8958results about "Dynamo-electric brake control" patented technology

The invention relates to the technical field of motor control, in particular to a permanent magnet motor current control system based on predictive compensation, which comprises a thermal field identification module, a response offset identification module, a correction track generation module, an amplitude limiting interval decision module and a compensation instruction output module. According to the method, a dynamic prediction correction mechanism is established based on thermal field characteristics and a response error trend, real-time adjustment of a current track of the permanent magnet motor is realized, a three-dimensional space thermal load curved surface is constructed by means of a stator temperature rise curve, a permanent magnet temperature change rate and driving torque fluctuation, and transient influence of thermal working condition change on magnetic performance of the motor is reflected. A multi-dimensional prediction compensation path is integrally constructed through interaction among multiple thermal field parameters, electromagnetic response variables and time sequence feature points, the response precision and control stability of the permanent magnet motor under the dynamic working condition are improved, the magnetic performance attenuation influence caused by thermal drift is reduced, and meanwhile the tracking consistency under current track disturbance is improved.

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 relates to the technical field of motor control, and discloses a control method and system of a permanent magnet direct current brushless slotless motor. The method comprises the following steps: performing temperature compensation and signal filtering on voltage data of a Hall sensor to obtain rotor position information and temperature characteristic data; performing magnetic field intensity dynamic compensation calculation according to the rotor position and the temperature data to obtain magnetic field intensity parameters; adjusting the amplitude and phase of the three-phase current according to the magnetic field intensity parameter, and generating a current control quantity; extracting and compensating a harmonic component to obtain a torque smooth control instruction; all losses are calculated and optimized, and efficiency optimization parameters are generated; and performing feature extraction and anomaly judgment on the operation parameters, and outputting a protection control strategy. Accurate detection of motor position signals, stable control of torque, optimal control of loss and real-time monitoring and protection of abnormal states are achieved under the high-temperature working condition, and therefore stable operation and the service life of the motor under the high-temperature environment are guaranteed.

The invention provides a parameter self-tuning control method for a permanent magnet synchronous motor, and relates to the technical field of synchronous motor control, and the method comprises the steps: firstly, monitoring a dynamic operation data set, including stator current, rotor flux linkage, rotating speed fluctuation quantity and load change characteristic parameters, of the permanent magnet synchronous motor in real time; secondly, feature relevance mapping is carried out based on multiple parameter combinations in the dynamic operation data set, initial parameter adjustment sets are generated, and each initial parameter adjustment combination comprises the mapping relation between the motor operation state and the target response index; dividing a plurality of parameter adjustment subsets according to the parameter correlation degree, configuring a dynamic error range, extracting a positive adjustment parameter from the first subset corresponding to the first operation state, and extracting a negative adjustment parameter from the second subset with the low correlation degree; and finally, training a dynamic parameter adjustment model by utilizing the positive adjustment parameters and the negative adjustment parameters, generating an optimized real-time parameter setting instruction set, realizing intelligent self-setting of the parameters of the permanent magnet synchronous motor, and improving the running performance of the motor.

The invention provides a generator excitation regulation and control optimization system based on model prediction, and relates to the technical field of data processing, and the system comprises a data acquisition module which is used for collecting the operation parameters of a generator; the state estimation module is used for calculating a state variable of the generator excitation system and constructing a state space model of the generator excitation system; the model prediction control module is used for calculating excitation current prediction values at multiple moments in the future by adopting a rolling horizon optimization method, and performing dynamic correction based on a prediction error correction mechanism; the optimization calculation module is used for constructing a target optimization function which comprises a generator voltage stability target item and an excitation current adjustment smoothness target item, and calculating an optimal excitation current adjustment amount by adopting a constraint optimization method; the excitation adjusting module is used for controlling a generator excitation system so as to adjust excitation current; according to the invention, the autonomy and accuracy of the generator excitation regulation and control optimization system are 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 relates to the technical field of motor control, and discloses a brushless motor adaptive control method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-point temperature data of a motor, building a thermal dynamic model, and reconstructing the complete temperature field distribution; identifying a relation function between motor parameters and temperature, and establishing a temperature sensitive parameter model; in combination with temperature field distribution and a temperature sensitive parameter model, learning long-term influence of control actions on temperature distribution, and predicting temperature evolution trends under different control strategies; constructing a multi-objective optimization control strategy, and generating a motor control instruction and a heat dissipation control instruction; dynamically selecting an optimal heat dissipation strategy combination according to the generated control instruction, the current working condition and the predicted heat influence; according to the invention, high-precision control and thermal management optimization of the brushless motor in a temperature change environment are realized.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention discloses a motor control method and device based on an encoder, a medium and a program product, and relates to the field of motor control, and the method comprises the steps: receiving a position signal and a speed signal outputted by the encoder, and carrying out the filtering processing to obtain an operation state parameter; collecting mechanical vibration and electromagnetic noise generated in the operation process of the motor to obtain acoustic parameters of the motor, and extracting frequency characteristic parameters representing the rotating speed and motion characteristics of the motor; performing data fusion on the operation state parameters and the frequency characteristic parameters according to a preset weight to generate an operation state model; the operation state model is compared with a standard motion trail, and the position deviation value and the speed deviation value of the actual motion trail of the motor are calculated; and calculating a compensation control quantity according to the position deviation value and the speed deviation value, converting the compensation control quantity into a driving signal and outputting the driving signal to a motor driver so as to drive a motor to perform operation adjustment. According to the invention, the control precision of the motor in actual operation can be improved.

The invention relates to the technical field of motor control, and discloses a hybrid excitation synchronous motor control strategy optimization method and device. The method comprises the following steps: performing mathematical modeling on permanent magnet flux linkage, exciting current and rotor parameters of the hybrid excitation synchronous motor to obtain a state observer equation; collecting current parameters and position parameters, and performing filtering processing to obtain motor operation state parameters; inputting a five-layer deep neural network for current and rotating speed parameter prediction to obtain an optimal current parameter and an optimal rotating speed prediction parameter; constructing a multi-objective function, and solving to obtain a current reference value; inputting the current reference value into a cascade PI controller, and performing voltage regulation through feedforward decoupling control and a space vector pulse width modulation technology to obtain a control voltage signal; and performing performance calculation control parameter optimization on the control voltage signal to obtain a target control parameter, thereby improving the control precision of the hybrid excitation synchronous motor.

The invention discloses a dual-electric brake boosting system, which belongs to the technical field of vehicle brake control and comprises the following steps: S1, dynamically calculating quadrature-axis inductance parameters, generating a synchronous magnetic field signal and inhibiting electromagnetic interference of dual motors; s2, extracting a harmonic-rotating speed mapping relation, and dynamically correcting an observation equation to control a parameter error; s3, reconstructing neutral point voltage to counteract high-frequency jitter, and reducing residual harmonic interference through adaptive filtering; s4, establishing a three-dimensional thermal field to predict copper and iron loss, dynamically adjusting current and generating an inverse time limit protection curve; s5, splitting the hot parameters into parity check frames, optimizing a communication protocol and compressing a refresh cycle to 10ms; s6, overlapping response of the white noise excitation system, and performing closed-loop calibration on the filtering weight to improve the observation precision; s7, triggering an audible and visual alarm and synthesizing a directional voiceprint and a tactile pulse to ensure safety regulations and perception intensity; the method has the beneficial effects that the method has remarkable advantages in a high-temperature and high-vibration severe environment, and the reliability and the safety of a system can be improved.

The invention provides an unmanned aerial vehicle motor rotating speed control method based on active disturbance rejection. The method comprises the following steps: 1) measuring the rotating speed of a motor by adopting a high-precision rotating speed sensor based on a quantum effect; 2) designing a tracking differentiator TD to smooth an expected rotating speed, and outputting a rotating speed tracking signal and a rotating speed tracking signal change rate; 3) introducing a dynamic weight matrix to form a dynamic weight expansion state observer DWESO, and taking an output state variable estimation value and a total disturbance estimation value as the input of the NLSEF; 4) automatically searching the optimal combination of the gain parameters of the DWESO by adopting an ALA (artificial Louver Mouse Algorithm), so that the optimal state variable estimation value and the optimal total disturbance estimation value are output to the NLSEF; compared with the prior art, the method for controlling the rotating speed of the unmanned aerial vehicle has the advantages that the dynamic response speed, the anti-jamming capability and the parameter adaptability of the system are remarkably improved, and the flight performance and the robustness of the unmanned aerial vehicle are improved.

The invention discloses a motor intelligent control method and system based on a self-adaptive control algorithm, and the method comprises the steps: obtaining motor operation parameters, and determining an overall control target according to the motor operation parameters; on the basis of the overall control target and the current working condition information, initial control parameters are calculated through a self-adaptive control algorithm; monitoring interaction parameters between the motor and the load; the initial control parameters are dynamically adjusted, and the running state of the motor is optimized; in a preset motor prediction model, based on the historical operation data and the current real-time data of the motor, predicting the operation state change of the motor, obtaining a motor prediction result, formulating an adjustment control strategy, and triggering a motor control instruction; the actual operation state of the motor is collected, and a state difference value is calculated; if the state difference value exceeds the threshold range, triggering a generator exception processing instruction; adjusting control parameters are obtained; triggering an adjustment instruction based on the adjustment control parameter; the control effect and the self-adaptive capability of the motor control system are improved.

The invention relates to a dual three-phase permanent magnet synchronous generator rectification system and a direct current voltage stabilization control method thereof. Comprising a dual three-phase permanent magnet synchronous generator, a VSD decoupling converter, a load current observer, a dynamic load feed-forward compensator, a voltage closed-loop controller, a dead-beat predictive current controller, a maximum four-vector SVPWM modulation module and a six-phase rectifier. The double-three-phase topological structure adopted by the invention has the advantages of multiple control degrees of freedom, high reliability and the like. A dynamic load feed-forward compensator is constructed to perform dynamic feed-forward compensation on quadrature-axis current of the motor, so that the recovery time of bus voltage is shortened, and the voltage fluctuation amplitude is reduced; the designed dead-beat predictive current controller optimizes the dynamic characteristics of a current loop, improves the torque tracking performance of the motor, and further improves the voltage stabilization regulation capability of the system. The designed load current observer realizes the analysis redundancy of current detection and improves the reliability of the system.

The invention discloses a three-phase permanent magnet synchronous motor full-speed-domain driving control system and a control method thereof. The system is composed of a torque limiting module, a full-speed-domain control neural network module, a current control module, a coordinate transformation module-I, a coordinate transformation module-II, a coordinate inverse transformation module, an SVPWM module, a SiC MOSFET driving module, a full-speed-domain position rotating speed observation module, a weak magnetic feedback module and a three-phase permanent magnet synchronous motor. The control method is used for driving the control system. According to the method, accurate tracing of the optimal dq-axis reference current working point is realized based on deep learning, and accurate control of three operation modes of MTPA, field weakening control and MTPV is realized; an improved mobile vision field estimation algorithm is adopted, and the rotor position and the rotor rotating speed are accurately estimated within the full-speed domain range; the SiC MOSFET is used as an inverter switching tube, and a brand new drive circuit is designed to adapt to the operation condition of the electric vehicle.

Provided in the present invention is an interference-factor-based high-safety and stable control method for an onboard magnetically suspended flywheel battery system. The method comprises: in the case of a safe operating condition interference, obtaining the difference between a displacement reference signal and an actual displacement signal, and inputting the difference into a Butterworth-filter-fused fuzzy active disturbance rejection control module, so as to obtain control currents, wherein the control currents comprise a radial control current, a torsion control current and an axial control current; the radial control current and the torsion control current are subjected to Clark inverse transformation, and the axial control current is subjected to power amplification, and the three currents are then compensated for, such that a three-phase control current of a radial coil, a three-phase control current of a torsion coil, and a control current of an axial coil are obtained; the control currents control the corresponding coils, and are also input into a multi-information extended Kalman filtering prediction module, such that the actual displacement signal is output; and in the case of a dangerous operating condition interference, cutting off an output signal from an operating condition recognition and safety determination module. The present invention can improve the stability, robustness and anti-interference capability of a system.

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 discloses a multi-cycle prediction compensation method for a servo motor based on an M3 kernel, and belongs to the technical field of industrial automation control, and the method comprises the steps: obtaining real-time operation parameters of the servo motor, carrying out the compensation for the rapid change of the servo motor, and generating a short-cycle prediction compensation amount; generating a long-period prediction compensation amount by analyzing a long-period operation rule of the servo motor; generating an instantaneous confidence coefficient according to the real-time operation parameters and the short-period prediction compensation amount, and generating a trend confidence coefficient based on historical precision data of the long-period prediction compensation amount; and calculating a dynamic weight according to the instantaneous confidence coefficient and the trend confidence coefficient, carrying out fusion processing on the short-period prediction compensation amount and the long-period prediction compensation amount, generating a final compensation instruction, and superposing the final compensation instruction to an output end of a servo motor control ring. According to the method, disturbance is processed by constructing short-period and long-period prediction with differential precision in parallel, and the dynamic response speed and tracking precision of the servo system can be remarkably improved.

The invention belongs to the technical field of motor control, and relates to a permanent magnet synchronous motor sensorless control method, system and hardware circuit, angular velocity estimation is designed based on a q-axis current model under a two-phase rotating coordinate system, angular velocity estimation compensation is designed based on a d-axis current model, and a rotating speed correction coefficient is introduced. And dynamic compensation of the estimated angular velocity value is realized. An accurate angular velocity estimated value and an accurate rotor angle estimated value are obtained by constructing a Romberg observer with an angular velocity estimated value and a rotor angle estimated value as outputs based on a dq rotating coordinate system. Taking an angular velocity error obtained by a motor angular velocity given value and a motor angular velocity estimated value as input of an outer ring PI controller, so as to ensure that the angular velocity estimated value tracks the motor angular velocity given value and output a q-axis current reference value; taking the difference value between the q-axis current reference value and the q-axis current sampling value and the difference value between the d-axis current reference value and the d-axis current sampling value as the input of an inner ring PI controller so as to ensure the bandwidth and the influence capability on the load, and outputting a d-axis voltage reference value and a q-axis voltage reference value; and converting the d-axis voltage reference value and the q-axis voltage reference value into voltage reference values under a two-phase static coordinate system based on the rotor angle estimation value so as to divide SVPWM drive circuit sectors, select voltage vectors, calculate action time, control on and off time of a permanent magnet synchronous motor switching device, and control on-off time of the permanent magnet synchronous motor switching device. And the alternating current speed regulation performance of the permanent magnet synchronous motor without the position sensor under the Romberg observer method is ensured.

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 relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control system and method for an electric drive hydraulic source, and the system comprises a data collection module which is used for collecting the state parameters of the electric drive hydraulic source and the operation data of a permanent magnet synchronous motor in real time through multiple sensors; the sensor redundancy and fault-tolerant module is used for performing parallel sampling through hardware dual-redundancy configuration and dual sensors; the self-adaptive fuzzy logic control module is used for dynamically adjusting a rotating speed instruction and a torque instruction of the permanent magnet synchronous motor based on the collected state parameters; a data driving optimization module; and a digital twinning prediction maintenance module. A self-adaptive fuzzy logic control module is used for dynamically generating a motor rotating speed and torque instruction based on parameters such as real-time pressure, flow deviation and change rate, a fuzzy logic algorithm is used for processing a nonlinear time-varying load and quick response, a self-adaptive fuzzy logic control rule weight and a power conversion circuit parameter are optimized through a reinforcement learning algorithm, and the control precision is improved. And dynamic self-optimization of permanent magnet synchronous motor control is realized.

An electric machine is provided. A polyphase machine is provided. A power inverter is electrically connected to the polyphase machine. A controller is electrically connected to the power inverter, wherein the controller provides switching signals to the power inverter, wherein the controller comprises a trajectory calculator that provides an optimized trajectory for transitioning the polyphase machine from a first torque to a second torque.

A motor controller for pulse heating of a power battery. The motor controller includes an inverter circuit, the inverter circuit includes three switching transistor bridge arms, two ends of each switching transistor bridge arm are respectively configured to connect to positive and negative electrodes of the power battery through a direct current bus, and bridge arm midpoints of the three switching transistor bridge arms are separately configured to connect to a three-phase winding of a motor. The bridge arm midpoints of the three switching transistor bridge arms are configured to output a three-phase current, the three-phase current is used to generate a pulse alternating current on the direct current bus, and the pulse alternating current is used to heat the power battery. A current of each phase of the three-phase current is a unipolar triangular wave, and a sum of the three-phase current is zero.

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 discloses a wide-speed-domain position-sensorless control method for a permanent magnet synchronous motor, and the method comprises the steps: building an extended state observer of the permanent magnet synchronous motor, and obtaining the counter electromotive force of the motor through the extended state observer; a self-adaptive vector filter is adopted to filter out higher harmonics in the back electromotive force obtained by the extended state observer, estimated back electromotive force is obtained, the obtained estimated back electromotive force serves as input, position information contained in the estimated back electromotive force is demodulated through a phase-locked loop position observer, and the rotating speed and the position angle of the motor are obtained; and controlling the permanent magnet synchronous motor according to the obtained rotating speed and position angle. The position observer is used for extracting position information from the rotor back electromotive force, a mechanical position sensor is omitted, the system structure is simplified, cost is reduced, the self-adaptive vector filter is used for filtering harmonic waves for estimating the back electromotive force, and estimation errors of the rotating speed and the position angle of the motor are reduced. And the high efficiency and the stability of position-sensorless control of the motor in a wide speed range are improved.

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 decoupling control method and system for a matrix motor with harmonic suppression capability, and belongs to the technical field of motor driving systems, and the method comprises the steps: building a matrix motor current loop mathematical model which considers a DC disturbance term and an AC disturbance term at the same time; based on the matrix motor current loop mathematical model, establishing a self-adaptive quasi-resonance extended state observer which simultaneously considers direct current disturbance observation and alternating current disturbance observation and performs self-adaptive adjustment on control parameters; the direct current and alternating current lumped disturbance of a matrix motor current loop is observed in real time through an adaptive quasi-resonance extended state observer; compensating the observed direct current and alternating current lumped disturbance in a forward controller of the current loop and obtaining a voltage control quantity; and controlling the matrix motor through a voltage vector space transformation and SVPWM modulation method according to the voltage control quantity. The method is improved and optimized based on an existing matrix motor vector control system, and the system does not need to be modified on a large scale.

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.