LAB experimental platform shown consists of a DSP controller, an OP56000 simulator, connecting cables, and a host computer. During the experiment, the multi-motor traction power total coordination and consistency control system model based on a virtual central shaft, built in the Simulink environment, was first input into the RT-LAB experimental platform and run on the OP56000 simulator. Then, the multi-motor traction power total coordination and consistency controller model based on a virtual central shaft designed in this chapter was input into the DSP controller, and finally, the output results were obtained. The same parameters and conditions as in the mathematical simulation process described above were selected for the experiment.

[0140] analyze Figure 17It can be seen that the output torque of the virtual total shaft can track the given torque of the system very well. Analysis Figure 18 It can be seen that this controller can ensure that the total output torque of the multiple motors in the system is consistent with the output torque of the virtual total shaft. Analysis Figure 19 It can be seen that, under the action of the two controllers, the total output torque of the multiple motors in the system can remain consistent with the given torque value of the system. Therefore, Figures 17 to 19 This demonstrates the consistency between the hardware-in-the-loop simulation results and the MATLAB simulation results.

[0141] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0142] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for dynamic synchronous control of multiple motors based on virtual total shaft state feedback, characterized in that, Includes the following steps: S1: Construct a multi-motor virtual main shaft control model, set the desired traction torque value through the output of the virtual main shaft and set each physical motor as a follower motor, so as to achieve coordinated and consistent total traction. S2: Based on the collected state data of each motor, use Bayesian method or Gaussian process regression method to perform joint probability modeling of the state and disturbance of each motor, and generate the estimated mean and covariance of each motor state; S3: Design a non-singular terminal sliding surface and calculate the control signal based on the multi-motor virtual total shaft control model and feedback control law; S4: Set the soft saturation buffer range and construct a soft switching function to buffer and correct the control signal and output the corrected control signal; S5: Synchronous control of multi-motor traction output is achieved based on the corrected control signal; whereby, In S3, the method includes: S31: Define the sliding surface: ,in, The rate of change of error, For adjusting the sliding surface gain, To prevent odd positive integers; S32: Feedback control law calculates the control signal: ,in For equivalent control items, , To control the input gain, Given the nonlinear model terms, The derivative of the reference trajectory used for feedforward compensation; This is a compensation term used to resist modeling errors and disturbances. , Based on fixed gain, An adaptive adjustment coefficient is used to balance the intensity of uncertainty with the strength of control. This represents the total uncertainty of all state variables.

2. The method for dynamic synchronous control of multiple motors based on virtual total shaft state feedback according to claim 1, characterized in that, In S1, the virtual spindle in the multi-motor virtual spindle control framework is not an actual physical motor. It sets the traction reference torque trajectory through control logic and feeds back the torque values ​​of each following motor in real time as the virtual spindle load feedback.

3. A multi-motor dynamic synchronous control method based on virtual total shaft state feedback according to claim 1 or 2, characterized in that, In S1, the method includes: S11: Set the dynamics of the virtual spindle as follows: Then, based on the target trajectory, a control law is designed to make the output of the virtual spindle achieve: ,in, The equivalent moment of inertia of the virtual principal axis. For virtual spindle angular velocity, To output driving torque to the controller, This is the sum of the traction torques of all following motors. The set desired traction torque value; S12: Make the sum of the outputs of all follower motors equal to the set desired traction torque value. And introduce a total error term: ,in Let be the traction torque of the j-th motor; S13: Set the capability weight for each follower motor: Then the output of each motor is: And introduce control error: , Let be the error between the expected traction torque and the output torque of the j-th motor.

4. The multi-motor dynamic synchronous control method based on virtual total shaft state feedback according to claim 3, characterized in that, In S2, the method includes: S21: Within each control cycle, collect the actual speed, output torque, and control error of each motor to construct a joint state vector: ,in, Let be the rotational speed of the j-th motor; S22: Model the joint state vector using Bayesian or Gaussian process regression methods: ,in The mean of the state estimate, , Let be the state covariance matrix. .

5. The multi-motor dynamic synchronous control method based on virtual total shaft state feedback according to claim 1, characterized in that, In S4, the method includes: S41: Defines the width of the soft saturation buffer used to determine the transition zone near the boundary: This allows setting the soft saturation buffer range: ; S42: Defines the saturation limit value used to determine the current saturation limit near the boundary: ； S43: Constructing a buffer weight function using a Sigmoid form: , where α is the buffer slope control coefficient; S44: Buffer and correct the control signal and output the corrected control signal. , among which when Near the border, As the value approaches 1, the output of the control signal gradually approaches the boundary but does not overshoot. Far from the border, When the value approaches 0, the control signal remains unaffected.

6. The multi-motor dynamic synchronous control method based on virtual total shaft state feedback according to claim 5, characterized in that, The width of the soft saturation buffer Set to 5% to 20% of the maximum control signal amplitude, and the saturation limit value As one of the saturation boundaries that the control signal is about to reach, when hour, ,when hour, .

7. The method for dynamic synchronous control of multiple motors based on virtual total shaft state feedback according to claim 5, characterized in that, In S41, the method includes: S411: Extracting Compensation Items The total uncertainty of all state variables ; S412: The total uncertainty of all state variables As a dynamic indicator, the buffer width is automatically expanded. ,in This is the initial buffer width. To dynamically expand the upper limit, To adjust the slope factor.

8. A multi-motor dynamic synchronous control system based on virtual total shaft state feedback using the method described in any one of claims 1-7, characterized in that, include: The overall framework module is used to build a multi-motor virtual master shaft control model. It sets the desired traction torque value through the output of the virtual master shaft and sets each physical motor as a follower motor, thereby achieving coordinated and consistent total traction. The joint distribution module is used to perform joint probability modeling of the state and disturbance of each motor based on the collected state data of each motor and to generate the estimated mean and covariance of each motor state. The non-singular control module is used to design the non-singular terminal sliding surface and calculate the control signal based on the multi-motor virtual total shaft control model and feedback control law. The soft saturation buffer module is used to set the range of the soft saturation buffer and construct a soft switching function to buffer and correct the control signal and output the corrected control signal. The control execution module is used to achieve synchronous control of the traction output of multiple motors based on the modified control signal.

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