197results about "Software algorithm control" patented technology

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

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.

The invention discloses a multi-motor cooperative control method and system based on speed compensation, particularly relates to the technical field of industrial multi-motor control, and is used for solving the problems of compensation misalignment and system oscillation caused by an existing parameter asynchronous drift working condition. Current and voltage signals of each motor are collected in real time, and dynamic resistance and inductance parameters are calculated on line; constructing a parameter variation causal entropy network to quantify causal influence intensity of single machine abnormity on the system, and synchronously generating a system collaborative vulnerability index to realize risk penetration perception; when low-frequency oscillation is detected, harmonic components are extracted, and a sensitivity grading model of parameter mismatch and oscillation frequency is established; a dominant compensation role is distributed based on causal entropy to focus on a key disturbance source, a dynamic amplitude limiting instruction is generated according to collaborative vulnerability to adapt to system stability, and resonance suppression priorities are distributed according to sensitivity levels to optimize resource configuration; and finally, a torque instruction is reconstructed and synchronously issued, so that cross-equipment accurate transmission of compensation energy is realized, and mechanical coupling oscillation is eliminated.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is improved.

The invention discloses a load prediction-based regenerative braking electric hoist control method for hot-line work, and relates to the technical field of electric hoist control. The method comprises the following steps: collecting parameters of the electric hoist, and obtaining a predicted load weight according to an LSTM model; adjusting parameters of a PID controller according to the load weight, recording the parameters as initial PID parameters, generating a control signal according to the initial PID parameters, and driving the electric elevator to operate; setting a regenerative braking triggering standard, and judging whether the electric hoist reaches the regenerative braking triggering standard or not; if the electric hoist reaches the regenerative brake triggering standard, monitoring the recovered energy and generating an expected cumulative reward Q; and updating the optimized PID parameter according to the expected cumulative reward Q to obtain an updated PID parameter, and generating a control signal according to the updated PID parameter to control the electric hoist. According to the invention, the reliability of control of the regenerative braking electric hoist based on load prediction for hot-line work is improved.

A controller 1 is provided with: a motor controller (10, 11, etc.) that supplies a drive current to a motor 3, which is a power source of a processing device so as to place the motor 3 in a normal operating state, and, in response to multiplexed commands (S1, S2) that are sent from outside and relate to a safety function operation of the processing device, controls the drive current so as to place the motor 3 and the processing device in a safety stop state; and a command monitoring unit 16 that monitors whether or not the commands (High / Low of S1, S2) match each other. If a period during which the commands do not match exceeds an allowable value, the command monitoring unit 16 generates a safety stop signal Sd (High) for safely stopping the motor 3, and continuously sends the safety stop signal Sd (High) to the motor controller. This configuration makes it possible to facilitate construction of a safety system that uses the controller.

The invention discloses an electric vehicle permanent magnet motor control method and system based on temperature rise prediction, and belongs to the field of motor control. The method comprises the following steps: generating motor temperature rise sample data based on a meshless method; a chaotic mapping-dream optimization algorithm is adopted to optimize multivariate variational mode decomposition parameters, and high-precision temperature rise data are reconstructed; a GNN-BILSTM-Attention prediction model is constructed, and a space-time double Attention mechanism is fused to predict a future temperature rise; and establishing a temperature rise-electromagnetic parameter mapping model, designing a graded early warning control strategy, and forming a prediction-control closed loop. According to the invention, the speed and precision of temperature rise prediction are improved, prediction-control deep coupling is realized, and the operation reliability and control precision of the motor are significantly enhanced.

The invention discloses a method, a system and a related device for controlling a motor based on an adaptive algorithm, which are used for improving the accuracy of motor control. The method comprises the following steps: acquiring initial data of a motor; decomposing the initial data into intrinsic mode function components, and performing time-frequency transformation on the intrinsic mode function components; dynamic time-frequency features of the intrinsic mode function component are extracted, cross-band weighted fusion is carried out on the dynamic time-frequency features through a preset attention weighting mechanism, and a multi-dimensional feature vector is generated; inputting the multi-dimensional feature vector into a preset deep learning model, and constructing a target prediction model in combination with a loss function and an adaptive momentum optimization algorithm; transmitting the target prediction model to an analysis module of the motor to obtain a load change trend and a confidence coefficient; dynamically adjusting the proportion of the weight of the PID controller according to the load change trend and the confidence coefficient, and optimizing the parameters of the unit of which the proportion is adjusted through a gradient descent method; and dynamically regulating and controlling the motor based on the PID controller.

The invention provides an industrial servo motor operation control method and system, and the method comprises the steps: collecting the motion image data of load equipment in a preset track parameter, carrying out the preprocessing and positioning of the motion image data, and obtaining the real-time position coordinate data of the load equipment; determining theoretical position coordinate data corresponding to the load equipment at each acquisition moment based on a preset trajectory parameter, and comparing the real-time position coordinate data with the theoretical position coordinate data to obtain trajectory deviation data at each acquisition moment; on the basis of the trajectory deviation data in combination with motion correlation characteristics of the servo motor and a preset control target, candidate control instructions for generating a plurality of future control periods in each control period are determined; and evaluating and screening the candidate control instruction set based on a minimization strategy of the trajectory deviation data to obtain a target control instruction, converting the target control instruction into a signal, and outputting the signal to a servo motor driver. The precision of industrial production and the stability of the production process are guaranteed.

The invention provides a control method and system for a continuous mixer model pre-control algorithm, and the method comprises the steps: obtaining the variables of near-infrared numerical filtering through learning equipment operation condition information in advance, collecting the data of time, servo motor rotation speed and near-infrared filtering numerical values under different mixing degrees, and feeding back the information to a processor, the controller establishes a time-mixing uniformity data model and a time-speed data model according to the learned working condition information, processes the two models and then establishes a time-mixing uniformity-speed digital model; and according to the established digital model and the mixing uniformity controller for near-infrared measurement, the rotating speed is calculated and is transmitted to the servo driver to change the running speed of the motor, so that the purpose of effective control is achieved. The method has the beneficial effects that the hysteresis, the delay and the volatility of the PID algorithm in the prior art are solved. And the accuracy, the stability and the reliability of the existing equipment are greatly improved. The service life of equipment is ensured, and the power consumption is reduced.

The invention discloses a multi-working-condition load matching method and system for a hydraulic motor for an oil pump, and the method comprises the steps: collecting multi-source operation data of the hydraulic motor for the oil pump under different working conditions, and the multi-source operation data comprises pressure, flow and rotating speed signals; performing signal decomposition on the multi-source operation data to obtain a plurality of working condition component signals; extracting time-frequency domain characteristics of each working condition component signal, and calculating an optimal decomposition scale based on extreme point time difference and energy distribution balance; dividing dynamic load sections according to the optimal decomposition scale, and analyzing the main parameter matching degree and the working condition credibility of each load section; and generating a self-adaptive adjustment coefficient based on the main parameter matching degree and the working condition credibility, and dynamically adjusting output parameters of the hydraulic motor according to the self-adaptive adjustment coefficient to realize multi-working-condition load matching. According to the method, the credibility weight factor is calculated through the multi-dimensional parameters, the working condition credibility is accurately quantified, a reliable basis is provided for self-adaptive adjustment of the hydraulic motor for the oil pump, and the multi-working-condition operation performance is improved.

The invention relates to the field of motor control and adjustment, in particular to a motor rotating speed adjusting method and system for a remote control driving vehicle, and the method comprises the steps: obtaining and preprocessing vertical, transverse and longitudinal vibration signals and running speed data of each wheel of the vehicle; extracting a vibration anomaly feature sequence and an average vehicle speed sequence through a sliding window method, and analyzing correlation to quantify road condition features; quantizing the wheel cooperative stability based on the wheel vibration correlation and the transverse / vertical energy ratio difference degree, and fusing the maximum road condition feature value and the stability mean value to obtain a rotating speed adjustment factor; the actual target rotating speed of the motor is calculated by combining the preset vehicle speed, the transmission ratio and the like, and precise vehicle speed adjustment is achieved through PID closed-loop control. According to the method, the rotating speed adjusting factor is constructed by quantifying the three-dimensional road condition characteristics and the vehicle dynamic stability, real-time adaptive adjustment of the vehicle speed is achieved in combination with PID closed-loop control, and the safety and stability of remote control driving are remarkably improved.

The invention discloses a remote control car motor rotating speed intelligent control method and system based on an efficient response driving algorithm, and the method comprises the steps: employing a multi-objective optimization algorithm, analyzing the road condition, the car speed and environment data in real time, optimizing the operation state of a motor, and balancing the rotating speed precision, the response time and the energy consumption; a reinforcement learning algorithm is combined, and a control strategy is dynamically adjusted according to real-time feedback; the vehicle state is evaluated through a vehicle-mounted sensor, and the motor rotating speed and power output are automatically adjusted; in the low-power-consumption mode, the battery efficiency is automatically optimized, and energy recovery is carried out during braking; the system has sensor fault detection and automatic switching functions, predicts future road conditions in combination with machine learning, and optimizes a driving strategy; according to the remote control car motor rotating speed intelligent control method and system based on the efficient response driving algorithm, quick response is achieved under operation in different environments, accurate rotating speed control is provided, energy consumption is reduced through the intelligent algorithm, and the cruising ability is improved.

The invention provides a safety redundancy control method, system and device for a combined magnetic field six-phase motor and a medium thereof, and the method comprises the following steps: 1, monitoring the operation state of the combined magnetic field six-phase motor in real time in a multi-dimensional manner, and obtaining the real-time operation parameters of the combined magnetic field six-phase motor; according to the invention, through multi-dimensional real-time monitoring of the operation state of the motor and an advanced intelligent diagnosis algorithm, more accurate and faster fault diagnosis is realized, and the reliability and safety of the motor are greatly improved; the redundancy strategy is dynamically selected according to the fault condition and the motor operation state, so that the fault-tolerant capability is improved, and the influence of the fault on the motor performance is reduced; through the adaptive optimization control strategy, it is ensured that the motor always keeps the optimal output performance and safety under the fault condition, the usability and adaptability of the motor are improved, the system can continuously improve the performance of the system and adapt to different working environments and fault conditions, and stronger vitality and competitiveness are achieved.

Electrical apparatus (20) includes an inverter (24) having input terminals for receiving DC input power and output terminals for coupling to an AC power grid. A pulse-width modulation (PWM) generator and drivers (26) drive the switches so as to control respective amplitudes, frequencies, and phases of the output current waveforms of the inverter. Control circuitry (28) receives measurements of respective time-varying voltages and currents on the input and output terminals, computes a model (40) that includes three virtual currents flowing in a synchronous machine that is emulated by the apparatus, wherein the three virtual currents are associated respectively with the three output current waveforms, and controls the PWM generator and drivers responsively to the three virtual currents so as to synchronize the amplitudes, frequencies, and phases of the three output current waveforms of the inverter with the three phases of the AC power grid.

The invention relates to the field of motor control, and discloses an AI-based motor torque control and battery energy management integration method and system, and the method comprises the steps: constructing an edge computing network of a to-be-controlled motor and a to-be-managed battery; constructing a motor mixing depth DRL model of a to-be-controlled motor, and establishing a battery unsupervised management model of a to-be-managed battery; integrating the motor hybrid depth DRL model and the battery unsupervised management model into an edge computing network to obtain an integrated computing network, analyzing a motor state of a to-be-controlled motor, analyzing a battery state of a to-be-managed battery, constructing an integrated computing model of the integrated computing network, and establishing an AI multi-target algorithm of the integrated computing model to obtain an AI multi-target algorithm; and analyzing the integrated control instruction of the to-be-controlled motor and the to-be-managed battery, and executing integrated control of the to-be-controlled motor and the to-be-managed battery based on the integrated control instruction. According to the invention, the integrated collaborative optimization effect of motor torque control and battery energy management can be improved.

A hydraulic system for a vehicle is provided. The hydraulic system includes a first electric motor, a second electric motor, a gearbox arranged to be driven by the first and second electric motors, a hydraulic pump arranged to be driven by the gearbox, and a control arrangement configured to individually control the first and second electric motors to either run at a reference speed or to deliver a reference torque and during operation control at least one of the first and second electric motors to deliver a reference torque.

Technology for managing a distributed control system for a motor control switch is described. A request to implement a first controller for a motor control switch is received. An embodiment determines a plurality of function modules of the first controller. Each of the plurality of function modules includes an instance of computer logic configured to perform a corresponding function. A corresponding motor control function set (MCFS) for performing the corresponding function of each of the plurality of function modules is determined. One or more of a plurality of configurable hardware blocks are assigned to each of the plurality of function modules. An embodiment configures each of the plurality of configurable hardware blocks based on the MCFS of the function module to which the corresponding configurable hardware block is assigned. The configured plurality of configurable hardware blocks operate as a distributed system to control the motor control switch.

The application discloses a torque control method, device, equipment, storage medium and program product, and belongs to the technical field of electromechanics. The method comprises the following steps: acquiring an expected output torque, an actual output torque and a current control value of a driving device, wherein the driving device comprises a rotating motor; determining an updated current control value based on the expected output torque and the actual output torque; determining a disturbance current compensation value based on the actual output torque, the current control value and a current compensation model; adjusting the updated current control value based on the disturbance current compensation value to obtain an adjusted updated current control value; and controlling the rotating motor to operate based on the adjusted updated current control value. According to the application, the adjusted updated current control value is more accurate, the number of control adjustments is reduced, and the adjustment efficiency is improved.

An electronic circuit can be configured to receive an input comprising one or more Hall sensor readings, to determine a current motor position based on the input and a first lookup table, to determine a desired motor position based on the first lookup table for the current motor position, to determine a pulse width modulation (PWM) commutation pattern based on the desired motor position and a second lookup table, and to output the PWM commutation pattern within a predetermined time from receiving the input.

The invention discloses a multi-motor synchronous control method, a multi-motor synchronous control device, multi-motor synchronous control equipment and a computer readable storage medium, and belongs to the field of motor control. And the speed consistency can be improved by uniformly driving each motor through the target driving current determined based on the closed-loop control algorithm, so that the current actual running speed and the current target running speed of the motor set for driving the track are determined in each preset period; according to the deviation value of the actual operation speed relative to the target operation speed, target driving current is determined through a closed-loop control algorithm; finally, all motors in the motor set are driven through the target driving current, the target driving current can be periodically updated, all the motors are uniformly driven, the running speed of all the motors is made to periodically approach the target running speed, and therefore the speed consistency of all the motors can be improved, and losses of the motors and rail components are reduced.

This invention discloses an intelligent inverter control method based on an adaptive algorithm, belonging to the field of intelligent inverter control technology. It employs a five-step approach: multi-source temperature acquisition and environmental identification, machine learning-based electro-thermal coupling prediction, adaptive inverter parameter optimization, collaborative thermal management, and fault diagnosis and self-repair. Addressing the risk of high or low temperature failures in motors and inverters under extreme climates, it acquires real-time temperature distribution and load information, predicts temperature rise trends in advance, and proactively adjusts inverter current, voltage, and modulation strategies to achieve safe derating or torque compensation. Furthermore, it performs multi-sensor cross-validation and observer fusion when sensors drift or components age, maintaining stable system operation and efficient energy utilization, significantly improving driving performance and vehicle reliability in extreme environments. During this process, online model updates further refine fault diagnosis and self-repair, enhancing the durability and economy of the electric drive system.

The invention discloses an electromechanical coupling resonance suppression method, a controller and a motor drive system, which relate to the field of electromechanical control technology and solve the problem that electromechanical coupling resonance between an electrical system and a mechanical system of existing complex electromechanical equipment affects system stability and accelerates component failure. The invention comprises the following steps: constructing a dynamic model of a mechanical system; constructing a control algorithm model of a control system: determining that a mathematical model of a control object in the control system includes a mathematical model of a permanent magnet motor and a converter, and designing a motor controller according to the mathematical model; constructing an electromechanical coupling model based on the dynamic model of the mechanical system and the control algorithm model of the control system; and analyzing the resonance characteristics of an electromechanical system based on the electromechanical coupling model to determine a resonance point. The method proposed by the invention combines ADRC, has good control performance when motor parameters change, and is robust to changes in PMSM parameters.

The load control system is configured to generate, by software, a function capable of using the power conversion device for a prescribed use in a case where hardware and software-based cooperative security authentication is established by cooperation between at least the control device and the power conversion device.

The present application discloses a motor output current detection system, installation method, motor control method, and device. By providing an installation groove extending from the top of the pole to the bottom of the output pole along the axial direction on the output electrode of the motor, the Hall sensor that is pre-welded on the motor control board after being bent and set can be set in the installation groove and can slide along the installation groove to complete the installation of the motor control board. In the embodiment of the present application, the installation groove and the Hall sensor are closely matched and installed, and the output is directly sensed by the structure of the output electrode itself, and the design of the open magnetic ring is cancelled. The space occupation will not be increased due to the increase of the current detection function, which is conducive to reducing the overall volume of the control board and reducing the overall cost of the control board.

The invention relates to the technical field of motor control, in particular to a stability control method and device for a household appliance motor, and the method comprises the steps: obtaining a rotating speed data sequence, a current data sequence and a torque data sequence of each adjustment time period; according to the data characteristics of all the rotating speed attenuation positions and all the current lifting positions in the single adjustment time period and the energy characteristics of the high-frequency components in the current data sequence, obtaining a load unbalance factor in the single adjustment time period; according to the fluctuation amplitude change characteristics in the torque data sequence of the single adjustment time period and the non-linear correlation degree of the torque data and the rotating speed data in the single adjustment time period, the torque anomaly of the single adjustment time period is obtained; and obtaining an adjustment factor of a single adjustment time period, and further adjusting the proportional parameter of the next adjustment time period. According to the invention, the stability of the working process of the motor is improved by adaptively adjusting the proportional term parameters in the PID algorithm in each adjustment period.

The invention discloses a torque control method and device, equipment, a storage medium and a program product, and relates to the technical field of motor control, and the method comprises the steps: determining a target pulse width based on a preset pulse width and a preset pulse width change coefficient in response to a torque control instruction, and determining a torque amplitude value based on the target pulse width and a preset torque amplitude value; generating a target pulse modulation signal, and performing torque control based on the target pulse modulation signal; under the condition that the preset torque amplitude is the same and the target pulse width is greater than the preset pulse width, the target area of the target pulse modulation signal in the single pulse period is the same as the area of the preset pulse modulation signal in the single pulse period; if yes, reducing the corresponding torque in a period of time starting from the pulse starting moment and / or a period of time before the pulse ending moment; therefore, the shaking or NVH problem of the whole vehicle is fundamentally relieved, and the driving comfort of the vehicle is improved.

A motor position control system includes a central server including first information of a first display device and second information of a second display device, and a motor position control device which controls positions of the plurality of motors, where the motor position control device includes a controller which receives the first and the second information from the central server and store the first and the second information in a memory, controls the positions of the plurality of motors based on the first information, calculates at least one value based on the first and the second information, selects at least some motors based on the at least one value, and controls positions of the at least some motors based on the at least one value.

A nonlinear and adaptive alternating-current servo electric motor angular position control method and system includes: acquiring original electric motor rotor angular position data; inputting the original electric motor rotor angular position data into a rotor angular position control module; performing second-order time differentiation on the electric motor rotor position data, so as to generate electric motor rotor angular acceleration data; inputting the electric motor rotor angular acceleration data and an output from the rotor angular position control module into an incremental inverse dynamic control module at the same time; and inputting the output from the incremental inverse dynamic control module into an electric motor current control module.