416 results about "Fuzzy pid control" patented technology

The invention relates to the technical field of automation, in particular to an automatic monitoring and optimizing system for a fine chemical production process, which comprises the following steps of: extracting time-frequency domain fusion characteristic quantity of stirring torque power time sequence fluctuation data in real time through an incidence matrix construction module, dynamically inverting a thixotropic index by combining a deep neural network model, and optimizing the stirring torque power time sequence fluctuation data; the problem that a traditional method is difficult to perceive material rheological characteristics in real time is solved. The dynamic coupling analysis module analyzes the material viscosity change rate based on the thixotropic index, fuzzy PID control is adopted to generate a stirring speed adjusting instruction dynamically matched with the viscosity and a jacket temperature compensation value, and the defects of uneven mixing and local overheating caused by lagging adjustment of process parameters are overcome; the multi-target collaborative optimization module locks the mass optimization weight in the viscosity sudden change stage, rapidly stabilizes the reaction condition through a feed-forward compensation algorithm, dynamically balances the stirring power consumption and the heat transfer efficiency based on Pareto frontier search in the steady state stage, and solves the conflict between the mass and the energy efficiency target.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention discloses an energy efficiency control method for a cooling fan of an industrial electric cabinet, and the method comprises the steps: firstly collecting temperature, humidity, equipment current / voltage and other multi-mode sensing data, carrying out the PCA algorithm dimension reduction and feature extraction, combining with Granger causality test, thermodynamic principle and machine learning algorithm, and carrying out the building and fusion, thereby obtaining a cooling efficiency model. Thirdly, preprocessing current and voltage data of the equipment, training and optimizing an LSTM model to predict load power, and calculating a fan rotating speed control value through a heat dissipation efficiency model; then, a PWM speed regulation signal is generated by adopting a fuzzy PID control algorithm, and a pulse width modulation and smooth speed regulation mixed strategy is applied to the duty ratio; and finally, energy efficiency indexes are collected to compare and update the weight of the LSTM model, knowledge distillation is introduced to optimize new model training, and self-adaptive adjustment of the rotating speed of the fan is achieved. The rotating speed of the fan can be accurately adjusted, energy consumption is reduced, the energy utilization efficiency is improved, performance reduction of equipment due to overheating is effectively avoided, and the service life of the equipment is prolonged.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention discloses a molten salt heat storage and heat exchange efficiency optimization system and method in a whole-plant technological process. The system comprises a high-temperature molten salt storage tank, a low-temperature molten salt storage tank, a temperature sensor group, a variable-frequency pump group, a multi-stage heat exchanger, an intelligent control unit, a heat buffer module and the like, and the high-temperature molten salt storage tank and the low-temperature molten salt storage tank are connected through a conveying pipeline to form a circulation loop. The intelligent control unit regulates and controls the rotating speed of the pump set and the valve opening degree based on a fuzzy PID algorithm, the heat buffering module reduces heat loss, and the multi-stage heat exchanger enhances heat exchange. The method comprises the steps of temperature and flow data collection, efficiency deviation value calculation, fuzzy PID control, thermal buffer optimization, waste heat feedback and the like. Besides, the system also has the functions of digital twinborn prediction, multi-objective optimization, abnormal working condition self-healing and the like, can be connected with a DCS (Distributed Control System) to obtain energy consumption data and dispatch stored energy, and also can predict equipment faults. The fused salt heat energy storage and heat exchange efficiency is effectively improved, reasonable energy allocation and cost control are achieved, and the reliability and stability of the system are enhanced.

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention provides a water electrolysis hydrogen production monitoring and control method with dynamic fluctuation adaptability, and relates to the technical field of hydrogen production. Comprising the steps that a multi-dimensional operation parameter set of an electrolytic cell is collected in real time through a multi-source sensor array, an electrolysis process prediction model is established based on the LSTM and the CNN neural network, and therefore the prediction result of the electrolysis state is determined. And according to the prediction result, a dynamic parameter adjustment matrix is constructed, and the electrolytic current density, the electrolyte concentration and other key parameters of the electrolytic cell are adjusted for the first time through fuzzy PID control. After adjustment, the multi-dimensional operation parameter set is updated, the health degree index of the electrolytic cell is calculated, and the relation between the index and a preset health index threshold value is monitored in real time. And second-time adjustment is carried out according to the monitoring result, so that safe and efficient operation of the electrolytic cell under the dynamic fluctuation condition is ensured. The problems that in the prior art, an electrolytic cell is slow in response to load change, insufficient in monitoring capacity, dependent on manual maintenance and the like are effectively solved.

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

The invention relates to an automatic identification control method and system based on heat dissipation of a liquid cooling plate in a heat exchange system. The method comprises the following steps: acquiring real-time operation data of a target liquid cooling plate; establishing a dynamic thermal resistance network model, and calculating a current thermal resistance value; refrigeration demand parameters are obtained, and a demand weighted thermal resistance value is generated in combination with the current thermal resistance value; thermal resistance deviation is obtained based on the demand weighted thermal resistance value, and the opening degree of a cooling liquid flow valve in the heat exchange system is dynamically adjusted through a fuzzy PID controller; the PID parameters are updated through a preset optimization algorithm, and the fuzzy PID controller is optimized; according to the automatic identification control method and system based on heat dissipation of the liquid cooling plate in the heat exchange system, real-time dynamic adjustment of the flow of the cooling liquid is achieved by establishing the dynamic thermal resistance network model and combining fuzzy PID control, the problems that a traditional method lags in response and is insufficient in control precision are solved, and the control efficiency is improved. The method has the effects of improving the dynamic response speed, improving the control precision and optimizing the energy efficiency.

The invention provides a high-efficiency control method and system for a central air conditioner refrigeration machine room, and relates to the technical field of automatic control, and the method comprises the steps: 1, obtaining chilled water supply and return water temperature difference and flow data, and calculating the dynamic cold load demand of a building in combination with the personnel distribution thermal imaging data of each floor; and 2, on the basis of the dynamic cooling load requirement, a dynamic adjustment model for the number of running water chilling units is built, matching degree calculation is conducted on the actual cooling load value and the single refrigerating capacity of the water chilling units through a fuzzy PID control algorithm, and a final unit running combination scheme is generated. By calculating the cooling load requirement, dynamically adjusting the operation parameters of the water chilling unit, the chilled water pump and the cooling tower and balancing the equipment operation time, the system energy efficiency is effectively improved, the energy consumption and the operation and maintenance cost are reduced, and stable operation of the refrigerating system is guaranteed.

The invention discloses an integrated dedusting-cooling system and method for a high-temperature mining working face of a mine. The integrated dedusting-cooling system comprises a sensor monitoring device, a fuzzy PID control device and a dedusting-cooling device, after the compressed air injection unit of the dust removal and cooling device sucks high-temperature dust-containing airflow in the environment into the system, primary dust removal is performed in the humidification and growth unit firstly, then cold fog cooling and secondary dust removal are performed in the cold fog dust removal unit, and finally airflow purification and tertiary dust removal are performed in the dust filtration and fog dissipation unit. In addition, the sensor monitoring device is adopted to collect environmental monitoring data in real time and feed back the environmental monitoring data to the fuzzy PID control device for processing, when any monitoring data deviates from the environmental parameters set by the system, the working condition of the dust removal and cooling device can be adaptively regulated and controlled, and then continuous dust removal and cooling are conducted on the high-temperature mining working face under different environmental parameters. And efficient cooperative treatment of dust and heat damage on the high-temperature mining working face of the mine is realized. The device has the advantages of being small in dust escape amount, high in dust removal-cooling efficiency and low in energy consumption.

The invention discloses a wind power tower lifting platform based on self-adaptive weight and fuzzy PID control and an adjusting method of the wind power tower lifting platform. The lifting platform comprises a main frame body, a ladder stand component, a guide rail assembly, a lifting platform mechanism and a lifting driving assembly. The device comprises a transmission chain wheel assembly, a driven chain wheel assembly, a counterweight assembly, an anti-falling assembly, a top corridor, a sliding terminal and a conductive sliding frame. The adjusting method comprises the steps of sensor installation and data acquisition; processing and analyzing data; self-adaptive weight optimization adjustment is carried out, the load condition and the environment condition of the current platform are judged, and the optimal balancing weight position is calculated in real time; ensuring that the lifting speed is matched with the current load according to a weight optimization result; and carrying out multi-layer neural network load prediction. The stability and safety of the platform under complex working conditions are improved, and the problems that a traditional method is slow in response and insufficient in adjusting precision when dealing with load fluctuation and environment change are solved.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention discloses a transmission driving control method of a bimodal integrated engine. The transmission driving control method comprises the following steps: S1, state sensing; s2, mode decision making; S3, dynamic adjustment; s4, mode switching; S5, power optimization distribution; s6, adaptive learning is carried out; and S7, fault-tolerant guarantee. The invention further discloses a bimodal integrated engine transmission system which comprises the following modules: a power input module; a transmission adjusting module; a state sensing module; an intelligent control module; and a fault-tolerant guarantee module. Parameters are dynamically adjusted through fuzzy PID control according to speed deviation, meanwhile, real-time correction is conducted in combination with a multi-mode intelligent algorithm, speed fluctuation is precisely controlled within + / -3 km / h, flight stability is guaranteed, a model prediction control and multi-mode intelligent control cooperation mechanism is adopted, an optimal power output sequence is calculated in advance, coordinated distribution is completed, and the optimal power output sequence is controlled to be within + / -3 km / h. The dynamic response speed is increased by 40%, and the high-dynamic task requirement is met.

The invention relates to the technical field of agricultural machinery control, in particular to a multi-variety vegetable harvester dynamic identification and feeding control system based on image processing, which comprises an image acquisition module, an environment sensing module, an image processing module, a data fusion module, a dynamic identification and tracking module and a feeding control module. Through binocular stereo vision and millimeter wave radar fusion, three-dimensional space positioning and dynamic tracking of the brassica oleracea corm are realized. Track prediction is carried out in combination with extended Kalman filtering and a long-short-term memory network, and the clamping path of the mechanical arm is optimized; the clamping force is adaptively adjusted through fuzzy PID control, and accurate feeding of the cabbages is achieved in combination with speed control of the conveying belt. The automatic cabbage harvesting machine improves the automation level of cabbage harvesting, effectively reduces the damage rate, improves the operation stability and harvesting efficiency, and is suitable for intelligent harvesting operation of a large-scale cabbage planting base.

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

The present invention relates to the technical field of water distillers, and disclosed are a thermocompression multi-effect water distiller system and a control method therefor. The thermocompression multi-effect water distiller system comprises a distillation unit and a control unit; the distillation unit comprises a multi-effect evaporator; the multi-effect evaporator is a horizontal tube falling film evaporator and has a side-by-side configuration, and the shell pass of a preceding effect evaporator is communicated with the tube pass of a following effect evaporator; the control unit controls the distillation unit during working, and the control unit is a feedforward-cascaded fuzzy PID model predictive control system and comprises an inner-loop control system and an outer-loop control system; the inner-loop control system uses fuzzy PID control, and the inner-loop control system is provided with a control system for fluctuations in industrial steam pressure Psteam; and the outer-loop control system uses model predictive control. In the present invention, by using the thermocompression multi-effect water distiller system and the control method therefor, the heat exchange efficiency is greatly improved; and a novel control system is provided, enabling normal operation within a range of 40-200% of a set operating condition.

The invention discloses a fuzzy PID (Proportion Integration Differentiation) control method for an upper limb rehabilitation robot based on myoelectricity feedback. The fuzzy PID control method comprises the following steps: step 1, sampling surface myoelectricity signal data; 2, performing signal processing on the surface electromyogram signal data; 3, performing feature extraction on the surface electromyogram signal data after signal processing through a feature selection method so as to obtain a key feature set; 4, creating a fusion model, training the fusion model by using the fatigue state key feature set, and determining an optimal fusion model; detecting the fatigue state of the person to be detected in real time; and step 5, obtaining a fatigue state corresponding to the currently obtained surface electromyogram signal of the patient in real time, and dynamically adjusting a corresponding PID parameter in combination with a self-adaptive fuzzy PID controller so as to adapt to different fatigue states. Proportional, integral and differential parameters can be dynamically adjusted according to the real-time fatigue state of a patient; the method adapts to different fatigue levels of patients, and control flexibility and response speed are improved.

The invention discloses a robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

The invention discloses an intelligent irrigation method and system and a related device, and relates to the field of irrigation, the intelligent irrigation system comprises a control system and an irrigation system, the irrigation system comprises a photovoltaic power supply module, a detection module and an irrigation execution module, and the photovoltaic power supply module is used for supplying power to the detection module and the irrigation execution module; the control system responds to the irrigation starting instruction, controls the detection module to collect the soil humidity at the current moment, and calculates the soil humidity deviation value between the soil humidity at the current moment and the target humidity; the detection module calculates the soil deviation value change rate between the current moment and the previous moment; the detection module calculates irrigation duration corresponding to the soil humidity deviation value and the change rate of the soil deviation value by using fuzzy PID (Proportion Integration Differentiation) control; and the irrigation execution module performs irrigation according to the irrigation duration. According to the method, the irrigation duration is adjusted according to the actual soil humidity condition, manual subjective judgment is not needed, automatic power supply is conducted through the photovoltaic power supply module, automatic operation is guaranteed, and effective irrigation is achieved.

The invention relates to the technical field of electric clamping jaw control, in particular to a fuzzy PID (Proportion Integration Differentiation) control-based electric clamping jaw control method for cutting off a cable, which comprises the following steps of: controlling a power source permanent magnet synchronous motor of an electric clamping jaw, and obtaining the position and the rotating speed of a rotor of the permanent magnet synchronous motor through an encoder; a three-closed-loop cascade control structure comprising a position PID controller, a speed PID controller and a current fuzzy PID controller is built, a clamping force PID controller is introduced and used for dynamically adjusting the clamping force of an electric clamping jaw in the cable cutting-off process, the control precision and stability of the clamping force are improved, and the clamping force control precision and stability are improved. An SVPWM algorithm is adopted to modulate the built three-phase inverter circuit so as to reduce the pulsation of the output torque of the motor, and then the robustness and stability of the whole vector control system are improved.

The invention discloses a high-precision metering and weighing system based on PLC control, relates to the technical field of metering and weighing, and realizes precise product weight measurement and sorting by combining a pressure sensor, a weighing module and a fuzzy PID control algorithm. The system can quickly convert the weight information of the product to be detected into a voltage signal, and after amplification and analog-to-digital conversion, an accurate digital signal is provided for the PLC. And the PLC performs error analysis and parameter adaptive adjustment by using a fuzzy PID algorithm, so that the control precision and the response speed are improved. In addition, through quantitative processing and use of a membership function, the system can effectively reduce errors and perform self-correction, and therefore the accuracy and reliability of overall weighing and sorting are improved. According to the invention, the weighing system can realize high-efficiency automatic control in industrial application, and can meet the weighing requirements of different products.

The invention provides a method for regulating and controlling technical parameters in a continuous preparation process of a filling material, and relates to the technical field of mine filling automatic control. According to the method, aiming at the condition that the material level of the stirrer is in a safety interval, pumping pressure-stirrer material level double-rate interval control is adopted, that is, the material level of the stirrer is maintained in a safety range required by a process, and meanwhile, the pumping pressure for conveying a filling material required by underground filling is ensured; after the pumping pressure and the material level of the stirrer are both stable, aiming at the condition that the concentration of the slurry pump is not in a set concentration range, adopting concentration fuzzy PID control, and adjusting the concentration of the slurry pump until the concentration is stable in the set range; aiming at the conditions that the concentration of a slurry pump is in a set concentration range and the frequency of a stirrer is in a normal operation range, torque fuzzy PID control is adopted, the frequency of the stirrer is adjusted, and energy conservation and emission reduction are achieved on the premise that the shear stress of a filling material is stabilized.

The invention relates to the field of temperature control, in particular to a solution conductivity meter temperature control method based on fuzzy PID, and the method comprises the steps: collecting the temperature data of a measurement solution through a high-precision temperature sensor; acquiring temperature data of the measurement solution, and calculating a temperature deviation value and a temperature change rate of the measurement solution; inputting the temperature deviation value and the temperature change rate into a fuzzy PID (Proportion Integration Differentiation) control module; according to the input temperature deviation value and the temperature change rate, the input of the PID controller is corrected through a fuzzification and fuzzy rule table in the fuzzy PID control module and a corresponding variable quantity output by defuzzification; according to an algorithm, updated PID parameters are obtained, and then a pulse width control signal is obtained by an incremental PID control algorithm to control a driving circuit to dynamically adjust a TEC semiconductor cooler, so that the temperature of the measured liquid is controlled, the control speed is effectively improved, the robustness of the system is enhanced, and the temperature is stabilized near the target temperature.

The invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is improved.

The invention discloses an automobile EMB system clamping force accurate control method based on an extended state observer, and belongs to the technical field of electric automobile EMB systems, and the method comprises the following steps: S1, building a novel EMB system five-degree-of-freedom dynamic model containing a LuGre friction model by using an EMB system dynamic relationship; s2, simplifying the five-degree-of-freedom dynamic model of the novel EMB system, estimating the load torque of an EMB driving motor according to the extended state observer, and then estimating the clamping force of the EMB system according to the load torque and the simplified five-degree-of-freedom dynamic model of the novel EMB system; and S3, replacing the measured value to be a feedback value of clamping force closed-loop control of the EMB system according to the estimated EMB clamping force, and accurately controlling the clamping force by adopting fuzzy PID control. By the adoption of the automobile EMB system clamping force accurate control method based on the expansion state observer, the problems that a pressure sensor is high in cost and automobile braking performance is prone to being reduced are solved, and clamping force response is accurately controlled through a fuzzy PID closed loop.

The invention belongs to the related technical field of temperature control, and discloses a fuzzy PID temperature control method and system based on an improved PSO algorithm, and the method comprises the steps: S1, constructing a temperature control system comprising a fuzzy PID controller; s2, parameters of a fuzzy PID controller are optimized based on an improved PSO algorithm; s3, performing temperature control by using the optimized fuzzy PID controller; s2 comprises the steps of S21, setting algorithm parameters and initializing a population; s22, updating the speed and position of particles in the population in each iteration, and calculating a fitness value; when the speed and the position of the particles in the population are updated, the inertia weight and the learning factor are updated by adopting a nonlinear decline strategy; and S23, when the optimal fitness value of the group is smaller than a preset fitness value or the maximum number of iterations is reached, ending the algorithm. According to the invention, the POS algorithm is improved, the global search capability and the iteration efficiency are improved, and the optimized fuzzy PID control can ensure the temperature control precision.

The invention discloses an intelligent controllable road bridge vibration detection method based on magnetorheological fluid and a damping device, and belongs to the technical field of bridge engineering structure health monitoring and vibration control, and the method comprises the steps: calculating a comprehensive vibration strength index through multi-source signal fusion and adaptive Kalman filtering, and estimating the bridge speed; the damping force is mapped through the pre-calibration database, and a damper state verification mechanism is introduced; a gain self-adaptive ceiling algorithm is combined with a fuzzy PID control force algorithm for tracking to realize accurate control; finally, the health state of the system is evaluated through performance degradation indexes of temperature compensation, corresponding devices are integrated with a sensing network, a control unit and a distributed acquisition architecture, and the problems that a traditional system is single in state sensing, fixed in control strategy, lack of a self-diagnosis function, complex in engineering wiring and the like are solved. The comprehensive solution integrating intelligent monitoring, self-adaptive control and health self-diagnosis is realized, and the reliability, adaptability and maintainability of the bridge damping system are remarkably improved.

The invention provides a temperature control method, device, equipment and system, a storage medium and a program product, and relates to the technical field of industrial control, and the method comprises the steps: determining a PID parameter of a temperature controller of a controlled object; and based on the obtained current environment temperature of the controlled object and the target control temperature of the controlled object, carrying out adaptive adjustment on PID parameters of the temperature controller according to a fuzzy PID control algorithm. And the temperature controller is adjusted according to the PID parameters after self-adaptive adjustment, and temperature control is carried out on the controlled object according to the adjusted temperature controller. According to the invention, the PID parameters of the temperature control system are adaptively adjusted according to the fuzzy PID control algorithm, so that the PID parameters can adapt to the dynamic change of the environment temperature of the system, the overshoot of the system can be reduced, the reaction time of the system can be shortened, and the system can accurately control the temperature of the controlled object under the variable working condition or multi-disturbance condition.