10683results about "Electric motor control" patented technology

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

An electric motor based system includes an electric motor; an electric motor power controller for controlling the electric motor; a first control unit, adapted to provide first control signals for the electric motor power controller; an electric power storage device adapted to power the electric motor power controller; a first sensor adapted to determine information related to power exchange between the electric power storage device and the electric motor power controller; and a second control unit, wherein the second control unit is adapted to pass the first control signals, received from the first control unit, to the electric motor power controller in a first mode and adapted to provide second control signals for the electric motor power controller in a second mode; and the second control unit inputs and exploits the information.

A portable fan includes an airflow portion and a handheld portion. The airflow portion includes rotation air blades and a drive portion that is driveably connected to the rotation air blades. The drive portion is a three-phase motor that is configured to drive the rotation air blades to rotate to generate an airflow. The handheld portion is connected to the airflow portion. A power supply assembly is arranged inside the handheld portion; and the power supply assembly is electrically connected to and supply power to the three-phase motor.

The invention provides a brushless motor attitude detection and balance control method and system based on a gyroscope, and relates to the technical field of control, and the method comprises the steps: collecting angular velocity and acceleration data through a six-axis gyroscope, carrying out the noise reduction through wavelet transform, and carrying out the attitude calculation through the combination of an extended Kalman filter and a quaternion algorithm. A rotor position signal is obtained through a magnetic encoder, nonlinear compensation is carried out, and rotating speed data are calculated. A motor state is modeled by adopting a long-short-term memory network, a double-layer adaptive fuzzy neural network controller is constructed, and attitude error compensation and rotation speed fluctuation suppression are realized. A controller model is optimized through particle swarm optimization and a genetic algorithm, a compensation current vector is corrected in real time, and the control precision and stability of the brushless motor are improved. According to the method, the operation efficiency and the dynamic response capability of the brushless motor are effectively improved.

The invention belongs to the technical field of motor control, and discloses a control method of a motor for an inverter compressor, which comprises the following steps: embedding a distributed thermocouple array in a motor stator slot to acquire temperature data of the motor, synchronously acquiring operation parameters of the motor, generating a dynamic thermal field characteristic spectrum through a data fusion algorithm, coordinates of the hot spot areas with the temperature abnormally increased are marked, a dynamic correlation model is established, and heat conduction characteristic parameters in the dynamic correlation model are updated through a sliding window algorithm; outputting a predicted temperature rise curve through the dynamic correlation model, adjusting a PWM control strategy of the inverter, inputting the PWM control strategy to a motor driving unit to execute preliminary regulation and control, collecting feedback data to generate a multi-dimensional feedback feature vector, iteratively optimizing a combined weight of a switch interval compensation amount and a harmonic injection strategy through a gradient descent algorithm, and performing harmonic injection control. And a global optimal strategy considering the heat dissipation efficiency and the operation stability is formed, and the operation stability and the energy efficiency level of the inverter compressor under various working conditions are remarkably improved.

The invention provides an artificial intelligence-based permanent magnet motor energy efficiency optimization and energy-saving control method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting motor operation data through employing a multi-dimensional sensor array, carrying out the feature extraction based on a mutual information entropy criterion, constructing a multi-physics field coupling model, and building a digital twinborn model; a double-layer deep reinforcement learning neural network is constructed based on the model, a global optimization target is generated through a high-layer strategy network, and a low-layer execution network adopts a deep deterministic strategy gradient algorithm to decompose sub-targets; training the network by adopting a priority experience playback mechanism to obtain an optimal control strategy; and inputting the optimized parameters into a vector controller to realize three-closed-loop control. The operation efficiency of the permanent magnet motor is improved, and the system stability and the dynamic response performance are enhanced.

The invention discloses a sampling implementation method and system suitable for motor protection quick response, belongs to the technical field of motor protection, and can increase the sampling frequency and improve the quick response of motor fault protection. Comprising the steps that motor phase current and bus voltage are collected, after anti-aliasing filtering is conducted, parallel sampling is conducted through a main ADC module and a redundant ADC module which are independently configured, and the following operations are synchronously executed based on a double-buffer-area framework: in a first buffer area, a compensation coefficient updating period is dynamically adjusted, and convergence is restrained; in the second buffer area, generating a voltage gradient prediction sequence, and compensating data abrupt change; when the harmonic frequency spectrum amplitude or the voltage gradient prediction sequence exceeds the limit, an oversampling mode is triggered, a redundant ADC module is allocated to load a multi-order digital filter, and an independent hardware acceleration unit is started to process harmonic compensation and gradient prediction in parallel; and generating a dynamic safety envelope, and if the gradient extreme value in the preset continuous window breaks through the safety envelope, generating an overvoltage fault signal.

The invention provides a wide-voltage self-adaptive closed-loop vector swimming pool pump anti-interference method, and relates to the technical field of motor control and power electronics, and the method comprises the steps: collecting three-phase power supply voltage fluctuation characteristic parameters and motor operation state parameters in real time; performing amplitude compensation and phase synchronization correction on the voltage parameter based on the dynamic voltage compensation factor; a closed-loop vector anti-interference control model is constructed, and model parameters are adjusted in real time through a vector immunity coefficient; dynamically correcting control output by adopting a multivariable feedback regulation mechanism; executing an anti-interference instruction and synchronously updating PWM modulation parameters, and verifying a control effect in combination with hardware-in-the-loop simulation; and parameter self-learning optimization is realized through a genetic algorithm. The dynamic anti-interference capability under power grid voltage fluctuation and harmonic interference is improved, intelligent thermal management is realized through a temperature-voltage-current multi-dimensional coupling model, full-life-cycle self-adaptive optimization of the system is guaranteed, and the problems of compensation lag, insufficient harmonic suppression and poor long-term stability are effectively solved.

Disclosed is a low-precision incremental encoder-based method for ultra-low speed control of a servo system. According to the present invention, a fast speed observer in an ultra-low-speed working state is achieved, only a sampling speed and load torque in an M / T method need to be observed, and position information of an electric motor does not need to be observed, such that the structure of the observer is simplified, and calculation is simple and fast; moreover, the influence of a friction force and cogging torque on a speed change is considered, such that the speed observation precision is further improved. The fast speed observer can reduce hysteresis and measurement dead zones, such that the speed response and the speed control precision in a low-speed working state are greatly improved, the problem in a traditional observer of a large position deviation caused by nonlinear factors such as an external load is avoided, improving the stability and robustness of the system. According to the present invention, the processing of sampling measurement time at a medium-high speed and the processing of switching the observer at a low speed in the M / T method can optimize the speed measurement precision at both the medium-high speed and the ultra-low speed, and can better satisfy the requirements of apparatuses such as a radar on rotating speed control.

The invention relates to the technical field of motor temperature intelligent control, and particularly discloses a motor temperature monitoring and regulation method and system based on intelligent control and a storage medium, and the method comprises the steps: carrying out data collection circulation: judging whether a sampling period is reached, if yes, reading multi-mode sensing data, and outputting a calibrated temperature value T, and if not, outputting a calibrated temperature value T; if yes, vibration noise is eliminated, the environment temperature is compensated, multimode sensing data is added, and then the calibrated temperature value T is output; intelligent analysis is conducted on the calibrated temperature value T obtained through data collection circulation, if the temperature is normal, data analysis and prediction are conducted, and if the temperature is abnormal, the calibrated temperature value T is analyzed, fault self-diagnosis is conducted, abnormal reasons are judged, the system is controlled to conduct self-adjustment, and temperature early warning is conducted. The problems that in the prior art, monitoring is delayed, regulation and control are rigid, and maintenance is passive, so that energy efficiency is wasted, the failure rate is high, and operation and maintenance cost rises are solved; and a real-time temperature monitoring means and an intelligent control system are lacked.

The invention discloses a high-precision linear vibration feedback motor system, and relates to the technical field of precise electromagnetic driving, and the system comprises a core driving module which is a moving magnet type linear motor of a Halbach array magnetic circuit structure, and is integrated with a multi-physics field sensing unit; the dynamic parameter tracking module is used for acquiring a reed rigidity coefficient, a damping coefficient and a magnetic constant in real time when the motor runs, and establishing a dynamic displacement model based on a recursive least square method; a double-closed-loop control framework is adopted, an inner loop adopts field-oriented control, and an outer loop is based on model prediction control; the aging prediction unit activates a temperature rise compensation algorithm when the accumulated number of vibration times is greater than a set threshold value; the self-adaptive calibration engine is used for injecting a sweep frequency excitation signal when the system is started, and automatically compensating individual difference parameters according to the harmonic peak offset; and the digital twin mapping unit is used for establishing a real-time mapping relationship between the physical parameters of the motor and the virtual model, and dynamically correcting the model parameters by comparing the actual displacement with the model displacement.

The invention relates to a PINN-based permanent magnet synchronous motor parameter identification method. The invention relates to the technical field of permanent magnet synchronous motor driving control, and the method comprises the steps: collecting motor operation parameters, and obtaining the quadrature-direct axis voltage, current and electrical angular velocity of a motor; a PMSM model is built; a PINN model is built; and performing finite element simulation according to the established model to obtain motor operation data, and identifying motor parameters. Compared with a traditional permanent magnet synchronous motor model, the method is higher in precision, and solves the problems that the control performance of the motor is reduced, the dynamic response is not accurate and the efficiency is low due to the influence of a traditional motor equation under a high-load or complex working condition, so as to guarantee the efficient and stable operation of the motor.

The invention provides a magnetic suspension motor control method and device and a magnetic suspension motor, and relates to the technical field of magnetic suspension motor control, and the technical scheme is characterized in that the method comprises the steps: detecting the voltage change rate of a power grid, and judging a voltage fluctuation mode based on a preset threshold value; environment RFID tag information is read through a scene code analysis unit, and a current scene code is obtained; retrieving a maximum compensation current value, a current change rate threshold value and a PID integral time constant from a preset parameter matrix based on the voltage fluctuation mode and the scene code; and limiting the current change rate of the motor winding according to the current change rate threshold, and executing motor control through the maximum compensation current value and the PID integral time constant. The magnetic suspension motor control method and device and the magnetic suspension motor provided by the invention have the advantages of improving the operation stability of the motor in voltage fluctuation and multi-noise scenes, suppressing electromagnetic noise and adapting to different scenes.

The invention discloses an armored vehicle permanent magnet synchronous motor health monitoring model construction method and system based on a reduced-order digital twin model, and the method comprises the steps: building an electromagnetic-temperature coupling field finite element model based on the parameters of a permanent magnet synchronous motor, and simulating the operation parameters of the permanent magnet synchronous motor at a preset rotating speed; constructing an original feature set based on the operation parameters of the permanent magnet synchronous motor at the preset rotating speed; performing dimensionality reduction and cumulative variance interpretation on the original feature set by using a principal component analysis method to obtain a matrix formed by principal component feature vectors; an ordinary differential neural network is adopted to continuously solve an ordinary differential equation fitting historical state through dimension reduction time sequence data obtained through simulation, and an adjoint state of network parameters is reversely solved to form a permanent magnet synchronous motor digital twin reduced-order model; the coupling relation between the physical system and the preset initial value is relieved, the initial value problem is prevented from affecting the prediction effect, and reliable model and experience support is provided for health management operation and maintenance of the permanent magnet synchronous motor.

The invention relates to the technical field of direct-current brushless gear motor auxiliary control, and discloses a control method and system for a direct-current brushless gear motor, and the method comprises the steps: carrying out the control of the direct-current brushless gear motor based on the real-time data during the starting and stopping of the motor, including detection data (such as temperature, vibration, noise and the like), operation data (such as current, voltage, torque and the like), and load data; and comparing and analyzing with preset historical detection data to judge whether the motor has a fault: when a large data difference is detected, evaluating the severity of the motor fault through a fault scoring mechanism. If the fault score exceeds a preset threshold value, early warning is triggered, and a corresponding control method is selected according to the fault type, so that fault processing or control strategy adjustment is carried out, and stable operation of the motor is ensured. According to the invention, the maintenance and fault diagnosis process of the motor is optimized by combining the fault score and the dynamic adjustment of the control strategy and through the real-time monitoring and analysis of the operation data and the load data, and the efficiency and accuracy of fault response are improved.

Disclosed in the present invention are a dual three-phase asymmetric stator-type double-permanent-magnet vernier electric motor and an improved model predictive current control method therefor. The electric motor comprises a stator core, stator permanent magnets, a rotor core and a rotor ferrite, wherein the stator core is formed by alternately arranging umbrella-shaped armature teeth and quasi-rectangular armature teeth in the circumferential direction, the stator permanent magnet, which is magnetized radially outwards, is adhered to each of openings on two sides of the umbrella-shaped armature teeth, and a fan-shaped auxiliary slot is formed between the umbrella-shaped armature teeth and the stator permanent magnet; and the shape of the rotor core is similar to a gear, the rotor ferrite is embedded in the rotor core using a Halbach-based alternating-pole array, and the length of the rotor ferrite is greater than the length of the rotor core and the length of the stator core. In addition, the present invention uses a model predictive current control system based on a virtual voltage vector to construct a cost function to select the voltage vector having the minimum switching frequency, and make same operate preferentially. The present invention not only improves the torque density of the electric motor, but also significantly reduces switching losses in model predictive control.

The invention relates to the technical field of motor control, and particularly provides a permanent magnet synchronous motor current harmonic disturbance suppression method and a motor system.The permanent magnet synchronous motor current harmonic disturbance suppression method comprises the steps that firstly, a permanent magnet synchronous motor mathematical model considering harmonic disturbance under the d-q axis of a synchronous rotating coordinate system is constructed, and a theoretical basis is provided for subsequent control strategy design; an improved resonance controller is set and embedded into the extended state observer, comprehensive estimation of d-q axis non-periodic disturbance and periodic harmonic disturbance is achieved, finally, an enhanced current loop active disturbance rejection controller based on the improved resonance-extended state observer is built, unexpected peak values are eliminated, and the disturbance rejection rate of the current loop is improved. And comprehensive suppression of aperiodic disturbance and periodic disturbance is realized. According to the method, aperiodic and periodic disturbance can be suppressed, unexpected peak values are eliminated, system instability caused by noise amplification is avoided, and the steady-state performance and robustness of current control of the permanent magnet synchronous motor are remarkably improved.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention discloses a robot joint motor dynamic load self-adjusting method based on fuzzy entropy optimization. The robot joint motor dynamic load self-adjusting method comprises the following steps: S1, acquiring operation parameter data of a robot joint motor by adopting sensors arranged at key joints of a robot; s2, generating a corresponding adaptive adjustment factor data set; s3, forming a load probability distribution model parameter data set; s4, applying the load probability distribution model parameter data set to real-time load prediction, determining the load trend of a robot joint motor in a future period of time, and outputting predicted load distribution data; and S5, dynamically adjusting control parameters of a robot joint motor according to the predicted load distribution data and the adaptive adjustment factor data set to form a new control strategy data set. According to the method, the control parameters of the robot joint motor are dynamically adjusted in different load states, so that the oscillation effect of the robot in a complex load environment can be effectively reduced, and energy consumption is optimized under the condition that the motion precision is not affected.

The invention relates to the technical field of motor control, in particular to a permanent magnet synchronous motor online parameter identification system and a compensation method thereof, comprising a data acquisition module, a deep transfer learning parameter identification module, a parameter optimization module, a temperature compensation module and a controller parameter integration module. The deep transfer learning parameter identification module establishes mapping and function relations between characteristic factors and motor parameters through a teacher network, a transfer network, a memory network and an output layer, processes time sequence information and outputs a parameter identification result, and the parameter optimization module finely adjusts the parameters based on a particle swarm optimization algorithm. The temperature compensation module carries out temperature compensation on the optimized parameters according to a temperature distribution model, the controller parameter integration module generates control parameters, and the motor control module generates torque control signals according to the control parameters, so that fusion processing of multi-physical field characteristics is realized, and the precision and adaptability of parameter identification are greatly improved; and the d-q axis inductance identification error is reduced by more than 70%, and the millihenry-level precision is achieved.

A power tool system includes a power tool, a sensor, and an electronic controller of the power tool. The power tool includes a power tool housing, a motor housed within the power tool housing, and a pack interface coupled to the power tool housing. The pack interface receives a power tool battery pack having a corresponding tool interface. The sensor senses an electrical parameter corresponding to a power tool battery pack coupled to the pack interface. The electronic controller of the power tool controls the power tool battery pack to discharge current; receives an output of the sensor when the power tool battery pack is controlled to discharge current; generates a temperature estimate by processing the output of the sensor using a thermal model stored on the memory; and generates a temperature warning signal in response to the temperature estimate exceeding a temperature threshold.

A system for opening or closing a closure member of a vehicle and an accelerometer calibration method are provided. The system includes an actuator assembly with an electric motor operably coupled to an extensible member for opening or closing the closure member. The system also includes an accelerometer configured to sense movement of the closure member and output an accelerometer signal. An actuator controller is coupled to electric motor and to the accelerometer and is configured to determine an adjusted accelerometer signal as the accelerometer signal adjusted using one of a plurality of predetermined compensation factors determined through an accelerometer calibration process temporally before installation of the accelerometer in the vehicle. The actuator controller then controls the opening or closing of the closure member using the electric motor based on the movement of the closure member as represented by the adjusted accelerometer signal.

The invention belongs to the technical field of integrated circuits, and particularly relates to a speed-adjustable fan driving and anomaly detection circuit with high reliability and low cost. Comprising a current expansion driving module for providing a voltage FCC stabilized by an LDO for a fan and performing current expansion on an output current of the LDO to drive the fan; the logic control module is connected with the current expansion driving module and is used for adjusting a control signal ADJlt; n: 0gt; the value of the voltage FCC output by the LDO is changed; the open circuit and locked-rotor detection module is connected with the logic control module and the VIN port of the LDO; and the short circuit detection module is connected with the logic control module and the output end of the LDO, outputs a signal to a state flag bit FTO, and judges whether the fan is in a normal working state, an open circuit state, a locked-rotor state or a short circuit state or not according to the waveform and the level of the state flag bit FTO. The speed-adjustable fan driving circuit is integrated, and the function of detecting various abnormal states of the fan is achieved.

The invention relates to a motor energy-saving control method for adaptive parameter adjustment. The method comprises the following steps: constructing an operation parameter sequence and comparing the operation parameter sequence with a preset reference model by collecting the rotating speed, the torque and the current signal of a motor in real time to generate a parameter deviation vector; based on a deviation threshold comparison result, carrying out noise processing on the overrun deviation to obtain a filtered sequence, and calculating a time delay degree in combination with historical parameters; calculating a compensation gain according to the time delay, fusing the compensation gain with the filtered deviation to generate a control instruction sequence, correcting the compensation gain through fuzzy control to obtain a stabilization parameter, and further generating a correction control instruction sequence; and finally, acquiring load data, fusing the load data with the correction instruction to obtain a system operation characteristic value, and generating an optimization state identifier when a judgment standard is met. By adopting the method, adaptive adjustment of the control parameters can be realized, the dynamic working condition is accurately responded, and the energy-saving efficiency of the motor under the variable-load working condition is improved.

The invention provides a sensorless motor stator temperature protection method, device and equipment, and relates to the technical field of motor control, and the protection method comprises the steps: obtaining the current voltage, current and rotating speed of a motor when the motor in a motor control system is in a running state, determining a corresponding temperature rise coefficient and a heat balance temperature according to the current voltage, the current current and the current rotating speed; obtaining a current estimated temperature according to the current voltage, the current current and the current rotating speed; determining a current predicted temperature according to the current estimated temperature, the heat balance temperature, the last estimated temperature and the corresponding temperature rise coefficient; and comparing the current predicted temperature with a preset protection threshold value, and performing temperature protection control on the motor according to a comparison result. A temperature sensor does not need to be installed in the motor, so that the installation difficulty and cost are reduced, the reliability and durability of the system are improved, and the risk that the sensor breaks down or is damaged is avoided.

The invention relates to the technical field of electric vehicle transmission and control, in particular to an electric vehicle speed reducer parking motor position feedback system. The system aims at solving the problems that the position feedback precision is reduced, the reliability is insufficient and the redundancy is deficient in a severe environment in the existing scheme. According to the system, through redundancy design of a main position sensing module (a magnetoelectric encoder) and an auxiliary position sensing module (an inductive sensor) and in combination with a multi-source data fusion module (based on Kalman filtering), anomaly detection and fault tolerance, position prediction and compensation and a temperature compensation and self-adaptive calibration mechanism, accurate feedback of the position of the parking motor rotor is achieved. According to the system, the accuracy and robustness of position feedback and the reliability of the system can be remarkably improved, the service life of a parking mechanism is prolonged, and the parking safety is improved.

The invention relates to the technical field of motor control, in particular to a control and parameter intelligent optimization method for an anti-impact high-precision PMSM (permanent magnet synchronous motor) feeding servo system, which comprises the following steps of: S1, establishing a physical structure and a dynamic model of a PMSM driving feeding system; s2, designing a variable gain fractional order super-spiral sliding mode controller VGFSTSMC, realizing finite time convergence of position tracking errors by dynamically adjusting and controlling gain coefficients, and inhibiting system jitter; s3, designing an adaptive sliding mode disturbance observer ASMDO, and estimating and compensating unknown disturbance in real time through adaptive gain adjustment and an integral sliding mode surface; s4, identifying a system Stribeck friction model based on a least square method, and inputting the system Stribeck friction model as a friction feedforward compensation FFC to a current loop to reduce the disturbance uncertainty of the system; s5, dynamically adjusting and optimizing control parameters of the VGFSTSMC and the ASMDO by adopting an optimization algorithm so as to minimize a root-mean-square value RMSE of a position tracking error and a maximum instantaneous error; and S6, VGFSTSMC, ASMDO and FFC are combined with an optimization algorithm, and the PMSM is driven to realize high-precision position control.

The invention discloses a six-winding high-voltage variable-frequency speed regulation control method and system, and belongs to the technical field of high-voltage variable-frequency speed regulation, and the method comprises the steps: building an electric-thermal-magnetic coupled six-dimensional nonlinear system model through collecting the structural parameters, thermosensitive data and an initial state of a motor; establishing a frequency domain regulation and control matrix among the six windings in combination with the operation condition and the load type; predicting a future disturbance power trend by using a weighted short-time Fourier analysis method, dynamically correcting the reference current of each winding, and generating six groups of PWM control signals; when the performance of the winding is degraded, fault-tolerant topology reconstruction and driving task redistribution are executed; periodically reconstructing a control matrix through a thermoelectric feedback mechanism, and executing parameter adaptive optimization according to a current balance coefficient and hot redundancy distribution; according to the method, high-precision current coordination control, frequency domain energy decoupling and winding thermal load balancing can be realized, and the speed regulation response speed, the stability and the fault-tolerant capability of the system are remarkably improved.

The invention discloses an error compensation method for a dead-beat predictive current control model of a high-speed permanent magnet synchronous motor in a static coordinate system. The method comprises the following steps: firstly, analyzing a dead-beat predictive current control scheme of the high-speed permanent magnet synchronous motor under a static coordinate system, and on the basis, analyzing a steady-state current control error caused by parameter mismatch, transient current tracking, voltage loss caused by nonlinearity of an inverter, and current errors in a steady state and a transient state; and an error compensation scheme based on dead-beat predictive current control of the high-speed permanent magnet synchronous motor under a static coordinate system is provided, namely, self-adaptive reference correction current injection is provided. According to the method, the parameter robustness of dead-beat predictive current control of the high-speed permanent magnet synchronous motor based on static coordinates is effectively improved, and steady-state current errors caused by inaccurate models can be effectively eliminated.

The invention provides an electronic temperature protection control method for a direct current brushless motor, and relates to the technical field of motor control, and the method comprises the steps: temperature sampling: employing a controller or a motor internal sensor and a winding resistor to estimate and collect a temperature signal in real time; temperature filtering: eliminating noise interference by adopting mean value or Kalman filtering; temperature comparison: comparing the filtered temperature value with a preset multi-level threshold value (from one level to three levels); and protection control is carried out, and measures such as power reduction, current limiting or driving signal closing are taken according to the threshold exceeding degree. Through multi-point sampling, heat conduction installation, dynamic filtering parameter adaptation and threshold setting based on the insulation grade, the temperature monitoring precision and response speed are improved, the problems of response lag and cost of traditional hardware protection are avoided, dynamic and precise temperature protection of the motor is achieved in combination with a thermal response time constant and temperature change rate pre-judgment mechanism, and the reliability of the motor is improved. The service life of equipment is effectively prolonged.