435 results about "Adaptive control algorithm" patented technology

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention relates to the technical field of intelligent agriculture, and discloses a visual intelligent agricultural planting control system. The system comprises an environment data acquisition module used for acquiring multi-source environment sensing data; the growth feature modeling module is used for extracting crop growth state features through a morphological analysis algorithm; the environment regulation and control decision module is used for generating an environment regulation and control instruction set by utilizing a dynamic threshold matching algorithm; the visual interaction module is used for generating a three-dimensional farmland live-action simulated diagram through multi-dimensional data fusion processing; the strategy execution module is used for driving agricultural facilities to execute actions by adopting a self-adaptive control algorithm; an abnormity early warning module is further arranged, and abnormity early warning signals are generated through correlation analysis. The system realizes comprehensive acquisition and analysis of agricultural planting environment data, precise environment regulation and control, visual and visual display and abnormal early warning, effectively improves the intelligent and precise level of agricultural planting, improves the crop yield and quality, and assists the development of intelligent agriculture.

The invention relates to the technical field of spraying process control, and discloses a spraying process self-adaptive adjustment method based on temperature measurement. The method comprises the steps that target coating parameters and base material physical property data are obtained, and initial spraying control parameters are generated through a first intelligent calculation model in combination with historical process data and real-time environment monitoring data; when spraying is executed, heat distribution data of a spraying area are collected in real time through a temperature sensing device, and according to the difference between the heat distribution data and a preset target temperature interval, technological parameters are dynamically corrected through a first self-adaptive regulation and control algorithm; meanwhile, a machine vision system is used for capturing actual coating morphological characteristics, and after the actual coating morphological characteristics are compared with target parameters, excitation parameters are adjusted in a partitioned mode through a second self-adaptive regulation and control algorithm so as to optimize uniformity; and collecting whole-process data, comprehensively evaluating the whole-process data through the second intelligent calculation model to generate a process optimization instruction, and updating the first intelligent calculation model and the first self-adaptive regulation and control algorithm parameters according to the process optimization instruction.

The invention relates to the technical field of intelligent equipment monitoring, and discloses a sandstone separator monitoring control method and system based on multi-source data processing. According to the method, equipment operation parameters, real-time image data and vibration sensing data of the sandstone separator are collected in real time, data preprocessing and feature extraction are carried out, a monitoring model based on a multi-modal deep learning framework is constructed, and accurate joint characterization of the equipment state is achieved. The system can dynamically generate a control instruction according to a monitoring result, adjust equipment operation parameters in time, and avoid production interruption and quality problems. Besides, the multi-modal data fusion technology and the self-adaptive PID control algorithm are adopted, so that the operation efficiency and stability of the sandstone separator are further improved, and powerful technical support is provided for the field of intelligent manufacturing.

The invention discloses an automatic parameter adjustment test method for an electromagnetic angular displacement sensor, and the method comprises the steps: injecting a standard angle signal into a to-be-tested sensor, and collecting an output response signal; calculating a deviation value of the sensor parameter based on the output response signal and the standard angle signal, and judging whether the deviation value exceeds a preset threshold value or not; if yes, based on the deviation value, a control instruction used for compensation is generated through a self-adaptive control algorithm, and the mechanical zero position of the to-be-detected sensor or the relative position of an internal electromechanical conversion element is adjusted; based on the adjusted output response signal, the above steps are repeatedly executed, and sensor parameters are iteratively updated; and when the deviation value is lower than a preset threshold value, judging that the output precision meets a preset requirement, storing the current parameter, and completing the parameter adjustment process of the to-be-detected sensor. By constructing a closed-loop control flow of'detection-analysis-adjustment-verification 'and an adaptive control algorithm, full-automatic, high-precision and high-efficiency calibration of the zero position, sensitivity and excitation signal parameters of the electromagnetic angular displacement sensor is realized.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention belongs to the technical field of aircraft control, relates to an aircraft gravity center real-time balancing method and system based on dynamic parameter fusion, and aims to solve the problems of insufficient real-time performance, accuracy and self-adaptability of a gravity center balancing method under complex flight conditions in the prior art. The method comprises the steps that real-time parameters are obtained, and the real-time gravity center position is calculated; if the real-time gravity center position is not within the preset range, calculating a first inertia moment and a first additional moment caused by fuel density change and a second inertia moment and a second additional moment caused by aeroelastic deformation; performing inertia moment correction based on the first inertia moment and the second inertia moment; additional moment correction is carried out based on the first additional moment, the second additional moment and the real-time pitching inertia moment; when the real-time gravity center position is within the preset range, gravity center balancing is completed. The fuel oil density and aeroelastic parameters are estimated in real time through a self-adaptive control algorithm, rudder deflection and fuel oil distribution are cooperatively adjusted, and the problem of center-of-gravity shift under complex disturbance can be effectively solved.

The invention relates to the field of industrial automation, and particularly provides a semiconductor photoetching system based on an intelligent industrial robot. And a multi-sensor fusion and real-time compensation algorithm is adopted, so that photoetching positioning errors are accurately sensed and corrected, the positioning precision is greatly improved, and the stable performance of the chip is ensured. According to a multi-physical field monitoring and self-adaptive control algorithm, process parameters are dynamically regulated and controlled according to a photoetching environment and a photoresist state, pattern transfer is optimized, pattern defects are avoided, and photoetching pattern quality is improved. According to the multi-modal data fusion and deep learning algorithm, photoresist coating parameters are automatically adjusted according to the surface characteristics of the wafer, uniform coating is realized, and the stability of the photoetching process is enhanced. The reinforcement learning algorithm assists the multiple robots in efficient collaboration, optimizes task allocation, improves the overall photoetching efficiency and the resource utilization rate, and promotes the progress of the semiconductor photoetching technology in all directions.

The invention relates to the technical field of photovoltaic cleaning control, and discloses a spraying cleaning control method of a photovoltaic cleaning unmanned aerial vehicle. The method comprises the following steps: receiving photovoltaic panel surface state data and environmental parameters, comparing dirt grade standards to obtain dirt distribution characteristics and stubborn stain positions, and determining initial spraying parameters through a self-adaptive control algorithm in combination with the environmental parameters; after the spraying executing mechanism is driven to conduct preliminary cleaning, an image collecting device is used for obtaining surface image data, and a deviation value is calculated by comparing a cleanliness threshold value; and in combination with flight attitude data of the unmanned aerial vehicle, a fuzzy decision system is adopted to dynamically adjust initial spraying parameters to generate a real-time control instruction, an execution mechanism is controlled to perform fine cleaning until surface image data accords with a cleanliness threshold, and a final scheme is generated. According to the method, through real-time feedback and dynamic adjustment, spraying parameters are made to adapt to different dirt states and environment conditions, targeted cleaning is achieved, and the intelligence and accuracy of photovoltaic panel cleaning are improved.

The invention relates to the field of navigation signal processing, and discloses a passenger car battery temperature control intelligent regulation and control method and system based on multi-source sensor data, environment data and battery operation state data are acquired by constructing a multi-source sensing array, and fusion processing and feature extraction are performed on the acquired data by using an edge calculation method; analyzing and predicting the charging and discharging behaviors of the battery based on the extracted characteristic parameters, judging the charging and discharging states of the battery, dynamically optimizing the switching threshold value of the refrigerating or heating mode of the battery, switching the mode according to the optimized switching threshold value, and performing matching analysis on the photovoltaic power generation energy and the temperature control energy consumption, so as to determine the temperature control energy consumption of the battery. According to a matching analysis result, an exclusive temperature control matrix is generated through a geographic position and meteorological data, and time-sharing gradient control is performed by using an adaptive PID control algorithm in combination with instantaneous wave cloud characteristics of photovoltaic energy storage, so that customized temperature control strategies can be provided for different environmental conditions, temperature fluctuation is avoided, and the temperature control stability is improved.

The invention discloses a method and equipment for constant temperature control of a printer nozzle and a storage medium, and relates to the technical field of printer nozzle temperature control, temperature data is collected in real time through a distributed temperature sensor array, and PID parameters are adjusted by combining environment temperature and humidity and ink flow and utilizing a dynamic self-adaptive control algorithm model, so that the temperature of the printer nozzle is controlled. The method comprises feedforward control, partition gradient heating, a pulse type low-power-consumption heating mode and an abnormal temperature processing mechanism, precise control over the temperature of the spray head is achieved, the electronic equipment comprises a multi-sensor fusion module, a data processing module, a power driving module and a state monitoring module, efficient execution of a control strategy is ensured, and the control efficiency is improved. According to the method and the equipment, the control strategy is continuously optimized through the self-adaptive learning function, and the printing quality and the equipment stability are improved.

The invention provides a concrete spraying rebound rate optimization decision-making method and a concrete spraying rebound rate optimization decision-making system. The method comprises the following steps: firstly, collecting and extracting time-frequency characteristics in a jet impact acoustic emission signal, collecting laser Doppler array measurement data in a concrete jet process to obtain concrete flow velocity distribution, then carrying out multi-source fusion processing on the time-frequency characteristics and the concrete flow velocity distribution, and extracting principal component characteristic vectors associated with springback; the method comprises the following steps: firstly, constructing a main component feature vector, then constructing a space velocity field model to obtain an identification result representing a concrete rebound state, then carrying out clustering analysis on the main component feature vector and the identification result, identifying a high rebound mode category and a low rebound mode category to generate a risk partition map, and finally, dynamically generating a jet parameter adjustment instruction by applying a self-adaptive control algorithm. Outputting a concrete rebound rate optimization decision scheme; according to the technical scheme provided by the invention, the problem of feature drift caused by space-time asynchronization of physical field data is solved, and the overall rebound rate prediction precision is greatly improved.

The invention belongs to the technical field of drilling, and provides an active vibration reduction method and system based on self-adaptive adjustment of a drill rod load in order to effectively reduce vibration of a drill rod. A real-time feeding oil pressure signal is converted into real-time feeding thrust, and a real-time rotating oil pressure signal is converted into real-time rotating torque; filtering the original vibration signal by adopting a singular value subspace decomposition method to obtain a real vibration signal; according to the preset initial value of the feeding thrust and the preset initial value of the rotating torque, a neural network self-adaptive PID control algorithm is used for conducting self-adaptive comparison calculation, the vibration adjustment amount is obtained, and active vibration reduction is conducted according to the vibration adjustment amount; the system comprises six closed-loop control modules including a preset parameter input module, a signal acquisition module, a signal filtering module, a control algorithm module, a control output module and an execution module, accurate vibration reduction control is achieved, the bounce phenomenon of a drill bit is eliminated, the deviation error of a drill hole is reduced, meanwhile, stick-slip vibration is restrained, and the clamping stagnation failure rate of a drill rod is greatly reduced.

The invention belongs to the technical field of mobile mechanical arm grabbing, and provides a mobile mechanical arm grabbing control method and system based on machine vision, and the method comprises the steps: collecting an image in the moving process of a mobile mechanical arm according to a preset grabbing path, recognizing a dynamic obstacle in the image through an RT-DETR algorithm, and generating a prediction track of the dynamic obstacle, collision detection is conducted on a preset grabbing path of the movable mechanical arm, an obstacle avoidance track switching instruction is generated by combining the current time point of the mechanical arm with the predicted collision time point of the dynamic obstacle, trajectory tracking is conducted on the obstacle avoidance track of the mechanical arm, a model reference self-adaptive control algorithm is adopted, a reference model is established, and obstacle avoidance is achieved. According to the method, mechanical arm vibration caused by obstacle avoidance track switching is effectively restrained, interference of vibration on grabbing stability is reduced, and the operation precision and reliability of the mechanical arm in a dynamic obstacle avoidance scene are remarkably improved.

The embodiment of the invention provides an oxidation kettle temperature self-adaptive control system and a control method. The control system comprises a temperature detection module; the self-adaptive control algorithm module is used for dynamically adjusting the output of the heating or cooling device through data provided by the temperature detection module based on a self-adaptive control algorithm of a snow ablation algorithm; the feedback control module compares the output of the temperature detection module with a set temperature, the feedback control module adjusts control parameters and ensures that the temperature reaches a set value, the feedback control module adopts a double-closed-loop PID control system, and the double-closed-loop control structure comprises a self-adaptive PID controller of an outer loop and a rapid PID controller of an inner loop which are combined; and the man-machine interaction module allows an operator to set a target temperature and a control parameter, and displays a temperature change curve in real time. Through dynamic adjustment of PID controller parameters combined with real-time monitoring, intelligent analysis and an intelligent optimization algorithm and design of a double-closed-loop feedback system, quick response and stable control of temperature control are realized.

The invention discloses a high and low temperature environment test box thermal load verification method and system, and relates to the technical field of environment test, and the method comprises the steps: initializing a target temperature range, a cooling rate, a heating rate, a preset temperature fluctuation range and a thermal load demand, and carrying out the preliminary setting of a thermal load; adjusting a liquid nitrogen adjusting unit in real time by adopting a dynamic optimization algorithm, and adjusting internal thermal load parameters of the test box under the condition of no external thermal load to obtain reference data; based on a self-adaptive control algorithm, dynamically adjusting the matching relationship between the liquid nitrogen release rate and the internal thermal load of the test box, and verifying the operation state of the test box under different thermal load conditions; a self-adaptive thermal load adjusting algorithm is adopted, the relation between the liquid nitrogen release rate and thermal load change is adjusted in real time, and thermal load verification of the test box is carried out according to the states under different load conditions. According to the invention, through the dynamic heat exchange coefficient model and the high-order injection optimization algorithm, the thermal load verification precision of the high and low temperature environment test box is improved.

The invention provides a tunnel support self-adaptive adjusting method and system based on rock mass-support feedback, and relates to the technical field of tunnel engineering support. The method comprises the following steps: acquiring surrounding rock strain data and stress data through a sensor network arranged in tunnel surrounding rock, and generating original monitoring data; calculating the surrounding rock strain energy density by using a data processing unit; matching the strain energy density with a preset energy stage feature through an adaptive control algorithm to generate an energy stage matching result; determining an energy state stage based on a stage confirmation module and generating an energy stage identifier; a supporting mode switching instruction is generated through a supporting mode mapping querier; and finally, the supporting control unit adjusts the working parameters of the supporting structure, real-time monitoring of the energy state of the surrounding rock and dynamic matching of the supporting parameters are achieved, the adaptability and safety of tunnel supporting are effectively improved, and the hysteresis of manual intervention is reduced.

The invention discloses an efficient charging and discharging control method and system for a capacitive energy storage welding machine, and the method comprises the steps: the capacitive energy storage welding machine monitors the voltage, current and temperature of a capacitor in real time through a battery management system, and determines the system state and the environment temperature; in the direct current discharging mode, the system dynamically adjusts the charging and discharging rate through an inverter and a self-adaptive control algorithm, and voltage output is smoothed; in the indirect discharge mode, a welding transformer is adopted to reduce the voltage and accurately control the frequency and time of the inverter; the system automatically adjusts charging and discharging current and discharging time according to the thickness, speed and temperature of a welding material, and intelligently switches modes; a multi-safety protection mechanism ensures the safety of the welding process, and a power supply is automatically adjusted or cut off when abnormity occurs; according to the efficient charging and discharging control method and system for the capacitive energy storage welding machine, the safety of the welding-free transformer capacitive energy storage welding machine is improved, the phenomena of huge sound and metal splashing are reduced, meanwhile, the energy utilization efficiency is improved, the welding quality is improved, and the production efficiency is enhanced.

The invention relates to the technical field of farmland fertilization optimization, and discloses a farmland fertilizer application amount analysis and diagnosis method based on machine learning. The method comprises the following steps: firstly, acquiring near infrared spectrum data and surface microscopic image data of farmland soil, and generating a soil comprehensive risk area; analyzing the change gradient of the migration rate of the nitrogen element in the region to evaluate the offset state, and analyzing the interaction strength of the phosphorus element and the potassium element to evaluate the asymmetric coupling state of the phosphorus element and the potassium element; calculating the farmland fertilizer demand degree according to the evaluation result; analyzing the matching deviation between the operation behavior of the peasant household and the fertilizer demand degree in combination with historical fertilization operation records, and generating an operation deviation value; a fertilization optimization strategy is generated in combination with the fertilizer demand degree and the operation deviation value, and execution parameters of the fertilizer applicator are dynamically adjusted through a self-adaptive control algorithm. According to the method, the multi-dimensional information of the soil can be synthesized, the fertilizer demand condition is accurately evaluated, the fertilization strategy is optimized, and the fertilization operation is dynamically adapted.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.

The invention relates to the technical field of power system control, in particular to a direct-current capacitor self-synchronizing grid-connected control strategy method, system, equipment and medium, a direct-current capacitor energy dynamic model is established based on an actual energy balance relation, a direct-current voltage signal is acquired in real time, and a synchronous compensation signal is calculated based on the acquired direct-current voltage signal; performing phase compensation and gain adjustment on the synchronous compensation signal through a correction link based on the calculated synchronous compensation signal, adjusting the internal frequency of the inverter based on the synchronous compensation signal after phase compensation and gain adjustment, controlling the DC voltage through the internal frequency of the inverter, and establishing a self-synchronization control model; and based on the self-synchronization control model, establishing an adaptive control algorithm, generating a damping coefficient and a virtual inertia coefficient through the adaptive control algorithm, and dynamically adjusting control parameters according to the damping coefficient and the virtual inertia coefficient.

Development of technologies for effectively suppressing edge localized modes (ELM), maintaining core plasma stability, controlling plasma rotation, adjusting edge transport barriers, achieving smooth transitions between different plasma regimes, minimizing error magnetic fields, sustaining plasma for long periods, stabilizing fusion power, controlling plasma impurities, and optimizing plasma-wall interactions simultaneously in tokamak fusion reactors. [Solution] We provide an adaptive edge-localized 3D magnetic field control system that organically links a real-time plasma state monitoring system, a 3D magnetic field response prediction model, a flexible 3D coil system, an adaptive control algorithm, a plasma regime transition management system, an integrated control interface, a high-precision magnetic diagnostic system, a fusion power control system, a plasma impurity control system, and a plasma-wall interaction optimization system.

The invention relates to the technical field of general control or regulation systems, in particular to a sushi type S-shaped layered horizontal self-driven transmission mushroom and vegetable shelter planting system. The system comprises a data acquisition module, a path planning and scheduling module, a motor driving control module, an environment regulation and control module and a central controller. The system collects environment parameters, carrier states and crop growth information in real time through a sensor network, and constructs system state vectors. The central controller combines a heuristic path planning algorithm, an adaptive PID control algorithm, a model prediction control algorithm and a DQN optimization strategy based on deep reinforcement learning to realize carrier path optimization, motor driving precision control, cabin environment prediction regulation and control and system closed loop feedback optimization. The system has the advantages of flexible orbit scheduling, efficient operation response, fine environment control, high intelligent level and the like, and can realize subarea fine management and continuous optimization evolution of the system in a multi-category crop co-cabin planting scene.

The invention relates to the technical field of pH value adjustment, and discloses a pH value adjustment method and system, a storage medium and equipment. The method comprises the following steps: denoising time sequence data of a pH sensor to obtain a smooth pH value sequence, and calculating a first-order difference of the smooth pH value sequence to obtain a pH value rate change sequence and rate characteristics; when any value in the sequence exceeds a preset threshold value, extracting feature distribution data, and inputting the feature distribution data and real-time state data of the solution into a solution state evaluation model to obtain an estimated value of the buffer capacity and reaction rate change of the solution; based on the estimated value, the pH value rate change sequence and the rate characteristics, calculating a preliminary control sequence of the acid-base addition amount, adopting a self-adaptive PID control algorithm to adjust parameters, generating an optimal control sequence, controlling the acid-base addition equipment to adjust the addition amount, obtaining new pH value data, and performing fast Fourier transform to obtain a stable pH value sequence. The stability of pH value control of the solution is improved.

The invention relates to the technical field of transmission shaft machining, and discloses an intelligent clamping method and system for universal joint transmission shaft machining. The method comprises the steps that the geometric dimension, the form and location tolerance number, vibration signal data, unbalance data and pressure distribution data of a transmission shaft are obtained; performing precision correction based on preset machining precision to obtain transmission shaft positioning parameters; adjusting parameters in real time through an adaptive control algorithm to obtain real-time positioning parameters; performing feature extraction to obtain vibration spectrum features; outputting a vector diagram to obtain an unbalanced vector diagram; performing Kalman filtering processing to obtain filtering parameters; calculating by adopting a fuzzy control algorithm to obtain a vibration compensation amount and an unbalance correction amount; performing data processing to obtain a corrected pressure matrix; inputting into a pre-trained neural network model to obtain a predicted risk level; and risk prediction and judgment are carried out, clamping strategy adjustment is carried out, a corrected clamping strategy is obtained, machining of the transmission shaft is carried out, and the machining accuracy is improved.

The invention discloses a self-adaptive leveling and anti-disturbance vehicle-mounted unmanned aerial vehicle lifting platform and method, and relates to the technical field of automation control technologies, and the vehicle-mounted unmanned aerial vehicle lifting platform comprises a lifting mechanism assembled on a vehicle; the leveling mechanism is assembled at the lifting top of the lifting mechanism, and an unmanned aerial vehicle nest is assembled at the top of the leveling mechanism; the attitude sensing unit is used for measuring attitude data of the unmanned aerial vehicle nest in real time; the disturbance sensing unit is used for detecting external disturbance data of the unmanned aerial vehicle nest in real time; and the control unit is electrically connected with the lifting mechanism, the leveling mechanism, the attitude sensing unit and the disturbance sensing unit respectively. According to the invention, a high-frequency-response attitude sensing and multi-degree-of-freedom rapid leveling mechanism and a closed-loop control system of an advanced adaptive control algorithm are integrated. The system can monitor the posture of the platform in real time and quickly drive the leveling mechanism to compensate, so that the leveling mechanism always keeps a high-precision horizontal state under the condition that a vehicle inclines in a large range and is dynamically disturbed.

The invention discloses a long-arm-span heavy-load robot and a force-position hybrid control and high-precision dynamic compensation method, and belongs to the technical field of forging robots, the long-arm-span heavy-load robot comprises a 4R type series main chain and five parallel closed-loop structures, and an end effector, a static platform and a connecting rod part can achieve the seven-axis movement capacity under motor and hydraulic drive. According to the force-position hybrid control and high-precision dynamic compensation method, the position and force feedback signals of the robot are collected in real time, and the self-adaptive control algorithm is combined, so that high-precision forging control is achieved; and meanwhile, the deformation of the connecting rod is calculated by obtaining the set coordinate value of the end effector and the stress condition of the connecting rod, then the compensation deformation of the driving oil cylinder is controlled, a closed-loop feedback mechanism is utilized, mechanical deformation online compensation and multi-connecting-rod deformation compensation are achieved, and high-precision control over the robot is achieved. The device has the advantages of being large in load, high in rigidity, long in arm span and high in precision, and medium and large forgings in a forging workshop can be rapidly and accurately transferred.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

The invention discloses a carbon dioxide pneumoperitoneum instrument based on a self-adaptive PID control algorithm, and relates to the technical field of medical instruments, the carbon dioxide pneumoperitoneum instrument comprises a pneumoperitoneum instrument body, a control panel is arranged on the front side of the pneumoperitoneum instrument body, and a display area is arranged on the surface of the control panel; the display area comprises a preset pressure value display screen, a real-time pressure value display screen, a preset maximum flow value display screen and a carbon dioxide usage amount display screen, and the surface, located on one side of the display area, of the control panel is provided with a start-stop key and an air source pressure state indicator lamp. A power switch button and an air outlet are arranged on the surface, located on the other side of the display area, of the control panel, and a preset pressure adjusting button and a start-stop button are arranged on the surface, located below the display area, of the control panel. By means of the electronic proportioning valve and real-time pressure feedback, it is ensured that the gas injection flow is accurate and controllable, excessive or insufficient gas injection is avoided, and the operation effect is improved.

The invention provides an intelligent cabinet heat dissipation control method and system, and belongs to the technical field of cabinet heat dissipation control. According to the method, data items all have acquisition time and cabinet area identifiers; the method comprises the following steps: dividing a cabinet hot area by using fuzzy clustering based on a historical sample, dynamically modeling by combining an adaptive PID control algorithm, optimizing control parameters through a particle swarm optimization algorithm, adjusting a hot area control model weight coefficient according to real-time feedback, generating a hot area-equipment association map, and constructing an adaptive heat dissipation control network; predicting a temperature trend through a decision tree model by using a correlation map and real-time operation characteristics generated by the network, and generating a fan rotating speed adjusting strategy, an air conditioner power distribution scheme and a multi-stage heat dissipation control strategy according to the temperature trend; inputting a heat dissipation control strategy and an execution result as feedback into the self-adaptive heat dissipation control network, and dynamically optimizing a correlation map and control parameter modeling; and multi-cycle feedback closed-loop optimization control is realized through multi-time feedback and parameter adjustment.