780 results about "Control algorithm" patented technology

The application provides a hydraulic valve positioning control method and control system based on a sensorless stepping motor, and the method comprises the following steps: controlling the sensorless stepping motor to drive the spool of the hydraulic valve to move for system initialization, and determining the absolute zero point position and coordinate establishment by detecting the locked-rotor of the stepping motor; in the position control process, the total micro-step number is calculated according to the target position and the absolute zero point position, and the planned micro-step increment of each control period is determined according to the motor speed; before the execution of each control period, it is judged whether the planned micro-step increment will exceed the remaining micro-step number after execution, and the actual execution micro-step number is corrected; the cumulative execution micro-step number and the remaining micro-step number are updated according to the actual execution micro-step number, and the target position is reached until the remaining micro-step number is 0 in a certain control period. The application does not depend on any physical position sensor, realizes high-reliability locked-rotor detection and zero-point self-calibration through a magnetic field orientation control algorithm, and then realizes accurate and reliable positioning of the spool.

This invention relates to the field of semiconductor technology, and more particularly to a method, apparatus, and device for controlling the intrinsic temperature of a wafer. The method involves extracting features from amplitude ratio and phase difference spectra based on a preset time sequence to obtain a corrected growth rate; calculating the process formulation change sequence according to a preset thermal inertia equation and a preset growth kinetic relationship to obtain feedforward compensation power; calculating the corrected growth rate according to a preset multivariable PID control algorithm to obtain feedback regulation power; and solving the feedforward compensation power and feedback regulation power according to a preset decoupling formula to obtain the intrinsic temperature power control command for the wafer. By generating feedback regulation power through the multivariable PID control algorithm, and reducing temperature zone interference through decoupling, the method achieves zoned temperature control, improves the system's anti-interference capability, ensures the stability of epitaxial layer quality, enhances the consistency and reliability of MOCVD processes, and provides support for the large-scale production of semiconductor devices.

The present application relates to high-altitude hoisting technical field, disclose a kind of hoisting operation control method, comprising the following steps: one, real-time acquisition the state information of tower material to be hoisted;Two, construct multi-source data fusion model to process the state information of tower material to be hoisted, and obtain the high-precision state information estimate of tower material to be hoisted;Three, based on the layout information of pedestal, hoist arm and sling, establish fixed coordinate system;Four, based on the elastic deformation of tower material to be hoisted and crane's sling, gravity and visual feedback information, establish the differential equation of sling shape;Five, predict the state trajectory of tower material to be hoisted in future time window, design model predictive controller, with the error between tower material desired pose and actual pose minimization as goal, solve the optimal control parameter of tractor;The present application is aimed at hoisting tower material state feedback demand, installs multiple sensors in hoist arm end, hook and other positions, constructs tower material closed-loop attitude control algorithm, realizes the accurate control of tower material attitude.

The application discloses a kind of chain CAV queue shaping improvement swarm control methods based on virtual guiding berth, first, intelligent network connection unmanned vehicle team is the research object, the force between each unmanned vehicle in the system of unmanned vehicle intelligent agent is described using nonlinear function relationship;Second, the chain unmanned vehicle queue shaping improvement swarm control algorithm based on multiple virtual guiding berth is designed.The results show that the algorithm realizes the safe, rapid and stable constraint of different initial states, different number of intelligent network connection unmanned vehicles into regular chain queue (single / double chain) and drives out the intersection without signal light, and all unmanned vehicles maintain reasonable spacing, avoid collision while speed gradually reaches agreement during the completion of swarm process, realize the stable planning and tracking of intelligent network connection unmanned vehicle team, which is of great significance to improve the driving safety and road traffic efficiency of unmanned vehicle queue.

This invention relates to a laser interferometric control system and method that integrates multimodal collaborative photoelectric detection and intelligent control algorithms, belonging to the field of laser interferometry. It solves the problems of slow response speed, high complexity, and insufficient adaptability in existing laser interferometric control systems. The system includes: a light source module, whose optical path is connected to a static reference optical path module and an active adjustment optical path module. Both the static reference optical path module and the active adjustment optical path module are optically connected to an interference signal detection module, which outputs the interference contrast. A processing and decision-making module is configured to receive and fuse the interference contrast, first coordinate information, and second coordinate information, output adjustment action commands for the adjustment optical path adjustment components to adjust the interference contrast, and perform autonomous learning optimization based on the change in interference contrast before and after adjustment and the magnitude of the adjustment change, thereby achieving automatic adjustment and stable maintenance of the interference contrast. This invention is applied to laser interferometry.

The invention relates to a method for setting the parameters of a control of a pressure supply system of a hydraulic system, wherein the pressure supply system has a motor (4) and a hydraulic machine (2) that are controlled with a manipulated variable, wherein the control (160) is implemented by a control algorithm, wherein the control algorithm is parameterised by one or more parameters that can be set or changed during operation, the method comprising: carrying out (110), during operation of the hydraulic system, an estimation method based on a physical model in order to determine an estimated value for at least one characteristic variable of the hydraulic system, wherein the at least one characteristic variable includes a hydraulic capacity of the hydraulic system; determining (120) an adjustment time constant as equal to or greater than a target time constant, by means of which a maximum pressure build-up rate of the hydraulic system is achieved, which is determined by the estimated value for the hydraulic capacity and a maximum volumetric flow rate of the hydraulic machine; setting (150) at least one parameter of the one or more parameters, wherein the at least one parameter is determined (130, 140) by a respective function dependent on the setting time constant.

This invention discloses a hardware-in-the-loop (HIL) simulation platform for load cluster tracking control. The master station simulation module simulates the issuance of load cluster control aggregation commands. Any smart energy unit module decomposes the load cluster control aggregation commands according to a distributed collaborative control algorithm, achieving distributed collaboration among each smart energy unit. Any adjustable load in each adjustable load cluster decomposes the load cluster control commands according to the distributed collaborative control algorithm, achieving distributed clustering of each adjustable load in the adjustable load cluster. Each adjustable load calculates its current adjustment amount based on the received load control commands and adjusts its operating state in real time according to this adjustment amount. This invention employs a distributed collaborative control strategy on the HIL platform and uses a synchronization mechanism to ensure the continuity of the distributed strategy, improving the system's stability and robustness.

The application discloses a kind of wind light storage micro-grid optimization operation method and system.The method adopts three-layer architecture of top optimization, middle layer cooperation and bottom execution, and forms the intelligent closed loop of " prediction-planning-coordination-execution-feedback ".Top is based on LSTM prediction and traffic jam algorithm to carry out global power flow congestion identification and dredging, and utilizes improved particle swarm algorithm in feasible interval to carry out multi-objective fine optimization, and generates optimal operation instruction;Middle layer dynamically adjusts optimization target weight according to operation scene, and decomposes instruction into multiple device cooperation strategy;Bottom execution customized control algorithm, realize device accurate regulation and millisecond level fast protection.The application effectively solves the problems of micro-grid multi-objective cooperative optimization, prediction control disconnection and operation risk foresight protection, and significantly improves the voltage stability, economy, dynamic response speed and safety and reliability of system operation.

The application provides an environment-friendly low-carbon PVC material and a preparation process thereof, and relates to the technical field of polymer materials.The material comprises, in percentage by mass, 45-55% of PVC resin powder, 25-35% of environment-friendly plasticizer, 8-20% of negative-carbon calcium carbonate and 5-12% of stabilizer.The negative-carbon calcium carbonate is prepared through mineralization reaction of industrial waste residues and industrial discharged carbon dioxide.Aiming at the color fluctuation problem between batches of the recycled filler, the application provides a preparation process comprising an online dynamic color adjusting closed-loop control system, the raw materials are filed, the material color is monitored in real time online, the Delta E is calculated based on a color difference model, the PID control algorithm is used to dynamically add color masterbatches for real-time correction when the preset threshold is exceeded, and the final product is preheated and verified for color change.The application realizes the reuse of waste resources and carbon emission reduction, solves the problem of inconsistent color caused by the negative-carbon calcium carbonate, ensures the stability of the color and performance between batches of the material, and is suitable for industrial large-scale production.

This application relates to the field of vehicle technology, and particularly to a method and apparatus for lane-changing decision-making and delay compensation control based on predictive information. The method includes: acquiring traffic information collected by a roadside sensing unit; predicting traffic information of vehicles within the sensing range over a future period based on the traffic information, and constructing a multi-objective optimization problem of the target vehicle's future driving environment based on the predicted information; solving the multi-objective optimization problem to obtain the target vehicle's optimal expected speed and lane-changing decision time; and sending the expected speed and lane-changing decision time to the target vehicle. The target vehicle responds to the expected speed and lane-changing decision time using its own lane-changing safety judgment algorithm, lane-changing path planning algorithm, and delay compensation control algorithm to adjust its speed and change lanes in advance. This addresses the problems in related technologies, such as low matching degree between vehicle lane-changing planning and current traffic, and delays in the vehicle control system leading to poor control accuracy and stability, and low driving safety.

This invention discloses a digital identification system and method for high-pressure testing instrument detection data. The system includes: a data acquisition and conversion module for acquiring and converting analog signals; a data preprocessing module for noise removal and anomaly detection; a key parameter extraction module for extracting and optimizing key parameters; a data analysis and identification module for analyzing data and issuing early warnings of anomalies; a feedback adjustment module for optimizing test parameters; and a display and control module for displaying data, analysis results, and alarm prompts. This invention combines sliding window time series analysis, Kalman filtering, temporal convolutional neural networks, and adaptive fuzzy logic control algorithms to intelligently analyze and process the detection data of high-pressure testing instruments, enabling fault early warning, precise optimization, real-time adjustment, and maintenance decision-making. The system features high precision, strong real-time response capabilities, and significant optimization and maintenance effects, significantly improving data processing capabilities and decision-making efficiency during high-pressure testing.

This invention discloses an adaptive portable pipe external wall cleaning mechanism and control method suitable for different pipe diameters, belonging to the technical field of pipe maintenance equipment. The mechanism includes a grinding mechanism, a cylinder, a scanning device, and a clamping and moving mechanism. This invention employs a centralized-distributed intelligent control system and method, where the centralized unit uses a nonlinear model predictive control algorithm based on Koopman operator theory to dynamically map the system nonlinearly to a high-dimensional linear space for rolling optimization; the distributed unit is responsible for the real-time control of each actuator. This system updates model parameters through an online adaptive mechanism, significantly improving control accuracy and robustness in complex nonlinear and time-varying operating environments. This invention solves the problems of poor adaptability, low efficiency, and insufficient intelligence in traditional pipe external wall cleaning methods, achieving efficient, high-quality, and fully automated pipe external wall cleaning operations.

The application discloses a dynamic adjustment method and system suitable for balanced air supply of high and large space air conditioners, and the method comprises the following steps: obtaining an initial control strategy item from a seasonal control database based on current outdoor temperature and humidity data, current indoor temperature and humidity data and target control parameters, so as to perform automatic air supply control on the air conditioner; obtaining an adjusted control strategy item from the seasonal control database based on the outdoor temperature and humidity data, the indoor temperature and humidity data and the target control parameters after a preset time interval, so as to perform automatic air supply control on the air conditioner; obtaining an optimized control strategy item by analyzing and processing the indoor temperature and humidity data after air supply of the air conditioner, the target control parameters and all outdoor temperature and humidity data through a preset seasonal control model, performing automatic air supply control on the air conditioner by using the optimized control strategy item, and continuously optimizing the control parameters automatically by using a machine self-learning optimization control algorithm, so that the consistency of the high and large space air conditioner control is realized.

This invention discloses a multi-target adaptive jamming system based on an array antenna, belonging to the field of electromagnetic countermeasures and signal processing technology. The system includes an array antenna unit, a multi-channel RF front-end, a signal processing and control unit, and an environmental sensing unit. The array antenna scans the airspace and captures signals from multiple targets; the signal processing and control unit runs various algorithms to estimate the azimuth, frequency, and power parameters of multiple targets; an interference suppression matrix is ​​constructed based on singular value decomposition, generating multiple low-sidelobe focused jamming beams aimed at each target; the environmental sensing unit monitors background noise intensity in real time and dynamically adjusts the transmission power through a noise adaptive power control algorithm. This invention overcomes the shortcomings of traditional jamming techniques, such as energy dispersion, poor environmental adaptability, and unintelligent resource allocation. It can simultaneously achieve efficient and precise jamming of multiple targets, significantly improving the suppression ratio of a single target, and maintaining high jamming efficiency and resource utilization even in high-noise environments.

The invention relates to a method for operating a hydraulic system of a working machine with a hydraulic fluid supply system (2) and one or more hydraulic consumers (4) which are connected to the hydraulic fluid supply system via a control valve arrangement (6) in order to be supplied with hydraulic fluid. The hydraulic fluid supply system and / or the control valve arrangement are controlled by means of a control algorithm (20) based on specifications (22) for desired movements of the one or more consumers. The control algorithm is parameterized by one or more parameters which can be changed during operation of the hydraulic system, wherein several sets of parameter values ​​are given for the one or more parameters.When a toggle command (41) is received indicating that the parameter values ​​of a new set of multiple sets of parameter values, which is different from a current set, are to be used, while in the control algorithm the parameter values ​​of the current set of multiple sets of parameter values ​​are being used, a toggle operation is performed in which the parameter values ​​used in the control algorithm are changed to the parameter values ​​of the new set.

The present application relates to the technical field of real-time monitoring, in particular to a real-time monitoring system for road and bridge construction progress, which comprises a dynamic relationship analysis module, an event response decision module, a structure health monitoring module, a risk management and prediction module, a progress prediction and adjustment module, a resource optimization configuration module, an environmental impact assessment module and an overall progress management module.In the present application, the dynamic relationship in construction is captured through the graph neural network, the graph attention network and the dynamic graph convolution network, the construction model is updated in real time, the accuracy of progress analysis is ensured, the event response decision module quickly optimizes the construction plan through the predictive control algorithm, the strategy flexibility is improved, the structure health monitoring and risk management module combines the Markov decision process and the Monte Carlo tree search, effectively prevents risks and ensures safety, the progress prediction, resource optimization and environmental impact assessment module improves resource efficiency and environmental adaptability, and the application of comprehensive evaluation method realizes efficient and high-quality construction management.

This utility model discloses a photovoltaic-storage system for addressing three-phase imbalance, comprising a photovoltaic-storage unit assembly, circuit breakers QF8 and QF5, an electricity meter, circuit breaker QF1, a three-phase imbalance transformer, circuit breakers QF7 and QF6. The positive terminal of the photovoltaic-storage unit assembly is connected to the three-phase imbalance transformer via circuit breakers QF8, QF5, the electricity meter, and QF1. The negative terminal of the photovoltaic-storage unit assembly is connected to the three-phase imbalance transformer via circuit breakers QF8, QF5, the electricity meter, and QF1, forming a circuit for grid-connected charging and discharging. This invention, through the combination of the photovoltaic-storage system and a specific control algorithm, can detect the power of each phase in real time and dynamically adjust the charging and discharging state of the integrated photovoltaic-storage unit, accurately balancing the three-phase power, significantly improving power quality, and effectively solving the voltage quality reduction problem caused by three-phase imbalance in rural transformer areas.

The application discloses a multi-unmanned aerial vehicle control method based on reinforcement learning and belongs to the technical field of unmanned aerial vehicle cooperative control. The application designs a control algorithm for a double-time-scale multi-agent, effectively avoids possible calculation errors in the process of solving a matrix equation by introducing a novel coefficient matrix, and proposes a hybrid iteration algorithm which is independent of initial stable controller gain and has a fast convergence speed and is used for solving an approximate solution of a game algebraic Riccati equation based on a communication topology between agents. The application can realize consistency between unmanned aerial vehicles, enables the unmanned aerial vehicles to work cooperatively, and completes a task.

This invention relates to the field of programmable control technology, specifically to a digital twin-based intelligent multi-parameter testing method for transformers. The method establishes an electromagnetic-thermal-fluid multi-physics coupling mechanism model of the transformer. Historical test data is input into a graph neural network to extract residual weight matrices, which are then superimposed onto the mechanism model coefficients to generate a digital twin. Using real-time acquired oil temperature, winding current, and top oil pressure as initial boundary conditions, an alternating iterative algorithm combining a long short-term memory network and a temporal finite difference method is employed to solve the multi-parameter evolution trajectory. The extreme points and inflection points of the evolution trajectory are extracted, and the amplitude, frequency, and application timing sequence of the excitation signal for the next test cycle are calculated using a model predictive control algorithm. This sequence is then sent to the power amplifier and signal generator for closed-loop adaptive adjustment of the test excitation. This invention eliminates the mismatch between the test excitation and the dynamic physical conditions of the equipment, reducing the cumulative deviation of multi-parameter synchronous test results from the actual physical state.

The application provides a device control method, device and medium. The method first acquires actual state data of a target device running according to a preset task target and simulation state data of a digital twin model of the target device running according to the preset task target, determines the difference between the target device and the digital twin model based on the actual state data and the simulation state data, adjusts the digital twin model based on the difference, simulates and predicts each control algorithm based on the preset task target and the adjusted digital twin model to obtain a simulation and prediction control result of each control algorithm, determines a target control algorithm from the multiple control algorithms according to the simulation and prediction control results, and controls the target device according to the target control algorithm to update the actual state data. The embodiments of the application can improve the device control precision.

This invention discloses a brushless motor current sampling method, specifically relating to the field of brushless motor technology. The method involves acquiring a current waveform sequence under no-load driving conditions and extracting small current feature segments before and after commutation. Based on each feature segment, a feature vector is constructed, including the pulse width modulation duty cycle change, driving phase voltage fluctuation, and relative phase. The feature vector is input into a small current error prediction model trained with historical errors, outputting a missampling offset. A linear interpolation method is used to superimpose the offset onto the original current sequence, generating a corrected current waveform. The corrected waveform is then input to a controller, where closed-loop regulation is achieved through a vector control algorithm. This invention effectively reduces the accumulation of sampling errors in the commutation section, improves current control accuracy, and is suitable for motor drive scenarios requiring high frequency and high dynamic response.

The application provides a mechanical arm grabbing method and system based on cooperation of large field of view and local vision, and relates to the technical field of robot vision recognition. The system comprises an unmanned ship platform, a five-degree-of-freedom mechanical arm, a large field of view vision module, a local vision module and a control module; the large field of view vision module realizes wide range search and tracking of a sea surface target through a spherical camera at a base, guides the unmanned ship to approach, and the local vision module acquires target depth and three-dimensional coordinates by means of binocular stereo cameras of end grippers. The application combines YOLOv8 detection, Kalman filtering and forward dynamics compliant control algorithm, realizes cooperation of the large field of view and the local vision, completes the whole process automation from long-distance target discovery to close-range accurate grabbing, and effectively improves the recognition accuracy and grabbing success rate of the mechanical arm on the sea surface environment on targets such as floating balls and ropes.

The application discloses a remote unmanned plane control method and device, and the method comprises the following steps: acquiring a local control scene, wherein the local control scene is obtained by processing three-dimensional environment information of the remote unmanned plane by using a compressed sensing control paradigm; acquiring an electroencephalogram signal generated when an operator watches the local control scene; analyzing the electroencephalogram signal by using an intention analysis algorithm to obtain original control instructions of the remote unmanned plane by the operator; optimizing the environment information and the original control instructions by using an environment self-adaptive control algorithm to obtain continuous unmanned plane control instructions; and sending the continuous unmanned plane control instructions to the remote unmanned plane and controlling the remote unmanned plane based on a kinematics model. The application effectively solves the problem of poor space perception and control ability of the existing brain-controlled remote unmanned plane technology.

The application discloses a kind of bionic mechanical arm control method and system.The bionic mechanical arm control method includes the following steps: bionic space construction;Grabbing precision training;Real object grabbing training;PID control model acquisition;Bionic test.The application is applied to the automatic control system constructed by PID control algorithm, then based on the training data of the grabbing precision of bionic mechanical arm obtained by PID control algorithm training, further obtains bionic control parameter and judges, then the corresponding data reaching reference bionic control parameter is input to sensor and carries out real object grabbing training, and the grabbing precision obtained is judged, and the running track of bionic mechanical arm is controlled by acquiring PID control model, finally, real object grabbing test is carried out and the corresponding grabbing precision error is judged, the effect of improving the efficiency of bionic mechanical arm moving track planning is achieved, and the problem of low efficiency of bionic mechanical arm moving track planning in the prior art is solved.

The application discloses a kind of liquid cooling system dynamic control method and system based on thermal resistance flow resistance analysis, and it is related to liquid cooling thermal management field.The method comprises the following steps: S1: collecting multi-source heterogeneous sensor array parameters, calculating dynamic confidence weight, and generating system state observation value;S2: based on system state observation value, establish thermal resistance flow resistance coupling model framework and online identification and real-time update pre-built key parameters, obtain updated thermal resistance flow resistance coupling model;S3: by the model predictive control parameter of preestablished, construct multi-objective optimization problem, based on thermal resistance flow resistance coupling model, solve current optimal control instruction using predictive control algorithm;S4: collect historical operation data to establish long-term evolution mode factor, optimize model predictive control parameter to generate correction execution parameter.Through real-time optimization thermal resistance flow resistance model and control strategy, combined with deep learning realizes intelligent pre-adjustment, significantly improves the heat dissipation efficiency of liquid cooling system, reduces energy consumption and ensures stable operation.

This invention discloses a method and terminal for intelligent coordinated control of traffic lights. The method includes: upon receiving a request for passage from an intelligent driving vehicle at a target intersection, real-time collection of pedestrian passage requests, traffic conditions in conflicting directions, and traffic conditions in the target direction; determining whether to trigger a constant green strategy based on the traffic conditions in the conflicting directions and the pedestrian passage request; if not triggered, calculating the next cycle phase sequence and duration scheme of the traffic lights using a signal control algorithm based on the intelligent vehicle passage request, the traffic conditions in the target direction, the traffic conditions in conflicting directions, the pedestrian passage request, and the default timing strategy; adjusting the traffic lights at the target intersection according to the next cycle phase sequence and duration scheme; and adjusting the traffic lights at the next preset intersection according to the driving route in the intelligent vehicle passage request and the traffic lights at the target intersection to form a green wave segment. This method can effectively improve intersection traffic efficiency and meet the passage needs of intelligent driving vehicles.