118 results about "Motor dynamics" patented technology

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

The invention discloses a servo motor dynamic overload detection and protection method based on a heat accumulation model, which relates to the technical field of servo system fault protection application, and comprises the steps of constructing a heat accumulation equivalent model, dynamically detecting an overload state, executing a heat dissipation recovery strategy, implementing a multi-stage protection mechanism, self-adaptive environment compensation and a historical data learning function. Through discretization processing of overload curve data and a nonlinear acceleration calculation strategy, the problems of protection lag and false triggering caused by traditional single-parameter detection are avoided, it is ensured that the motor can respond in time in light load, heavy load and load fluctuation scenes, the burn-out risk of the motor is reduced, and through integration of an environment temperature sensor and a heat dissipation condition evaluation unit, the reliability of the motor is improved. The heat accumulation coefficient and the heat dissipation attenuation parameter are dynamically adjusted, the problem of model deviation caused by environment interference is solved, when heat dissipation deterioration and temperature feedback abnormity are detected, the heat dissipation recovery period is automatically prolonged, calculation logic is corrected, and the continuous operation reliability of the system is improved.

The invention discloses a robot dynamics physical information prediction neural network construction method based on motor and external force coupling, and relates to the technical field of robots. Comprising the following steps: embedding a motor kinetic equation into a DeLaN network, and establishing an explicit dynamic mapping relation between a system motion state and a motor current through mapping between the motor current and a joint torque; introducing an external force item by fitting a Jacobian matrix, and constructing approximate mapping of the Jacobian matrix by adopting a data driving method; and an initial model is constructed, and after motor dynamics and external force modeling are incorporated into the initial model, an inverse dynamics model of the mechanical arm is obtained. Compared with an existing most advanced method, the method has higher modeling precision and generalization ability and better control performance. The innovative scheme provides solid technical support for realizing real-time accurate control in the field of robots.

The invention discloses a USV rotary column stabilization control system considering complex disturbance and motor dynamic characteristics, belongs to the field of motion control systems of ocean mobile observation platforms, and aims to improve the stability and control efficiency of USV navigation. The method comprises the following specific steps: firstly, establishing a rotary column stabilizer lift model, a USV nonlinear rolling model and a servo motor dynamic model; then designing a Backstepping controller based on a differential item disturbance observer, estimating and compensating disturbance caused by external and model uncertainty in real time, then adjusting expected control input in real time, and ensuring that the output rolling angle tracks the expected rolling angle. The MPC controller based on servo motor state feedback is designed, the state quantity of the motor is fed back to the controller for prediction and adjustment, the problems of response lag and output overload of the servo motor in complex sea conditions are effectively solved, rolling angle error minimization and effective control over energy consumption are further achieved, and the service life of the servo motor is prolonged. And the purpose of multi-objective optimization is achieved.

The invention relates to a motor dynamic driving control method, device and system based on motor working conditions. The motor dynamic driving control method based on the motor working condition comprises the following steps: S1, acquiring motor operation data; s2, judging the working condition of the motor according to the operation data of the motor; s3, selecting a corresponding control algorithm according to the working condition of the motor; and S4, through the control algorithm, according to the motor operation data, generating a control signal. The working condition of the motor is actively judged by monitoring the operation data of the motor, then the appropriate control algorithm is adaptively switched according to the current working condition of the motor, and high motor efficiency and good stability can be obtained under any working condition of the motor.

The invention discloses an early warning method and device for climbing instability of a crawler unmanned vehicle and a medium, and relates to the technical field of unmanned vehicle climbing control. The early warning method comprises the steps that a digital twin model of the tracked unmanned vehicle is constructed, the digital twin model fuses a motor dynamic model and a whole vehicle dynamic model, and virtual scene data is generated; real-time data of a multi-source sensor of the tracked unmanned vehicle are obtained, wherein the real-time data comprise vehicle posture data, environment data and driving system data; and constructing a time convolutional network prediction model based on transfer learning, performing pre-training by using virtual scene data as source domain data, and performing fine tuning by using real-time data as target domain data to form a target domain prediction model. And inputting the current data into the target domain prediction model, and outputting a predicted vehicle state parameter prediction sequence. According to the vehicle state parameter prediction sequence and a preset safety threshold value, the instability risk is evaluated, an early warning signal is sent out, and the generation logic of the early warning signal is evaluated and optimized through an early warning performance loss function.

The invention relates to the technical field of motor fault diagnosis, and discloses a multi-degree-of-freedom resonant excitation motor dynamic vibration test system and a test method, the system injects a specific harmonic current into a motor through closed-loop control and calculation and a high-bandwidth harmonic injection driver, so that the actual vibration of the tested motor tracks a preset target vibration profile in real time. In the testing process, the cross-spectral density of multi-point vibration signals is analyzed, and a transmission path and a collection area of vibration energy in a motor structure are calculated; and meanwhile, the electromagnetic impedance and the mechanical impedance of the motor are identified and decoupled on line by superposing a broadband disturbance signal in the driving current. In combination with energy flow analysis and an impedance decoupling result, the method can accurately position a vibration abnormal position, clearly causes whether a fault source is from mechanical structure characteristic change or electromagnetic part performance change from a physical level, and realizes deep and accurate diagnosis of the dynamic characteristics of the motor.

The invention relates to a motor dynamic driving control method, device and system based on deep learning prediction. The method comprises the following steps: S1, acquiring current motor operation data; s2, predicting future motor operation data according to the current motor operation data through a prediction model; s3, judging a future motor working condition according to the future motor operation data; s4, selecting a control algorithm according to the future motor working condition; and S5, by taking the current motor operation data as an input condition, generating a control signal through the control algorithm. According to the method, the future change of the working condition of the motor can be mastered in time through the prediction model, so that a proper control algorithm is actively switched according to the future change, the dynamic response capability is relatively high, and relatively high motor efficiency and relatively good stability can also be obtained under special working conditions.

The invention discloses an attitude-elasticity-pneumatic coupling flutter control method of a flying wing layout aircraft, and relates to the technical fields of mechanics, aeroelasticity, control and the like. The control method comprises the steps that a Lagrangian equation under a general body shafting is synthesized, a full-quantity coupling dynamic model is established, and a disturbance motion dynamic equation used for flight control system design is established through small disturbance linearization; correcting the disturbance motion kinetic equation based on a system identification flight test; designing an attitude-elastic-pneumatic coupling flutter suppression controller based on a traditional or modern control theory, and embedding the attitude-elastic-pneumatic coupling flutter suppression controller into a flight control system; the effectiveness of the flutter suppression controller is verified through a flight test. The problems that existing numerical analysis and wind tunnel experiments cannot accurately reflect the complex coupling effect in flight, flutter cannot be effectively restrained in flight, and the flight envelope cannot be expanded are solved, and the flight safety and stability of the flying wing layout aircraft can be effectively improved.

The invention provides an electric driving system, a control method and a vehicle with the electric driving system, and the electric driving system comprises a first driving part which is provided with a first output shaft; the second driving part and the first driving part are arranged at an interval, and the second driving part is provided with a second output shaft; the first end of the gear shifting part is in sliding connection with one of the first output shaft and the second output shaft, and the second end of the gear shifting part is connected with the other one of the first output shaft and the second output shaft; and the execution end of the adjusting part is connected with the gear shifting part, and the adjusting part is used for adjusting the rotating speed difference between the first output shaft and the second output shaft. The movement of the gear shifting part is controlled through the adjusting part, the rotating speed difference between the first output shaft and the second output shaft can be accurately adjusted, and therefore it is ensured that under various driving scenes, the electric drive system can achieve optimal power distribution, the escape capacity and the cross-country performance of a vehicle are improved, and the driving safety is improved. The problem that in the prior art, efficiency is low when dual-motor dynamic coupling and separation are achieved is solved.

The invention relates to the field of motor control, and particularly discloses a motor parameter identification method and system, and the method comprises the steps: building a to-be-identified parameter model based on a motor dynamics model considering external disturbance and nominal deviation; gaussian process regression training is carried out on the training data of the to-be-identified parameter model to obtain prior distribution; and obtaining posterior distribution of test data of the to-be-identified parameter model based on the prior distribution, and performing parameter identification on the to-be-identified parameter model based on the posterior distribution to obtain a target parameter model. Priori distribution is established through Gaussian process regression training, posterior distribution under a given input condition is presumed in combination with the prior distribution, parameter identification is performed on the to-be-identified parameter model based on the posterior distribution, and the obtained target parameter model has statistical significance and relatively high generalization ability. High-dimension, small-sample and non-linear term motor parameter identification can be realized with relatively high precision, and the efficiency and stability of motor system operation are improved.

The invention discloses a multi-motor dynamic synchronous regulation and control method based on virtual line shaft state feedback, and the method comprises the steps: constructing a multi-motor virtual line shaft control model, setting an expected traction torque value through the output of a virtual main shaft, and setting each physical motor as a following motor; according to the collected state data of each motor, performing joint probability modeling on the state and disturbance of each motor by adopting a preset modeling method, and generating an estimated mean value and a covariance of each motor state; designing a nonsingular terminal sliding mode surface and calculating a control signal; setting a soft saturation buffer area range, constructing a soft switching function, carrying out buffer correction on the control signal, and outputting a corrected control signal; and synchronous control of multi-motor traction output is realized according to the correction control signal. According to the multi-motor dynamic synchronous regulation and control method, the synchronous stability, the anti-interference capability and the output smoothness in a complex dynamic environment can be improved.

The invention discloses a super-efficient intelligent explosion-proof motor dynamic regulation and control system based on edge calculation, which relates to the technical field of motor dynamic regulation and control and comprises an edge data acquisition module, a data preprocessing module, a real-time load prediction module, a dynamic compensation module, an energy efficiency safety game control module, a cluster collaborative optimization module and an execution module. The edge data acquisition module is used for acquiring motor operation state parameters and environment safety parameters by adopting a multi-sensor fusion technology to obtain original sensing data; the data preprocessing module is used for carrying out noise reduction processing on the original sensing data by adopting a sliding window filtering method, and extracting a vibration signal characteristic frequency band through a wavelet transform method to obtain standardized time sequence data; and the real-time load prediction module is used for carrying out modeling analysis on the standardized time series data by adopting a time-space convolutional network (TCN) to obtain an initial load prediction value.

The invention discloses a real machine deployment method and device for a quasi-direct-drive robot for migration from simulation to reality, and the method comprises the steps: S1, driving each joint motor through a PD controller on a real machine of the robot to follow a plurality of groups of reference tracks, and synchronously collecting the real motion track data of each joint; s2, in a parallel simulation environment, performing initialization based on a random frame in a real motion track, randomly endowing a group of motor kinetic parameters, then driving a simulation robot through a PD controller by using the same group of reference tracks, and generating corresponding simulation motion track data; s3, based on a trajectory error between the two types of trajectory data, iteratively updating motor kinetic parameters by using an optimization algorithm until the error converges, thereby obtaining a calibrated simulator; s4, training a control strategy network of the robot through reinforcement learning in the calibrated simulator; and S5, directly deploying the control strategy network obtained by training to a real machine for performance evaluation and security verification.

The invention belongs to the field of four-wheel-foot robots, and provides a track optimization and motion generation method, controller and system for a four-wheel-foot robot. The method comprises the steps that a trajectory optimization objective function is constructed based on a motion dynamics model of the four-wheel-foot robot, and the whole-body optimal trajectory of the four-wheel-foot robot in a motion period is solved under the condition that set constraint conditions of the four-wheel-foot robot are met; introducing a virtual joint angle, enabling the world coordinate system to be always aligned with the contact coordinate system through rotation transformation, and constructing a single-wheel-leg kinematics model containing a wheel-ground contact point; and performing inverse kinematics calculation on the single-wheel-leg kinematics model containing the wheel-ground contact points to obtain position information of the wheel legs and the body, and performing motion control on the four-wheel-leg robot in combination with expected foot end contact force.

The invention provides a smooth trajectory planning and anti-skid cooperative control system for a four-axis global vehicle. The smooth trajectory planning and anti-skid cooperative control system comprises a signal input preprocessing module, a reference trajectory planning module and a wheel torque control module, the signal input preprocessing module is used for collecting and preprocessing the control expectation instruction and the vehicle state information to obtain an input signal; the reference trajectory planning module is used for planning an executable reference trajectory in real time according to the input signal in combination with physical constraints of vehicle movement and calculating a tracking reference value according to the executable reference trajectory, and the reference value at least comprises a reference rotating speed and a reference yaw velocity; and the wheel torque control module is used for tracking the reference trajectory, calculating the control torque of each wheel in real time in combination with the dynamic state of vehicle motion, and issuing the control torque to the corresponding wheel. According to the invention, global vehicles can stably run, and the situations of vehicle instability, overturning and collision damage are avoided.

The invention relates to the technical field of motor control, and discloses a vector control application of a direct drive motor, which comprises the following steps: S1, collecting stator current, rotor speed and bus voltage signals of the motor, and carrying out filtering preprocessing on the collected signals; and S2, constructing a self-adaptive flux linkage observation model, estimating a stator flux linkage in real time based on stator current and bus voltage signals in combination with rotor rotating speed information, and correcting stator resistance and inductance parameters through a model reference self-adaptive algorithm. According to the method, the motor parameters are corrected in real time through the self-adaptive flux linkage observation model, parameter drift caused by factors such as temperature change and magnetic saturation is effectively counteracted, and the accuracy of flux linkage estimation is improved; the improved PI regulator realizes online self-tuning of control parameters through a fuzzy control algorithm, can quickly adapt to changes of dynamic characteristics of the motor, and shortens the regulation response time of flux linkage and torque; and the resistance of the system to external interferences such as load fluctuation, power supply voltage change and the like is enhanced.

The invention discloses a layered optimization design method for a dual three-phase permanent magnet synchronous motor, and the method comprises the steps: firstly building a motor agent model according to a motor optimization target, and carrying out the optimization of a motor body through a multi-target optimization algorithm, so as to obtain a Pareto leading edge of a motor parameter; then, Kp and Ki parameter setting is carried out on a motor model in the Pareto frontier, so that the dynamic performance of the motor is optimized; and finally, determining an optimal solution containing dynamic performance and a motor body optimization target by combining a table look-up method with a multi-target optimization algorithm. According to the method, through joint optimization of the motor body and the control system, the dynamic performance of the motor and the body optimization target can be considered in the design stage, the overall robustness and the performance consistency of the motor are improved, and it is ensured that the performance of a machining prototype is kept consistent with a simulation result.

The invention relates to the technical field of motor testing, and discloses a motor dynamic characteristic compensation test method and platform based on multi-modal data, the method collects motor data through a multi-modal synchronous test system, and a processing module executes the following steps: constructing a state observer model to estimate winding temperature and loss torque; in the online test, the accuracy of the model is monitored by calculating the residual error between the model prediction and the measured value; and when the residual dynamic characteristic exceeds a threshold value, generating and applying an adaptive excitation sequence according to the residual characteristic, collecting high-resolution data to update model parameters on line, forming a self-optimization closed loop, after the test is finished, adopting a fixed interval smoothing algorithm to be combined with a final model to obtain a global optimal state track, and calculating a dynamic characteristic compensation result according to the global optimal state track. According to the invention, through adaptive excitation of residual error driving and online model updating, accurate identification and compensation of dynamic characteristics of the motor are realized, and the test automation level and accuracy are improved.

The invention relates to a braking control method for a distributed torque electric vehicle, a medium and equipment. The method comprises the steps that a control model of wheel rotation dynamics is established according to a vehicle resistance model and a longitudinal dynamics model, and regenerative braking torque of the electric vehicle is established according to the control model of the wheel rotation dynamics; a driving motor model of the vehicle is established, the regenerative braking torque of the electric vehicle serves as input of the driving motor model, and energy recovery of a driving motor is obtained; a braking system is improved based on motor braking force, and braking torque needed by a vehicle is distributed through energy recovery of a driving motor; and adjusting the braking torque of the front axle of the wheel and the braking torque of the rear axle of the wheel, and introducing a predictive control model. According to the method, the arithmetic optimization algorithm is adopted to optimize the performance index of the first stage and the performance index of the second stage, and real-time control and optimization of prediction errors are achieved.

The invention discloses a motor dynamic efficiency optimization regulation and control system, which relates to the technical field of dynamic efficiency optimization regulation and control and comprises a data acquisition module, an efficiency characteristic module, a health evaluation module, a state evaluation module, an optimization execution module and a parameter updating module. A comprehensive index comprehensively reflecting the running state of the motor is constructed, conversion from single efficiency optimization to system-level comprehensive optimization is achieved, a parameter updating module forms complete closed-loop control, the system can dynamically adjust key parameters according to the actual running effect, the self-learning and self-optimization functions are achieved, and the system reliability is improved. The system can dynamically adjust key parameters according to actual operation effects, has self-learning and self-optimization functions, realizes optimal balance of safety and economy through a multi-mode operation strategy, ensures equipment safety, and can dynamically optimize construction efficiency on the premise of safety.

The invention provides a motor vector control method based on high-frequency signal injection and related equipment. The method comprises the following steps: adjusting an injection parameter of a high-frequency signal according to a motor operation state; determining a high-frequency current response signal of the motor according to the injection parameter; extracting a rotor position estimation value corresponding to the high-frequency current response signal through a deep learning model; inputting the rotor position estimation value into a motor dynamic model to generate a joint estimation value of the rotor position and the speed; and feeding back the joint estimation value to a motor control loop to carry out motor vector control. Through the implementation of the scheme, the rotor position estimation value corresponding to the high-frequency current response signal is extracted through the deep learning model, the accuracy and robustness of rotor position estimation can be improved, and estimation errors caused by motor parameter changes or external disturbance are reduced.

The invention discloses an electric shovel lifting steel wire rope dynamic control device and method based on bucket lifting lug angle changes. The electric shovel lifting steel wire rope dynamic control device comprises a tilt angle sensor, a lifting motor and a whole vehicle controller. The tilt angle sensor is installed in a lifting lug of a bucket of the electric shovel and used for monitoring the dynamic change conditions of the axial tilt angle and the angular acceleration of the lifting lug in the operation process of the electric shovel in real time. A lifting motor dynamic control model established based on the data of the tilt angle sensor is configured in the vehicle control unit, the lifting motor dynamic control model analyzes the real-time posture information of the lifting lug according to the obtained data of the tilt angle sensor, and the tension state of the lifting steel wire rope is further recognized according to the real-time posture information of the lifting lug. And the lifting motor is dynamically controlled accordingly. The angle change of the lifting lug can be automatically monitored in real time, the lifting motor is dynamically adjusted, the fatigue fracture caused by overload of the lifting steel wire rope and the groove jumping overlying fault caused by looseness are effectively avoided, and the operation safety and reliability of equipment are remarkably improved.

The invention provides a preset performance control method and system under saturation of a train actuator, and relates to the technical field of train control, and the method comprises the following steps: building a train longitudinal motion kinetic equation based on the Newton second law; defining actual control input under saturation constraint of a train actuator, and establishing a dynamic compensation function in combination with a train longitudinal motion kinetic equation; designing a tracking error in the train operation process, and defining a filtering error in combination with a power compensation function; designing a preset performance function based on the train longitudinal motion kinetic equation and the filtering error, and defining an error conversion mechanism; constructing a preset performance control law based on the error conversion mechanism, and designing a parameter adaptive law to update the preset performance control law; and verifying the stability of the updated preset performance control law, and controlling the train operation by using the updated preset performance control law. Under the actual working condition of saturation constraint of the actuator, the tracking precision, the response speed and the overall stability of train operation control are remarkably improved.

A method for composite attitude control of a spacecraft with nonlinear sloshing and large flexible appendages disclosed by the present invention belongs to the technical field of spacecraft control. The implementation method of the present invention is as follows: various non-conservative disturbance torques acting on the spacecraft during on-orbit operation are characterized by Te; the attitude motion of the spacecraft body at any angle is described by Euler quaternions; the elastic vibration of 1 to m flexible appendages is described by the assumed mode discretization method; the motion description of the equivalent spherical pendulum model of liquid sloshing is improved by using the split variable technique; a large-range motion dynamics model of a liquid-filled spacecraft with large flexible appendages is established; the spacecraft attitude dynamics model is linearized; an output feedback attitude control law for a liquid-filled flexible spacecraft designed according to the Lyaponuv stability theorem; an input shaping-output feedback composite attitude controller for a liquid-filled flexible spacecraft designed based on the output feedback attitude control law of the liquid-filled flexible spacecraft and the Euler quaternion input command, thereby realizing active vibration suppression and improving the accuracy of spacecraft composite attitude control.

The invention provides a shaftless drilling winch and an intelligent cooperative control and thermal management method thereof, and the method is implemented based on an embedded controller and a sensor network, and comprises the following steps: data real-time acquisition, intelligent lubrication heat dissipation control, dual-motor dynamic cooperative control, predictive maintenance management based on a health state, and fault tolerance and degradation operation. According to the system, efficient cooperative driving, on-demand accurate heat management and state-based predictive health maintenance of the winch are achieved, the problems that a traditional winch control system is poor in collaboration, extensive in heat management, lack of health management and the like are solved, and the safety, economical efficiency and modernization level of drilling operation are remarkably improved. According to the invention, the winch is changed from traditional mechanical equipment to intelligent equipment with self-sensing, intelligent decision making and active health management capabilities.

The invention discloses a cross arm and insulator installation robot motor control method based on a T-S fuzzy model, and the method comprises the following steps: building a permanent magnet synchronous motor nonlinear model according to electromagnetism and motor dynamics; based on a sector nonlinear method, establishing a permanent magnet synchronous motor T-S fuzzy model; designing an observer to obtain a load torque estimated value; designing an L-infinity robust controller to ensure that the angular velocity of the motor can track a reference instruction; and an anti-control gain matrix solving criterion is formulated, and it is ensured that the system meets performance indexes. According to the method, the nonlinear control problem of the permanent magnet synchronous motor system is effectively solved, the influence of continuous interference signals on the system can be effectively suppressed, and the robustness of the system is enhanced.

The application provides a motor dynamic control method, device, equipment and vehicle, which can determine different induced electromotive force voltages according to different operation parameters of a target motor, determine a maximum value of the induced electromotive force voltage allowed to appear in the current relay state, and further determine the maximum allowed speed of the target motor. Since the induced electromotive force voltage is constantly changing, the maximum allowed speed is also changing. The switching of the protection mode is performed according to the maximum allowed speed, the dynamic switching is realized, the safety of the motor is ensured, the maximum allowed speed corresponds to a higher vehicle speed, and the normal driving of the vehicle is not affected by limiting the vehicle speed according to the maximum allowed speed.

The present invention discloses a fault-tolerant formation control method for a quad-rotor unmanned aerial vehicle with anti-interference capability, including establishing a cluster system of unmanned aerial vehicles, and establishing translational dynamics, rotational dynamics, motor dynamics and actuator fault models for each unmanned aerial vehicle; designing a nominal formation controller and combining a high-order sliding mode observer to estimate and compensate for actuator deviation faults and external time-varying disturbances to realize trajectory tracking control of each quad-rotor unmanned aerial vehicle on a virtual navigator; decoupling a position loop and an attitude loop, designing an attitude controller for the nominal system and combining a high-order sliding mode observer to solve the influence of external time-varying disturbances on the attitude loop to realize attitude stability of each quad-rotor unmanned aerial vehicle; calculating the lift coefficient of each rotor after an actuator fault based on the Newton correction method to realize online fault diagnosis, and calculating the speed of each motor according to the control efficiency matrix after the fault to realize safe and reliable formation control of the quad-rotor unmanned aerial vehicle after the actuator fault. The stability and safety of the quad-rotor unmanned aerial vehicle cluster flight are improved.

The invention discloses a high overload pulse type motor torque loading system, including a torque loading device, a torque measuring device and a timing device; the torque loading device includes a synchronous generator, a motor to be tested, a variable resistor, a rectifier bridge, a coupling No. 1 and a coupling No. 2; the motor to be tested is connected to the synchronous generator through two couplings; the synchronous generator is connected to the AC side of the rectifier bridge, and the DC side of the rectifier bridge is connected in series with a variable resistor and a timing device to form a torque loading loop; when the timing device works within the peak torque holding time or the load torque holding time, the torque loading loop is closed or disconnected, and the motor to be tested bears the peak load torque or the small load torque. The invention can provide a large load that can be adjusted with high precision for the motor, the loading time can be accurately controlled, the torque response time is extremely short, and the dynamic pulse type loading of the motor is realized, the efficiency of the motor load test is improved, and the reliability of the motor test is increased.