40 results about "Fuzzy pid control" patented technology

This invention discloses an intelligent control logic system for a side-discharge bucket, relating to the field of engineering machinery control technology. It includes a sensor module, an actuator, and a control module. The sensor module collects multi-dimensional data from the side-discharge bucket. The actuator drives the side-discharge bucket to rotate 0° to 90° around its axis, dumping material onto a conveying device, and simultaneously activates a stirring device to agitate the material and prevent clumping. The control module detects the initial states of the sensor module and the actuator, reads the multi-dimensional data to determine if the material weight is excessive, and then, based on the material's characteristics, introduces a fuzzy PID control algorithm to control the actuator. This invention is applicable to material handling equipment such as loaders, achieving efficient and precise side-discharge operations through automatic and manual dual-mode coordinated control.

This invention relates to a machine learning-based intelligent control system for molten salt heating, belonging to the field of thermal energy storage and intelligent control. It includes a data acquisition module, a machine learning prediction module, and a heating control module. The data acquisition module acquires multi-dimensional state data within the molten salt storage tank in real time, including temperature, liquid level, flow rate, and pressure distribution information. The machine learning prediction module constructs a nonlinear dynamic prediction model based on a long short-term memory network to accurately predict temperature change trends over a set future time period. The heating control module dynamically adjusts the heater's output power using an adaptive fuzzy PID control algorithm based on the deviation between the prediction result and the target temperature. This overcomes the temperature fluctuation problems caused by large delays, nonlinearity, and thermal inertia in traditional molten salt heating processes, improves the accuracy and stability of temperature control, significantly reduces the overall energy consumption of the system, and extends the service life of the heating equipment.

The application relates to the technical field of adaptive control, in particular to an intelligent control method and system for a proofing and steaming integrated cabinet, which comprises the following steps: taking the concentration of carbon dioxide in the proofing and steaming integrated cabinet as a fermentation degree, dynamically adjusting the membership function of each output fuzzy set in a fuzzy PID control model according to the fermentation degree, and obtaining an initial output; taking the concentration change rate of carbon dioxide as a fermentation activity index representing the metabolic activity of yeast, determining an integral inhibition coefficient according to the fermentation activity index, and inhibiting the integral gain adjustment amount in the initial output according to the integral inhibition coefficient to obtain a control output; and adjusting the parameters of a PID controller according to the control output to realize adaptive control of the proofing and steaming integrated cabinet. Through the technical scheme, accurate adaptive control of the proofing and steaming integrated cabinet can be realized in the food fermentation process.

The application discloses a kind of AGV autonomous mapping and path planning method and system based on multi-sensor fusion, and it is related to robot navigation technical field.The method includes: obtaining environmental information by laser radar and depth camera and carrying out coordinate conversion;Multi-sensor data fusion is carried out using extended Kalman filter algorithm, and environment grid map is constructed based on fusion data, and map update is optimized using dynamic key frame selection strategy;Real-time positioning of AGV is realized based on Monte Carlo localization algorithm combined with adaptive particle sampling strategy;Global path planning is carried out based on improved multi-step A-Star algorithm, and the kinematic constraint of Mecanum wheel AGV is integrated into heuristic function;Fuzzy PID control algorithm is used for path tracking, and dynamic obstacle detection and local obstacle avoidance are carried out simultaneously.The application improves environmental perception ability through multi-sensor fusion, improves path quality and execution efficiency through improved path planning algorithm, and is suitable for AGV autonomous navigation in complex industrial environment.

The application discloses a deep-groove wrapped lithium battery liquid cooling heat dissipation system based on fuzzy PID control and relates to the technical field of battery thermal management. The system comprises a battery module unit, a liquid cooling circulation loop, a data acquisition unit and a main controller. The battery module unit is integrated with square lithium batteries and deep-groove wrapped heat exchangers. The liquid cooling circulation loop is used for realizing external energy transfer. The data acquisition unit is used for collecting temperature signals reflecting the thermal state of the square lithium batteries in real time. The main controller performs logical reasoning on the temperature signals through a fuzzy PID control program, generates a pulse width modulation (PWM) signal, and adjusts the cooling liquid flow rate of a variable frequency water pump and the heat exchange intensity of a radiator assembly. Through the deep cooperation of the deep-groove wrapped heat exchange structure and the fuzzy PID control program, the heat exchange efficiency, temperature uniformity and temperature control precision of the lithium battery module are greatly improved, the optimal balance of heat dissipation energy efficiency is realized, and the operation reliability, safety performance and cycle life of the battery system are enhanced.

This invention discloses a fuzzy PID control method for suppressing the rebound of a tethered towed system, belonging to the field of spacecraft dynamics and control. The implementation method involves: constructing a simplified model of the tethered towed system; introducing fuzzy control into the PID control; and calculating the control force to be applied to the tether by observing the error and rate of change between the distance between the tether and the space debris and the desired distance. This control force is then applied to the tether to actively control the tethered towed system, bringing the distance between the tether and the space debris closer to the desired state, thus suppressing the rebound of the tethered towed system. This invention can calculate the control force to be applied to the tether simply by observing the error and rate of change between the distance between the tether and the space debris and the desired distance, offering advantages such as simple control method, low computational load, high operability, and strong robustness. This invention can suppress the rebound of the tethered towed system, ensuring its safe and stable operation.

The present application relates to the technical field of intelligent production, and specifically discloses a paper breakage prediction and automatic cruise compensation method and system for a corrugated board production line, which acquires and pre-processes historical and real-time operation data, constructs a multi-modal fusion paper breakage prediction model, outputs a paper breakage risk probability value to realize hierarchical early warning, obtains a final tension compensation value after a preliminary compensation through parameter secondary setting, queries a cruise compensation strategy mapping table in combination with real-time vehicle speed and flute type parameters, generates a cooperative control instruction to issue a PLC, and realizes intelligent cruise compensation and recovery. The present application can combine adaptive fuzzy PID control and dynamic parameter setting technology to realize automatic cruise compensation for paper breakage, thereby solving the problems in the prior art that a prediction model relies on a large amount of historical data, has weak generalization ability for unobserved fault modes, it is difficult to set parameters for a traditional control algorithm under complex working conditions, response is lagged, and adaptive adjustment is insufficient when orders are switched, and finally improves the stability and production efficiency of the production line.

This invention belongs to the field of unmanned construction technology and discloses a collaborative intelligent unmanned control method and system for asphalt pavement paving equipment clusters. The method includes: integrating and outputting standardized sensing data streams; performing initial path planning based on the standardized sensing data streams and pre-set construction tasks; based on the initial path planning results and the standardized sensing data streams, invoking a multi-field coupling model, a load balancing strategy library, and a fuzzy PID control algorithm to calculate construction quality risk nodes, equipment collaborative status, and process parameter deviations in real time, generating a primary control instruction set; evaluating the collaborative relationship and optimizing the risk-benefit of the primary control instruction set, the standardized sensing data streams, and the material lifecycle traceability database to obtain an optimized control instruction set; and distributing the optimized control instruction set to the execution units of the paving equipment to perform collaborative operations. This invention significantly improves construction accuracy and effectively solves problems such as uneven accuracy in traditional construction methods.

This invention provides a central dynamic cooling system based on AI adaptive load prediction data, belonging to the field of dynamic cooling technology. It includes a load prediction module, a cooling decision module, a decision execution module, and a feedback optimization module. The load prediction module constructs a load prediction model based on a time-series attention mechanism, taking operating parameters and environmental parameters as input, and outputting load prediction results for a preset future time period. The cooling decision module constructs a cooling demand assessment system and generates adjustment schemes for dynamic cooling parameter combinations using a whale optimization algorithm. The decision execution module uses a fuzzy PID control algorithm to execute the dynamic cooling parameter adjustment schemes. The feedback optimization module corrects the parameters of the prediction model and decision algorithm through Bayesian inference. This invention achieves differentiated allocation of cooling resources through multi-dimensional perception and precise control, adapting to the differences in heat distribution in different areas of the data center, extending the service life of servers, and reducing the operation and maintenance costs of the data center.

The application relates to a semi-trailer transverse stability self-adaptive control method based on online identification of loading conditions, belongs to the field of intelligent commercial vehicle active safety control and vehicle transverse dynamics control, and comprises the following steps: acquiring vehicle dynamics parameters; calculating stability error; online identifying loading conditions; calculating additional yaw moment; and executing brake force distribution control; the application realizes online identification of semi-trailer loading and mass center height, and adaptively adjusts fuzzy PID control parameters according to the identification results, optimizes differential brake moment distribution, effectively suppresses the yaw instability, folding and side-slip risk of the semi-trailer under different loading conditions, and improves the transverse stability and control robustness of the vehicle under complex conditions.

This invention discloses an artificial intelligence-based method and system for controlling the amount of liquid fertilizer drip irrigation in agricultural planting. The method involves collecting multi-source datasets through a multimodal sensing network; fusing the multi-source datasets using a STAFN spatiotemporal attention fusion network to obtain a fused dataset; establishing a dual-channel demand prediction model, inputting the fused dataset into the model, and outputting prediction results; calculating the fertilizer concentrate mixing ratio based on the prediction results; adjusting the fertilizer absorption rate of the Venturi fertilizer applicator according to the fertilizer concentrate mixing ratio using a fuzzy PID control algorithm; dividing the planting area into several intelligent irrigation zones; and implementing differentiated fertilization for each zone using a pulse drip irrigation strategy. This method adapts to the differences in soil and crops in different regions, improving drip irrigation efficiency and accuracy.

This invention belongs to the field of chili drying and processing technology, specifically relating to an intelligent drying system for the synergistic optimization of chili drying quality and energy consumption. First, a sensor monitoring module collects drying process data and calculates temperature deviation and deviation change rate. Second, a fuzzy PID control module performs fuzzification processing, fuzzy inference, and parameter self-tuning to obtain PID parameter correction values, which are then self-tuned online to generate a control signal. Finally, an actuator module performs signal conversion and power amplification to generate actual drive commands to drive the heat source coupling and waste heat recovery module, adjusting the hot air temperature, heat exchange efficiency, air volume, and air pressure in the drying chamber. This invention achieves precise control of the drying process and efficient recovery of waste heat, reducing system energy consumption while ensuring chili drying quality, and solving the problems of difficulty in synergistic optimization of quality and energy consumption, insufficient control precision, and low energy utilization efficiency in existing technologies.

This invention discloses a digitally adjustable baling cotton harvester, belonging to the field of intelligent control of agricultural machinery. It integrates onboard sensors, a machine-to-cloud management platform, and a fuzzy PID control system into a traditional cotton harvester, forming an intelligent harvesting system and equipment with autonomous perception and dynamic adjustment capabilities. The onboard sensors monitor the field environment, cotton harvester operating parameters, and harvesting quality in real time. The data is transmitted and processed through the machine-to-cloud management platform to generate control schemes for key parameters such as harvester head height, travel speed, and drum negative pressure, which are then distributed to the fuzzy PID control system to achieve dynamic and precise adjustment of the cotton harvester's operating parameters, thereby realizing a closed-loop control system integrating "perception-decision-execution." The digitally adjustable baling cotton harvester of this invention can adaptively adjust its operating status according to actual harvesting conditions, significantly improving operational stability, harvesting efficiency, and overall intelligence, achieving efficient and high-quality cotton harvesting.

The present application relates to the technical field of power repair and emergency power supply, and particularly relates to a diesel generator set group parallel operation control method and system based on fuzzy PID control. Real-time operation parameters of the diesel generator set are acquired to construct a fuzzy PID control input vector; a mapping relationship table between the control input vector and a preset parallel operation fuzzy PID control rule is established; a weighted average algorithm is used for defuzzy calculation to obtain a real-time PID parameter correction amount and correct the current PID control parameter; after voltage leveling, frequency adjustment and phase calibration of each unit based on automatic quasi-synchronous parallel operation logic, the units are connected in parallel to construct a parallel control group; power distribution is performed on the speed regulator and excitation controller of each unit according to the real-time fuzzy PID control parameter, and cyclic adjustment is performed until the error converges. The problems of poor parallel operation stability and uneven power distribution of the diesel generator set group caused by fixed traditional PID control parameters are solved, intelligent control of multi-unit parallel operation is realized, and the system operation stability and efficiency are improved.

The application discloses a kind of based on video monitoring's dry bulk cargo wharf dust monitoring and intelligent control system and method, belong to environmental monitoring and dust control technical field.The system includes: video acquisition module, for obtaining wharf multi-view real-time video;Image analysis module is used to fog enhancement and region growth segmentation to video image, extracts dust feature;Multi-source data fusion module uses adaptive weighted fusion algorithm, according to real-time meteorological condition and data source confidence degree dynamically adjusts weight, with laser radar, point sensor and meteorological data are deeply fused, and global dust concentration distribution chart is generated;Intelligent decision module, according to dust distribution chart, combined with mobile target recognition obstacle avoidance and operation equipment position correlation analysis, through fuzzy PID control generates accurate spraying instruction.The application realizes the leap from point monitoring to surface perception, solves the problem that traditional method covers narrow, response lag, control extensive, with significant environmental and economic benefits.

The present application relates to the field of electric smelting magnesium electrode current tracking control, in particular to a kind of electric smelting magnesium electrode current variable domain fuzzy PID control method, for solving the problem of insufficient current control precision caused by nonlinear, time-varying characteristics in the process of magnesium electrolysis smelting, improve energy utilization efficiency and product quality stability.The method realizes the self-adaptive adjustment of fuzzy domain by constructing dynamic scaling factor function, improves the control precision under small deviation working condition, and combines with multi-population genetic algorithm (MPGA) to optimize the parameters of controller, overcome the local convergence defect of traditional single population genetic algorithm.The experimental results show that, compared with conventional control method, the present application significantly improves the key indicators such as regulation accuracy, response speed and anti-interference ability, can effectively deal with the complex working conditions such as raw material composition fluctuation and load disturbance of electric smelting magnesium furnace, reduce energy consumption and electrode loss rate.

The present application mainly relates to the technical field of material air separation, in order to solve the problem of uneven distribution of material thickness, response lag, easy to leak or blockage of air separation system, the present application provides a kind of auger conveying mechanism material uniformity self-adaptive control method and system, its core is: detecting multiple material thickness in the length direction of supply chamber, when the double-sided discharge door is closed, the material put in on one side is evenly spread by auger conveying mechanism, and the discharge mode is automatically selected according to the preset target processing capacity and real-time material thickness;Then, a double closed loop fuzzy PID control strategy is used, the equivalent deviation of supply and its change are used to adjust the speed of auger, the deviation and change of material thickness are used to adjust the angle of double-sided discharge door, and the nonlinear relationship between the angle of discharge door and the flow gap is corrected based on nonlinear geometric compensation, to realize the collaborative adaptive control between auger conveying and discharge door, and improve the uniformity, steady-state accuracy and working condition adaptability of bulk materials in the process of conveying, material uniformity and discharging.

The application discloses a kind of aircraft attitude self-adaptive stabilizing control method and system, it is related to aircraft attitude control technical field, including: aim at solving existing aircraft when flying in low air field, it is susceptible to airflow disturbance, attitude drift influence, leading to attitude fluctuation, poor stability, operation precision is insufficient, cannot complete accurate low-altitude patrol, low-altitude surveying and mapping etc. technical problem of task.Based on the dynamic characteristics of aircraft, fusion IMU inertial measurement, barometer, attitude sensor and vision auxiliary positioning multi-source data, using attitude drift pre-judgment+adaptive fuzzy PID control+vision auxiliary calibration+closed loop parameter iteration core architecture, realize the accurate control of aircraft attitude, the advance inhibition of attitude drift, ensure that aircraft maintains stable attitude in low air complex environment.

The application provides a control method of a medium-deep geothermal well heat extraction system and related equipment, space temperature distribution data of a target geothermal well from a well mouth to a well bottom, actual circulation flow data and electric meter energy consumption data are acquired; the space temperature distribution data, the actual circulation flow data and the electric meter energy consumption data are solved based on an extended finite-length line heat source digital twin model and a self-adaptive fuzzy PID control algorithm, control instructions of a circulating water pump and a heat pump unit in the medium-deep geothermal well heat extraction system are obtained, and the problems that a current geothermal well control system cannot perceive a real heat state underground, control is lagged and a heat fault underground cannot be accurately positioned are solved.

This invention discloses an automated control method and system for the production of spandex spinning oil. The method uses a DCS (Distributed Control System) to detect and self-check the tank level, utilities, and pump / valve status. Upon successful self-check, production access is unlocked; otherwise, it is locked and an alarm is triggered. After unlocking, process parameters from the formula library are retrieved. Material is weighed via a variable frequency pump PID controller linked to a weighing sensor, and the feed flow rate is controlled by fuzzy PID, following the feeding sequence and timer intervals. The DCS issues a grinding command, collecting particle size data in real time; if the particle size does not meet the standard, the grinding time is extended until it does. Heating and stirring are initiated based on the mixing tank level signal, with timed temperature increases to the set value and then maintained. After the two processes are completed, the ground material is fed into the mixing tank for secondary stirring. Successful sampling triggers filling, with fuzzy PID controlling the filling flow rate. Once the liquid level is reached, the valve is closed, and a delayed cleaning process is initiated. After filling is complete, the entire line is shut down. This invention solves the problems of fixed PID control parameters and insufficient interlocking of all processes in traditional PID control methods.

The application provides a kiln control simulation method based on a computational heat transfer model, and the method comprises the following steps: determining a parameter domain of a PID control model and membership rules; introducing a fuzzy controller to construct a fuzzy PID control model in combination with an optimized membership rule table, and correcting parameter values according to a fuzzy calculation result; constructing a computational heat transfer model of a kiln and performing reliability verification, coupling the computational heat transfer model with the fuzzy PID control model, simulating a furnace temperature field by means of the computational heat transfer model, and updating boundary conditions by calling the fuzzy PID control model when the calculation converges; performing multiple iterations, taking a correlation coefficient reaching a preset value as an optimal solution of parameters, and performing kiln control simulation. The application realizes dynamic simulation of a kiln temperature control process, makes the simulation more consistent with actual operation of the kiln, and improves the accuracy of the kiln control simulation.

The present application relates to the technical field of industrial furnace temperature control, in particular to a curing furnace temperature regulation system based on fuzzy PID algorithm, which comprises a double-jacket furnace body, a partition temperature control heating module, a multi-dimensional temperature sensing module, a furnace airflow uniform distribution module, a fuzzy PID control module and a remote monitoring operation and maintenance module; the furnace body is divided into at least four temperature control zones by heat insulation gratings, and the load roller at the bottom of the temperature control zone is driven by a driving assembly; the heating module comprises a carbon fiber heating plate, a far-infrared heating tube and a high-temperature heat-conducting oil compensation interlayer; each group of the sensing module comprises six multidirectional sensors; the airflow module uniformly distributes airflow through closed loop circulation and cyclone air nozzles; the fuzzy PID module is internally provided with a special algorithm, process formula storage and parameter self-learning functions; the remote module supports 5G / industrial Ethernet dual-mode monitoring and fault self-diagnosis. The system is accurate in temperature control, uniform in thermal field and high in automation, and is suitable for diversified curing needs.

The application provides a baking device based on fuzzy PID control and a baking method thereof, which comprises a tunnel furnace, a conveying mechanism and a heating mechanism. The tunnel furnace comprises an outer furnace body and an inner furnace body. The outer furnace body is arranged around the outer periphery of the inner furnace body. One side of the inner furnace body is provided with a feeding port, and the other side of the inner furnace body is provided with a discharging port. The conveying mechanism is arranged between the feeding port and the discharging port of the inner furnace body. The heating mechanism is arranged in the inner furnace body and below the conveying mechanism. The inner furnace body is sequentially provided with a heating first zone, a heating second zone and a heating third zone along the feeding direction. The application can guide the heat energy of the heating second zone of the tunnel furnace to the heating first zone and the heating third zone through a bidirectional centrifugal fan. Because the air in the heating second zone flows to the heating first zone and the heating third zone, the pressure of the heating second zone is reduced. Under the action of the pressure difference, the air in the heating first zone and the heating third zone flows to the heating second zone, thereby forming a dynamic circulation loop.

The application discloses a model parameter identification method suitable for fuzzy PID control, and comprises the following steps: step (1), a first-order inertia plus pure lag FOPDT model suitable for adapting dynamic characteristics of a temperature control system is established; step (2), inverse Laplace transformation is carried out on a transfer function of the FOPDT model, a complex frequency domain model is converted into a time domain model, and a noise term e(t) is introduced in combination with interference factors existing in actual operation of the temperature control system to modify the time domain model. The application can realize accurate identification of dynamic characteristics of a controlled object in the temperature control system, provide a reliable model basis for adaptive setting of fuzzy PID control parameters, effectively solve the problem of poor parameter adaptability in traditional temperature control, guarantee high precision and long-term stability of the temperature control process, and can be widely applied to fields, such as industrial kilns, constant-temperature laboratories and semiconductor manufacturing, which have strict requirements on temperature control precision.

The application discloses a GIS mixed gas supplementing self-adaptive control method, system, equipment and medium, relates to the GIS supplementing control technical field, and realizes the deep perception and early warning of the gas state through the multi-source data fusion trend prediction, and changes the passive supplementing to the active prevention. The self-adaptive control method adopts the fuzzy PID control algorithm, finely adjusts the controller parameters according to the deviation, the deviation change rate and the equipment load current, utilizes the predicted gas leakage rate as a feedforward signal to compensate the leakage amount, finally makes the pressure stable at the dynamic pressure set point, and the gas proportion is always kept constant. The whole process is smooth and impact-free, can adapt to different leakage rates, environmental conditions and equipment load changes, and has high robustness. In addition, the self-adaptive control method also adopts the matching strategy based on the mass flow control, ensures that the pressure and proportion of the supplemented gas reach the optimal set values.

This invention provides a method for suppressing vibration during the opening and closing of isolating switches based on a fuzzy PID control algorithm. The method includes: constructing a physical model of a 500kV isolating switch and combining a fuzzy PID controller with the physical model to obtain a closed-loop control system; simulating the closed-loop control system based on the fuzzy PID control algorithm to obtain the simulated curves of the first rotation angle and the first angular velocity during the opening and closing of corresponding nodes; performing a first comparison between the simulated curve of the first rotation angle and the simulated curve of the initial rotation angle before control is applied, and a second comparison between the simulated curve of the first angular velocity and the simulated curve of the initial angular velocity before control is applied; continuously optimizing the control constraints during the simulation process until the results of the first and second comparisons meet the vibration suppression criteria. By setting up the fuzzy PID control algorithm, the stability of the isolating switch during the opening and closing process is improved, effectively suppressing the vibration of the isolating switch.

This invention belongs to the field of fluid control technology, specifically an automated control valve optimization method and system for fluid control. It includes a composite intelligent algorithm based on multivariate feedback information, employing a fusion of fuzzy PID control and model predictive control to dynamically adjust valve opening in real time. The composite intelligent algorithm constructs a system dynamic model using real-time collected multivariate data, combining the adaptability of fuzzy PID control to nonlinear elements and the decoupling capability of MPC for multivariate coupling to generate valve opening optimization commands. This invention uses a composite intelligent algorithm that integrates fuzzy PID control and model predictive control to collect real-time data such as flow rate and pressure to construct a system dynamic model. It uses fuzzy logic rules to adjust PID parameters online, compensating for system nonlinear errors, and uses the fuzzy PID output as the initial control quantity for MPC. Through a rolling optimization strategy, it decouples multivariate coupling relationships, adapting to changes in the fluid system and thereby improving the control accuracy of valve opening.

The application provides a field emission thruster extraction pole indium deposition cleaning temperature control method and system, relates to the technical field of space electric propulsion, and the method comprises the following steps: obtaining multi-source temperature data and electric field intensity data of a target area, the two kinds of data are processed through space-time fusion to generate target characteristic information reflecting the deposition state of indium material; based on the characteristic information, a fuzzy PID control algorithm is used to generate a power control signal, and then a heating unit is adjusted; the thermal impedance parameters are obtained by monitoring the electric heating characteristics, and then the heat flow distribution data are generated; then the state observer is used to analyze the heat flow data to predict the growth trend of indium deposition, obtain the deposition rate information, finally determine the cleaning control strategy according to the growth rate, and control the temperature change in the non-uniform heating mode, so that the indium material reaches the phase change state. The application improves the reliability and maintenance efficiency of the field emission thruster in long-term operation on the orbit.

The application provides a high-speed rail air tightness test device for simulating extreme air pressure changes, comprising an experiment cabin, a high-speed rail model arranged in the inner cavity of the experiment cabin, a fluid control module, a sensor group and a data processing module, wherein the fluid control module comprises a fuzzy PID controller, a variable frequency fan, an air charging channel and an air exhaust channel, which are electrically connected with the data processing module; one end of an air inlet pipeline of the air charging channel is in a cylindrical shape, and the other end of the air inlet pipeline is in a horn shape; the fuzzy PID controller is embedded with a fuzzy PID control algorithm to control the opening and closing of three pneumatic butterfly valves in real time. The application solves the problems of high cost, large turbulent disturbance and low simulation accuracy of the existing test platform, can accurately reproduce the air pressure environment of a large ramp and a long tunnel, and provides an efficient and low-cost test device and method for high-speed rail air tightness research and detection.

The embodiment of the application provides a PLC adaptive fuzzy PID power regulation method and system, which obtains the current error and error change rate of the system, combines a preset prediction gain coefficient and a scaling reference coefficient, performs predictive evaluation on the system disturbance intensity, dynamically decides and generates the effective domain boundary at the current time, based on which, further calculates the dynamic quantization factor of the error and error change rate, so that the fuzzy domain can be flexibly scaled in real time according to the current disturbance intensity. In this way, the problem that the large error signal is clipped and the information is lost in the traditional method is avoided, and the real amplitude information of the error can be accurately mapped to different positions in the effective domain even under severe fluctuations, providing accurate input for subsequent fuzzification and reasoning, and finally greatly enhancing the robustness and adaptability of the PLC adaptive fuzzy PID control system under complex and dynamic working conditions.