335 results about "Adaptive control algorithm" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a biomass boiler fuel adaptability enhanced feeding system and a control method. The biomass boiler fuel adaptability enhanced feeding system comprises a feeding device, a fuel pretreatment module, a multi-parameter detection module, a control unit and an execution mechanism. The feeding device comprises a screw feeder and a vibrating feeder which are arranged in parallel, outlets of the screw feeder and the vibrating feeder are converged into the same blanking channel, a wear-resistant ceramic lining plate is laid on the inner wall of the channel, and a pneumatic arch breaker is mounted at the bottom of the channel. Through a multi-stage pretreatment and intelligent feedback control cooperation mechanism, the adaptability of the complex biomass fuel is remarkably improved. The two-stage pretreatment module effectively breaks caking, screens out overrun particles and adjusts the water content, and the blocking risk is eliminated from the source; multi-parameter real-time detection is combined with a self-adaptive control algorithm, the feeding rate and the air-coal ratio are dynamically optimized, the combustion stability is ensured, and the heat efficiency is remarkably improved; due to the abnormal working condition self-diagnosis function, the manual intervention frequency is reduced, and multi-fuel combustion of multiple components in any proportion is supported.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention discloses an intelligent heat exchange system based on the Internet of Things and a self-adaptive energy-saving control method thereof. The intelligent heat exchange system is characterized by comprising an Internet of Things sensing layer, an edge computing layer, a cloud collaboration layer and a visual monitoring platform, and a self-adaptive control algorithm module carried by a main control unit fuses model predictive control (MPC) and reinforcement learning (RL); the MPC is configured to dynamically adjust the pump speed and the valve opening degree in a rolling optimization mode; and the RL is configured to dynamically adjust the weight coefficient of the MPC according to the real-time working condition of the system so as to realize priority optimization of energy efficiency or stability under different load working conditions. The energy consumption is greatly reduced, the heat efficiency is improved, the energy waste is reduced, the dynamic response speed is high, the anti-interference capability is high, the control precision is high, the output stability is good, passive maintenance is changed into predictive maintenance, the maintenance cost and manpower dependence are reduced, and the technical compatibility and generalizability are high.

The invention discloses a numerical control machine tool cutter regulation and control method and system based on cutting state diagnosis. The method comprises the following steps that S1, cutting state original data in the machining process of a numerical control machine tool are obtained and preprocessed; s2, inputting the preprocessed cutting state data into the trained cutting state diagnosis model, and outputting the current wear grade of the tool and a machining risk assessment result; and S3, based on the current wear grade of the cutter, the processing risk assessment result and a preset cutter life optimization target, the invention relates to the technical field of numerical control machine tool cutter regulation and control. According to the numerical control machine tool cutter regulation and control method and system based on cutting state diagnosis, a self-adaptive control algorithm is adopted, two optimization targets of cutting efficiency and cutter service life prolonging are combined, it is ensured that under different abrasion states, the optimized machining parameters can maximize the machining efficiency and the cutter service life, and the service life of the cutter is prolonged. The self-adaptive adjustment mode can be continuously optimized according to the actual machining state, and the machining precision and the production efficiency are improved.

The invention relates to the field of oil field heating furnace control, and discloses an oil field heating furnace intelligent digital implementation method and control method.The control method comprises the following steps that S1, a temperature sensor is used for monitoring the temperature of an oil field heating furnace in real time; s2, calculating a temperature error between the upper limit and the lower limit of the real-time temperature and the set temperature according to the monitoring data of the temperature sensor; s3, a self-adaptive PID control algorithm is applied, and heating power is calculated based on the temperature error; and S4, dynamically adjusting the heating power to control the temperature of the oil field heating furnace. The precision and stability of temperature control can be improved, the oil field heating furnace can control the target temperature more accurately, and the production efficiency is improved. Meanwhile, the self-adaptive PID control algorithm can automatically adjust parameters according to actual working conditions and environment changes, the adaptability and robustness of the system are improved, and the requirements for manual intervention and adjustment are reduced. In addition, the stable and efficient heating process can be achieved by dynamically adjusting the heating power, and energy and cost are saved.

The invention relates to the technical field of aerostat control, and discloses an aerostat control method and system, and the method comprises the steps: S1, collecting the internal state data, external environment data and environment parameters of an aerostat through a multi-source sensor; s2, processing the data acquired in the S1, and constructing a state and disturbance observer to obtain an aerostat state estimation value and an environmental wind disturbance estimation value; s3, based on the state estimation value and the wind disturbance estimation value of the aerostat, a self-adaptive control algorithm is adopted, a control instruction used for controlling the height and the horizontal position of the aerostat is generated, and the control instruction comprises a feedforward component used for actively compensating wind disturbance; and S4, distributing the control instruction to a channel power system of the aerostat and executing the control instruction. An integrated control architecture with state estimation, disturbance observation and adaptive control capabilities is constructed, three technical bottlenecks of weak control robustness, poor anti-interference capability and low intelligent level of a traditional aerostat in outdoor application are effectively solved, and the positioning precision and stability are improved.

The invention discloses a cable extrusion temperature control optimization method based on an adaptive control algorithm, and the method comprises the following steps: S1, collecting and preprocessing multi-source process data, and generating a multivariable temperature control input data set; s2, configuring a self-tuning PID (Proportion Integration Differentiation) controller, and setting an initial PID parameter; s3, performing coevolution optimization on PID parameters of each heating section to obtain an optimal multi-section PID parameter combination; s4, distributing an optimal PID parameter, and adaptively adjusting the parameter in combination with the input data set; s5, implementing closed-loop temperature regulation, monitoring deviation, coordinating section management, and collecting temperature control, energy consumption and process data; and S6, periodically feeding back temperature control and energy consumption data, optimizing parameters, dynamically adjusting the parameters, and resetting the parameters when the parameters are abnormal. According to the invention, intelligent self-adaptive optimization control of multi-section temperature in the cable extrusion process is realized, and the temperature control precision, the energy efficiency and the product quality stability are remarkably improved.

The embodiment of the invention provides a flywheel energy storage system and an electrical control method and system thereof, and the electrical control method comprises the steps: collecting the operation state data of the flywheel energy storage system in real time, and carrying out the preprocessing of the operation state data; inputting the preprocessed operation state data into a parameter adaptive control algorithm, dynamically adjusting control parameters of the algorithm through fuzzy logic, and generating a control instruction for controlling the power converter; based on the control instruction and the real-time demand of a power grid, cooperatively controlling the output power and the direct-current voltage of the flywheel energy storage system, and when it is detected that the direct-current voltage deviates from a target range, automatically switching an electrical control mode to a voltage stabilization mode for stabilizing the direct-current voltage within the target range; and monitoring the operation state of the flywheel energy storage system in real time, and triggering a protection mechanism when at least one abnormity of overvoltage abnormity, overcurrent abnormity or mechanical fault abnormity is detected.

The invention discloses a linkage system of a roadway air distribution device and a dust removal device and a control method, and belongs to the technical field of safety and environmental protection of mine / tunnel construction. The system comprises a long-pressure air control system, a short air draft dust collection system and an intelligent control module, the long-pressure air control system forms an airflow air wall through a long-distance air distribution device to restrain dust diffusion, the short-air-draft dust collection system captures dust through a short-distance dust collection cover, and the intelligent control module dynamically adjusts operation parameters of the long-pressure air control system and the short-air-draft dust collection system based on parameters such as dust concentration and air speed. The control method comprises the stages of dust suppression, capture, weak wind area removal and intelligent adjustment, and efficient dust removal is achieved through airflow collaborative optimization and a self-adaptive control algorithm. The dust collecting rate can be increased by 50% or above, energy is comprehensively saved by 20% or above, potential safety hazards in weak wind areas are eliminated, and the device is suitable for dust-producing scenes such as driving working faces and belt conveying roadways and has the advantages of being efficient, safe, capable of saving energy and wide in applicability.

The invention discloses an expressway energy collaborative scheduling method, system and device and a medium, mainly relates to the technical field of energy collaborative scheduling, and is used for solving the problems that a traditional prediction model is difficult to capture sudden load fluctuation characteristics and a rigid control strategy causes mismatching of an adjustment instruction and a real-time demand in an existing scheme. Comprising the steps that when sudden load fluctuation occurs in a load prediction curve, compensation adjustment is conducted on the sudden load fluctuation through a self-adaptive control algorithm, and a power grid adjustment instruction set containing voltage / current double parameters of priorities is output; a preset optimal charging and discharging objective function and constraint conditions of each energy storage unit are obtained, and the constraint conditions comprise the real-time meteorological data, the priority of the power grid adjustment instruction and the rated capacity of each energy storage unit; using a preset optimal charging and discharging objective function and constraint conditions to obtain charging and discharging power; and determining the charging and discharging power of each energy storage unit based on a preset distribution weight.

The invention discloses a hydraulic multi-way valve flow self-adaptive control system and method based on CAN communication, and relates to the technical field of hydraulic control, and the method comprises the following steps: monitoring a flow parameter and a pressure parameter in real time, and transmitting the flow parameter and the pressure parameter to a main control unit through a CAN bus; the main control unit is configured to receive a monitoring signal of the sensor through the CAN bus and generate and send a control signal to the hydraulic multi-way valve; according to a control signal sent by the main control unit, the flow direction and the flow size of hydraulic flow are dynamically adjusted; and mechanical actions are executed according to the adjusted hydraulic flow of the hydraulic multi-way valve, operation data are analyzed in real time, abnormality of the flow, the pressure, the valve opening degree and the actuator state is detected, and a fault diagnosis result is generated. The flow control performance and the system reliability of the hydraulic multi-way valve are remarkably improved by integrating the real-time data acquisition function, the self-adaptive control algorithm function and the fault diagnosis function.

The invention provides a gradient density metal implant additive manufacturing method, and relates to the technical field of selective laser melting additive manufacturing, according to the method, continuous gradient density distribution of a metal implant from an outer layer to an inner layer is achieved through multi-mode sensor fusion and a self-adaptive control algorithm, biomechanical characteristics of a skeleton are accurately simulated, and the manufacturing precision of the metal implant is improved. The step of designing a porous structure is omitted; cortical bone (high density) and cancellous bone (porous) structures of human bones can be accurately simulated, and the biomechanical suitability of the implant is improved.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

The invention relates to the technical field of high-end equipment manufacturing, in particular to a friction stir welding system pressure control method and system, electronic equipment and a storage medium, and the method comprises the steps that real-time sensor data in the welding process is obtained, and a controller preset algorithm module is used for analyzing and processing to obtain a real-time pressure value; and meanwhile, a plurality of modules such as a self-adaptive control algorithm module, a pressure prediction model, an online parameter identification model, a multi-mode switching module, an abnormal prediction model and an equipment health state evaluation model are further arranged to realize dynamic adjustment and control. The effects that the working pressure value of the friction stir welding system can be continuously and dynamically adjusted, the welding process is kept stable, self-adaptive control and precise control are achieved, the welding quality stability is improved, abnormity is early warned, parameters are adjusted, and the fault-tolerant threshold value is dynamically adjusted to maintain the control precision are achieved.

The invention requests to protect a multi-sensor fusion adaptive motion control system and method for a boat-type lotus root harvester, and the system comprises a multi-sensor array which is used for obtaining the position, posture, speed, inclination angle and motor load information of the lotus root harvester, and comprises a GPS, an IMU, a current sensor and an encoder; the microcontroller is used for running an extended Kalman filter (EKF) data fusion algorithm, a multi-mode PID self-adaptive control algorithm, a pure tracking path planning algorithm and a nonlinear model prediction control algorithm, and comprehensively considering path tracking precision, control smoothness and multiple constraint conditions in a prediction time domain through a rolling time domain optimization strategy; generating an optimal control instruction according to the state estimation; the executing mechanism comprises a walking motor and a steering engine and is used for responding to the control instruction and driving the lotus root harvester to move; the invention discloses a Simulink hardware-in-the-loop verification system.