436results about "Modelling/simulations for control" patented technology

The invention is suitable for the technical field of brushless motor control, and provides an intelligent brushless motor and a control method thereof, and the method is applied to a control system of the intelligent brushless motor. The system comprises a data acquisition module, a model construction module, a training tuning module, a model verification module, a parameter setting module, an operation application module, a wireless communication module, a memory, an alarm, a processing center and an intelligent mobile terminal. The data acquisition module, the model construction module, the training optimization module, the model verification module, the parameter setting module, the operation application module, the wireless communication module, the memory and the alarm are respectively connected with the processing center; the invention also provides an intelligent brushless motor. According to the method, the AI model is constructed and trained by collecting the daily motor operation quality data, and the operation control parameters are matched according to the model, so that various operation quality abnormalities are prevented, and accurate control of the motor is ensured.

The invention provides a multi-mode control method and system for a brushless doubly-fed motor, and relates to the technical field of motor control, and the method comprises the steps: extracting torque-rotating speed slopes and dynamic response time constants of two points based on a steady-state working point of a current mode and an expected working point of a target switching mode, and constructing a linear correction function; fusing an output value of the linear correction function with an optimization result to generate a modal transition compensation coefficient, and injecting the compensation coefficient into a rotor side voltage control ring based on a feedforward control channel to generate an anti-disturbance dynamic compensation control strategy; and for a non-smooth adjustment item in the dynamic compensation control strategy, performing smoothing processing on a discontinuous boundary of a control quantity by adopting a subgradient projection algorithm, verifying continuous conductivity of a control curved surface in combination with a Lyapunov stability criterion, and generating a final control instruction. According to the invention, adaptive smooth switching control of the brushless doubly-fed motor can be realized.

The invention provides a brushless motor multi-axis cooperative control method and system for an industrial robot, and relates to the technical field of control, and the method comprises the steps: processing the real-time parameters of a brushless motor of each joint axis through constructing an extended dynamic model, and obtaining a temperature drift coefficient and a load inertia parameter; a self-adaptive pre-compensation model, a neural network compensator and a recurrent neural network prediction model are combined, a torque control instruction is output, a three-phase current prediction value is calculated, a distributed model prediction controller is adopted to generate an optimal switching sequence, multi-axis cooperative control is achieved, and the system stability and the response speed can be effectively improved.

The invention discloses a degradation trend prediction method for a brushless direct current motor, and relates to the technical field of motor control, current, temperature and vibration signals of motor operation are collected, a characteristic signal transmission path model of a winding, a bearing and magnetic steel is constructed, and the degradation trend of the brushless direct current motor is predicted by analyzing the reflection characteristics and attenuation conditions of degradation signals of all parts. Extracting characteristic quantities of mutual influence among current, temperature and vibration; according to the method, a multi-component characteristic signal transmission path model is constructed, degradation signals are regarded as fluctuation quantity transmitted and reflected among different components, mutual influence of current, temperature and vibration signals is comprehensively analyzed, false characteristics are separated in combination with heat transfer lag characteristics and cooperative change critical points, accurate extraction of real degradation characteristics is realized, and the accuracy of the degradation characteristic extraction is improved. The problem that weak degradation signals are covered by interference is solved.

The invention provides a permanent magnet frequency conversion double-sine modulation swimming pool pump energy-saving control method, and relates to the technical field of motor driving and control, and the method comprises the steps: building a double-sine carrier modulation model of a permanent magnet synchronous motor, and calculating a dynamic harmonic suppression factor and a double-sine modulation depth in real time; feedforward compensation is carried out on the inverter current based on the DHSIF, and a q-axis current reference value is generated by adopting double-closed-loop vector control; dynamically adjusting a carrier frequency range according to the DSMD, generating double sine carriers with a phase difference of pi / 3, and superposing the double sine carriers into a composite modulated wave; the modulation wave amplitude is corrected through a DHSIF, and fifth and seventh harmonics are eliminated in combination with an adaptive filtering algorithm; a carrier wave ratio is dynamically optimized to generate a PWM signal, and a transient compensation mechanism is triggered to correct a voltage instruction when a load suddenly changes; and an energy efficiency optimization database is constructed to call historical optimal parameters, and nonlinear mapping of DHSIF and motor characteristics is established through parameter self-tuning. According to the invention, cooperative control is realized through dynamic harmonic suppression, modulation depth optimization and intelligent thermal protection.

The invention discloses a model prediction control method of a high-speed permanent magnet synchronous motor and a related device, and relates to the field of control, and the method comprises the steps: building a stator current equation capable of simulating the behavior of the high-speed permanent magnet synchronous motor based on the position change of a rotor in the same control period, a high control input basic voltage value is obtained by fixing motor parameters and combining a total disturbance parameter of a current loop system and a set stator current tracking target at the next moment at the same time; on the basis of the control input basic voltage value, current loop harmonic voltage compensation is carried out through the control input harmonic compensation voltage value to obtain a control input voltage value, and voltage modulation is carried out according to the control input voltage value to control the high-speed permanent magnet synchronous motor. According to the method, when the stator current equation is constructed, rotor position change parameters in the same control period are innovatively introduced, so that the motor behavior simulation precision is remarkably improved; the defect that the switching frequency is not fixed in a traditional finite set model predictive control algorithm is effectively overcome through the voltage modulation technology.

The invention discloses an intelligent control method and system for a brushless direct current motor and a storage medium, and the method comprises the steps: constructing a mathematical model of a rotating speed and current double-closed-loop speed regulation system of the brushless direct current motor and a simulation model of a control system of the brushless direct current motor, and controlling a rotating speed PID (Proportion Integration Differentiation) controller by using an improved Sabot swarm optimization algorithm; when a set termination condition is met, outputting the global optimal position of the salmons individual corresponding to the population position of the current salmons population, and obtaining a target value of the to-be-optimized parameter according to a solution corresponding to the global optimal position of the salmons individual; controlling the brushless direct current motor based on the target value of the to-be-optimized parameter; according to the method, an improved saloat swarm optimization algorithm is fused with a Gaussian random walk strategy and a warning mechanism of a sparrow algorithm, and a chaotic mapping optimization initial population is introduced and is used for optimizing a fuzzy rule, so that the convergence speed and precision of searching the fuzzy rule are improved, and the problem that parameters of a PID controller are difficult to set is more effectively solved.

The invention relates to the technical field of motor control, and discloses an axial magnetic flux hybrid excitation synchronous motor field weakening control system based on multi-source feedback. According to the system, a motor digital twinborn model is constructed, and real-time operation data are collected by using a multi-source sensor and synchronized to the model; establishing a three-dimensional electromagnetic field state space based on the model, extracting features through a manifold neural operator, and generating a multi-dimensional excitation state feature set; determining an excitation anomaly feature index set according to the feature set distribution density; collecting data in real time, extracting current excitation characteristic parameters, mapping the current excitation characteristic parameters to a three-dimensional electromagnetic field state space, calculating spatial correlation with the abnormal index set, and outputting weak magnetic demand correlation factors; and finally, according to the correlation factor, the feature set and real-time data iteration updating operator parameters, a control instruction is generated to dynamically adjust the coupling proportion of the electrical excitation component and the permanent magnet excitation component. According to the system, comprehensive perception and accurate regulation and control of the motor operation state are realized, and the adaptability and reliability of field weakening control are improved.

The invention relates to the technical field of motor control, discloses a direct current brushless motor control method and system based on current prediction, and aims to solve the problems of large torque ripple, low efficiency and poor stability caused by current loop lag in the prior art. The method comprises the following steps: acquiring a motor operation state signal in real time; determining a sector and an electrical angular velocity based on the rotor position and the rotational speed; inputting related parameters into the mixed current prediction model to obtain a three-phase current prediction value of the next period; a prediction error is calculated in combination with a current instruction, and an optimal voltage vector enabling a cost function to be minimum is solved through a model prediction control algorithm; and finally, a space vector pulse width modulation signal is generated to drive an inverter. According to the method, advanced control is realized by fusing a physical model and a data-driven prediction mechanism, the torque ripple is remarkably reduced, and the energy efficiency and robustness are improved.

The invention discloses a deep-cone thickener rake frame driving method and system based on a permanent magnet motor, and belongs to the technical field of mine solid waste disposal and filling, and the method comprises the following steps: collecting real-time operation parameters of the permanent magnet motor, the real-time operation parameters including stator current, voltage, rotating speed and output torque; based on the output torque, whether the deep-cone thickener is in a rake pressing state or not is recognized, and when the deep-cone thickener is in the rake pressing state, the reference rotating speed of the permanent magnet motor is controlled to be reduced; the real-time temperature of a stator winding and a rotor permanent magnet area in the permanent magnet motor is obtained, and when the real-time temperature is higher than a preset threshold value, the output power of the permanent magnet motor is controlled; acquiring historical load data of the deep-cone thickener, and modeling the historical load data and the output torque to obtain a historical load-torque model; and constructing a torque change prediction model based on the historical load-torque model, predicting future torque data by using the torque change prediction model, and identifying an impending rake pressing risk.

The invention provides an efficiency optimization control method of a permanent magnet synchronous motor and related equipment. The method comprises the following steps: acquiring an output current of an inverter; constructing an inverter loss model based on the output current of the inverter and the inverter loss coefficient; constructing a total loss model based on the electromagnetic loss model, the inverter loss model and an equivalent conversion method; constructing a current population by taking the plurality of d-axis current values as individuals; and performing optimization solution based on the total loss model, the current population, a PID search algorithm and an artemisinin optimization algorithm to obtain a global optimal solution and control the permanent magnet synchronous motor. According to the scheme, the driver loss is added into the total loss model, and efficiency optimization can be carried out on the whole motor driving system; in order to solve the problem that a traditional method is not suitable for an IPMSM, a PID search algorithm and an artemisinin optimization algorithm are combined, parameter search is carried out on a motor efficiency optimal point by simulating an incremental PID controller adjusting mechanism, a global optimal solution is obtained, and therefore optimal control over the click efficiency is achieved.

The invention provides a permanent magnet synchronous motor sliding mode control method and system based on disturbance compensation, and the method comprises the steps: constructing a first motor state equation based on a permanent magnet synchronous motor mathematical model containing disturbance; secondly, on the basis of a sliding mode control theory, a novel adaptive reaching law and a non-singular fast terminal sliding mode surface are constructed, stability proving is completed, and the fast convergence capability is analyzed; designing a novel speed ring controller through the state equation, the reaching law and the sliding mode surface; designing a sliding mode disturbance observer for a disturbance part in the speed loop controller; and finally, carrying out FOC speed regulation control on the permanent magnet synchronous motor by using the designed speed ring controller and the disturbance observer. Compared with an existing well-known sliding mode control scheme, the hybrid control method of the speed loop controller and the sliding mode disturbance observer is adopted, system buffeting is effectively weakened, meanwhile, the dynamic response speed is considered, and the system robustness is improved.

The invention discloses a BLDC (brushless direct current) driving parameter self-tuning embedded system and method for multiple equipment types, and the method comprises the steps: constructing a BLDC unified mathematical model containing an electromagnetic torque equation, a back electromotive force equation and a mechanical motion equation, and building a nonlinear mapping relation library of motor type identification codes and a key electrical parameter set; a predefined multi-mode excitation signal sequence is injected into the to-be-driven BLDC motor, dynamic response data are synchronously collected, and the dynamic response data are used for representing the characteristics of the key electrical parameter set; based on collected dynamic response data, equivalent inductance and equivalent resistance in a key electrical parameter set are decoupled and extracted through a frequency domain impedance analysis method, and meanwhile, a counter electromotive force coefficient and rotational inertia in the key electrical parameter set are identified by combining time domain counter electromotive force zero crossing point detection with rotating speed differential operation. Based on a motor fingerprint feature library and a fuzzy adaptive architecture, multi-model debugging-free plug-and-play is realized, and stable control performance is maintained under a dynamic working condition.

The invention discloses an EMC simulation modeling and optimization prediction method based on a permanent magnet brushless direct current motor driver. The method comprises the steps of obtaining first interference source data based on a driver real object; performing near-field electric field simulation based on the primary driver PCB and the first interference source data to obtain a primary near-field electric field radiation simulation cloud picture; obtaining an optimized driver PCB based on the initial edition of the near-field electric field radiation simulation cloud picture, and performing near-field electric field simulation based on the optimized driver PCB and the first interference source data to obtain an optimized near-field electric field radiation simulation cloud picture; judging whether optimization design is needed or not according to the near-field electric field radiation simulation nephograms obtained twice; and if the optimization design is not needed, acquiring second interference source data corresponding to the optimized driver PCB and judging whether the second interference source data reaches a preset target, and if the second interference source data does not reach the preset target, continuing optimization based on the optimized near-field electric field radiation simulation cloud picture. The method is low in complexity, rapid and accurate in simulation and lower in cost.

The invention discloses a permanent magnet synchronous motor cogging torque suppression method and device based on coordinate analysis, and belongs to the technical field of permanent magnet synchronous motors, and the method comprises the steps: effectively suppressing the cogging torque of a permanent magnet synchronous motor through collaborative optimization of the geometric parameters of stator and rotor auxiliary slots, reducing the torque pulsation, and improving the vibration noise, thereby improving the cogging torque of the permanent magnet synchronous motor. The comprehensive performance of the motor is improved. Wherein the stator auxiliary slots weaken periodic components related to stator tooth slots in air gap magnetic conductance harmonic waves by adjusting slot widths and depths, and excitation of the periodic components to tooth slot torque is reduced; the rotor auxiliary grooves optimize the pole-arc coefficient by adjusting the opening angle, and remanence harmonic waves of the permanent magnets are inhibited from being periodically matched and coupled with the stator tooth grooves. The collaborative design of the two enables the amplitudes and phases of air-gap magnetic conductance harmonic waves and permanent magnet residual magnetic harmonic waves to be mismatched, the amplitude of cogging torque is remarkably reduced, meanwhile, periodic disturbance of an air-gap magnetic field is reduced, the output torque is more stable, and mechanical vibration and noise caused by electromagnetic force fluctuation are synchronously weakened.

According to an aspect of the invention, there is provided a method of controlling a discontinuous linear motor system comprising a mover and a motor, the method comprising: obtaining a set of current values representing current flowing through coils of the motor, wherein a reaction plate of the mover is in a position relative to the coils; obtaining a set of voltage values representing voltage present over the coils; predicting a velocity, acceleration and / or position of the mover from the set of current values and the set of voltage values; and, calculating input parameters for control of the motor to move the mover to a desired velocity, based on the prediction.

The invention discloses a rapid calculation method for electromagnetic vibration of a permanent magnet synchronous motor, and belongs to the technical field of electromagnetic field calculation. According to the method, a magnetic potential and magnetic conductance method is innovatively combined with an improved sub-domain method, the problem of complexity of electromagnetic field distribution of a nonlinear material is effectively solved through the magnetic potential and magnetic conductance method, and the capture precision of electromagnetic field changes is remarkably improved by dividing a computational domain into a plurality of sub-domains through the improved sub-domain method. According to the method, the influence of the tangential electromagnetic force wave and the harmonic current is fully considered, and high-precision calculation of the electromagnetic force can be realized. On the basis, a stator core shell equivalent theoretical model is established, the inherent frequency of the stator is calculated by adopting an energy method, and the accuracy of the model is verified through a modal experiment. Furthermore, by determining the frequency response function of the stator in the low-order mode, the acceleration spectrum and electromagnetic force wave distribution of the prototype stator in each mode are obtained based on the linear superposition principle. Compared with a traditional electromagnetic-structure coupling finite element model, the method has the advantages that the calculation efficiency is remarkably improved while the calculation precision is guaranteed, the calculation time is shortened by 59.3%, and efficient and reliable technical support is provided for optimization design of electromagnetic equipment.

The invention discloses an improved multi-vector combined model predictive control method and device for a permanent magnet synchronous motor. The method comprises the following steps: acquiring a three-phase current of each sampling period, and converting the three-phase current into a d-axis current and a q-axis current; performing one-beat delay compensation on the current, predicting a reference voltage of a next beat, and judging a sector where the reference voltage is located; selecting a corresponding voltage vector output combination in the reconstructed voltage vector selection table by combining the sector and the previous period zero vector; solving a cost function only containing voltage vectors and action time by using the KKT theorem, and calculating the action time of each voltage vector; and generating a pulse signal according to the action time for driving the output of an inverter of the permanent magnet synchronous motor. According to the method, the advantages of fast dynamic response, easy processing of multiple constraints and the like of model prediction control are maintained, and meanwhile, lower calculation complexity, fixed switching frequency and higher reliability are realized, so that the comprehensive performance of a permanent magnet synchronous motor control system is improved.

The invention belongs to the technical field of parameter estimation of machines, and particularly relates to a permanent magnet synchronous motor parameter identification method based on an improved eel-grouper algorithm. The method comprises the following steps: establishing a full-rank discrete voltage equation set of a mathematical model of a PMSM under a dq coordinate system; constructing a fitness function reflecting an error between an actual parameter output value and a theoretical output value of the motor; performing parameter identification on the permanent magnet synchronous motor by using an improved eel-grouper optimization algorithm, and initializing a population by using an elite reverse learning mechanism; a Levy flight strategy is introduced in the algorithm development stage; introducing a reconnaissance alert strategy of a sparrow search algorithm; a chaotic Tent search strategy is introduced, a fitness function is calculated, parameter optimization is carried out through algorithm iteration, iteration is stopped after the maximum number of iterations is reached, and optimal identification parameters are output. By improving a search strategy and a parameter adjustment mechanism of the algorithm, the precision and robustness of parameter identification are further improved, and more reliable parameter support is provided for high-performance control of the permanent magnet synchronous motor.

The invention discloses a motor multi-parameter identification method based on sampling noise excitation and bias compensation, and the method comprises the steps: firstly obtaining the voltage and sampling current of a motor, and enabling the sampling current to comprise sampling noise; then constructing an information evaluation index, and adjusting a proportional parameter of a current controller to improve the identification information amount; then, shaft voltage feed-forward compensation is constructed to suppress electromagnetic torque pulsation; recursion is carried out by adopting a least square method based on the discrete linear parameterization model to obtain a parameter initial value and a covariance matrix; secondly, estimating offset-containing parameters on line, and calculating a sampling noise variance by using an estimated residual error; and finally, obtaining unbiased parameter estimation according to bias compensation iteration updating so as to output online estimation values of stator resistance, direct-axis inductance Ld, quadrature-axis inductance Lq and permanent magnet flux linkage.

The invention discloses a discrete modeling and position-free control method for a low-carrier-ratio long-cable permanent magnet motor, and belongs to the field of motors. In order to solve the problem that the precision of a conventional mean voltage model is reduced due to the remarkable resistance characteristic of a low-carrier-ratio long-cable permanent magnet synchronous motor, a switching period is decomposed into a plurality of zero-order holding voltage excitation sections according to the discrete characteristic of the output pulse width modulation voltage of a converter; a motor accurate discrete model considering resistance characteristics is established by solving accurate current response of the permanent magnet motor segment by segment, a dead-beat current controller and a motor back electromotive force observer are further established based on the discrete model, and sensorless operation of the low-carrier-ratio long-cable permanent magnet synchronous motor system is realized. The discrete modeling precision of the low-carrier-ratio long-cable permanent magnet synchronous motor is improved, and the position-free control performance of the permanent magnet synchronous motor system under the working condition is improved.

The invention belongs to the technical field of permanent magnet synchronous motor control, and particularly discloses a permanent magnet synchronous motor finite time switching active-disturbance-rejection rotating speed control method, which comprises the following steps: acquiring a reference rotating speed and an actual rotating speed of a permanent magnet synchronous motor; the absolute value of the error between the reference rotating speed and the actual rotating speed is calculated and compared with a preset first threshold value and a preset second threshold value, and the stage of the permanent magnet synchronous motor is judged; constructing a finite time cascade expansion state observer comprising a first-stage finite time ESO and a second-stage linear ESO to observe the state and lumped disturbance of the permanent magnet synchronous motor; and based on the stage of the permanent magnet synchronous motor, selecting a composite state of the observation value of the first-stage finite time ESO, the observation value of the second-stage linear ESO or the observation value of the first-stage observer and the observation value of the second-stage observer generated based on the Bezier curve to generate the rotating speed loop control law of the permanent magnet synchronous motor. According to the invention, the problems that the transient anti-interference capability is weak and the current performance is reduced due to the fact that transient control and steady-state control are not distinguished in the prior art are solved.

The invention discloses a multi-target intelligent optimization efficient control method for a cascaded multilevel electric drive system, and the method comprises the steps: constructing a motor broadband domain loss model and a converter loss model, and obtaining an electric drive system loss model after unifying current variables; deriving a motor torque ripple model taking a specific secondary current harmonic component as a component based on a magnetic field modulation principle; establishing an electric drive system loss multi-objective optimization model which takes a fundamental current angle and a specific harmonic current component as two-dimensional optimization variables and takes torque ripple performance into account; and performing particle optimization configuration in the two-dimensional optimization variables of the fundamental current angle and the specific harmonic current component by applying a particle swarm algorithm, solving the multi-objective optimization model, and obtaining the optimal fundamental current angle and the optimal harmonic current component setting. According to the method, the multi-target and multi-dimensional complex problem of the electric drive system is solved by adopting the particle swarm optimization, global optimization can be carried out from the multi-control-dimension angle, and the defect that a traditional optimization algorithm is converged to a local optimal point is overcome.

The invention discloses an irreversible demagnetization risk prediction method for a permanent magnet synchronous motor, and the method comprises the steps: constructing a relation model between the temperature of a magnetic block and the no-load counter electromotive force of the motor, and calculating a target no-load counter electromotive force at a highest allowable environment temperature through employing a reference no-load counter electromotive force. According to the method, a complete physical relation chain from the magnetic block temperature to the residual magnetism density and then to the motor no-load magnetic flux and the motor no-load counter electromotive force is considered, the motor no-load counter electromotive force at the highest allowable environment temperature is obtained based on the relation model by using the reference no-load counter electromotive force to serve as the target no-load counter electromotive force, and accurate assessment of the irreversible demagnetization risk is achieved. In the running process of the motor, the no-load counter electromotive force of the motor is monitored when the motor driving current is zero and the motor is in the no-load running state every time, when the difference value between the target no-load counter electromotive force and the monitored no-load counter electromotive force exceeds a preset threshold value Eh1, early warning is triggered, and when the difference value exceeds Eh2, shutdown protection is triggered; risk monitoring of the magnetic performance state is achieved by indirectly monitoring the no-load back electromotive force.

The invention discloses a non-singular fast terminal sliding mode observer based on a neural network phase-locked loop. A Sigmoid function is adopted to design a non-singular fast terminal sliding mode surface in the observer; a stator current error system is defined, an equivalent item of a sliding mode control law is obtained according to an equivalent sliding mode control principle, a switching control law is designed for control of the stator current error system state on a sliding mode surface, and the sliding mode control law in the observer is obtained according to the equivalent item and the switching control law; introducing a rapid tracking differentiator comprising a terminal attractor, wherein the rapid tracking differentiator is used for accurately tracking and filtering the back electromotive force; and a phase-locked loop in the observer is controlled through the BP neural network, and the phase-locked loop is used for extracting an electrical angle and an electrical angular velocity signal from the back electromotive force to obtain a final sliding-mode observer. According to the method, the convergence speed is effectively increased, high-frequency buffeting is reduced, phase delay is compensated, and the dynamic performance is improved.

The invention discloses a brushless motor control system and control method, and belongs to the technical field of motor control and driving. Comprising a real-time detection module, a control, regulation and control analysis module and a self-adaptive compensation module, multi-source operation data of the brushless motor are collected in real time, the control, regulation and control analysis module carries out depth correlation analysis, potential characteristics such as parameter sensitivity, observer coupling degree and stability attenuation index are mined, potential influences are quantified through analog simulation, and a comprehensive influence value is obtained. And the self-adaptive compensation module intelligently selects a compensation type according to the numerical value, calculates an accurate compensation data value, and performs dynamic closed-loop correction on controller parameters. According to the method, the problems of insufficient adaptive capacity and compensation lag caused by difficulty in coping with parameter time variation and potential coupling in the prior art are solved, the conversion from passive response to active prevention is realized, and the robustness, the control precision and the long-term operation stability under complex working conditions are improved.

The invention relates to the technical field of automation control, and discloses a brushless motor energy-saving control adjustment method and system, a controller and a storage medium, and the method comprises the following steps: S1, collecting the operation state information of a motor: collecting the operation state information, including current, voltage, temperature and load data, of the brushless motor in real time; s2, dynamic modeling and numerical solution: constructing a dynamic model of the brushless motor system based on the acquired running state information, and modeling by adopting a high-order differential equation set; s3, constructing a control cost function; s4, deducing an optimal control strategy; s5, approximately solving a control strategy; s6, adjusting and executing control parameters; and S7, performing feedback and parameter correction. By introducing the low-pass filter, the control change rate constraint and the rolling optimization mechanism, the dynamic closed-loop control effect of more stable control, more accurate feedback and more synchronous response is realized.

The invention discloses a motor temperature prediction method and system, and relates to the technical field of detection. The motor temperature prediction method comprises the following steps: acquiring current operation data of a motor; obtaining a motor temperature prediction model; wherein the motor temperature prediction model is obtained by training based on a first preset neural network model and a second preset neural network model; and determining the temperature of the motor according to the current operation data, the motor temperature prediction model and a first preset neural network model. According to the motor temperature prediction method provided by the invention, the motor temperature prediction model is trained through the first preset neural network model and the second preset neural network model, so that the prediction precision of the motor temperature prediction model is improved; the motor temperature is predicted through the trained and optimized motor temperature prediction model and the first preset neural network model, the prediction precision is improved, the complexity of the prediction model is reduced, and the efficiency is improved.

The invention discloses an adaptive model prediction control method for an aviation electromechanical actuator, relates to the technical field of aviation electromechanical actuators, and designs a corresponding gear clearance torque and friction torque current compensation method by adopting a simplified dead zone model and a LuGre friction model. By organically combining gear backlash compensation and friction compensation and redesigning a control law, the invention provides a control strategy combining feed-forward compensation and model prediction control, and the q-axis current of the system is accurately compensated and controlled; furthermore, an extended Kalman filter is introduced to carry out on-line identification of multiple parameters of the permanent magnet synchronous motor, a high-precision parameter identification result is obtained, and it is ensured that the model parameters of the system can be updated in real time in the control process, so that accurate control over the system is achieved.

The invention relates to the field of power system modeling and parameter identification, and provides a synchronous generator parameter identification method based on an improved tuna swarm optimization algorithm. Dividing the full parameter set of the to-be-identified synchronous generator into a d-axis parameter subset, a q-axis parameter subset and a mechanical parameter subset according to the internal physical association of the to-be-identified synchronous generator; performing multiple improvements on the tuna swarm algorithm to obtain an improved tuna swarm optimization algorithm; integrating the to-be-identified parameters in the d-axis parameter subset and the q-axis parameter subset into a high-dimensional parameter vector, constructing a target function based on the comprehensive test data, and performing multi-parameter synchronous global identification based on the target function by adopting an improved tuna swarm optimization algorithm to obtain an identification result; and carrying out model verification and validity analysis on the identification result. According to the method, the problem of parameter correlation distortion of traditional step-by-step identification is solved, the global optimization ability and convergence efficiency of the algorithm are improved, and the method can adapt to the accurate identification requirement of the high-dimensional strong coupling parameters of the synchronous generator.