130 results about "Fuzzy pid controller" patented technology

The invention relates to the technical field of self-adaptive control, and discloses a self-adaptive temperature control method and system for an automobile heater, and the method comprises the steps: calculating the real-time temperature deviation and temperature change rate of an actuator according to multi-dimensional temperature data; thermal inertia dynamic analysis is carried out on the historical heating power and temperature response relation through a preset thermal inertia compensation function, and a thermal inertia index of the actuator is obtained; the real-time temperature deviation, the temperature change rate and the thermal inertia index are jointly input into a preset fuzzy PID controller, and a preliminary power adjustment instruction of an actuator is output; analyzing the temperature change trend of the actuator in the time period, and performing prospective correction on the initial power adjustment instruction based on the temperature change trend; and controlling an actuator to execute heating operation according to the temperature control instruction, collecting real-time feedback temperature after the heating operation is executed, and performing joint online correction on parameters of the thermal inertia compensation function and a prediction coefficient of the time sequence model according to the real-time feedback temperature. According to the invention, the accuracy of self-adaptive temperature control of the heater can be improved.

The invention discloses an offshore rocket recovery platform attitude stability control method based on particle swarm optimization, and the method comprises the following steps: S1, collecting and preprocessing multi-source attitude data, and generating a time series data set; s2, constructing a fuzzy PID controller, and setting nine three-axis control parameters; s3, performing global optimization by adopting an improved particle swarm algorithm, and outputting an initial parameter solution; s4, introducing a zebra optimization algorithm to carry out local refined optimization; s5, constructing a collaborative optimization architecture, and fusing and outputting optimal control parameters; s6, the optimal parameters are input into a controller, and a servo system is driven to adjust the three-axis attitude; s7, monitoring disturbance amplitude, dynamically triggering a zebra strategy and feeding back the zebra strategy to the particle swarm; and S8, evaluating the control performance, and feeding back iterative optimization if the control performance does not reach the standard. According to the method, the improved particle swarm optimization algorithm and the zebra optimization algorithm are fused, so that self-adaptive optimization and accurate attitude stable control of attitude control parameters of the offshore rocket recovery platform are realized.

The invention relates to the technical field of detection of toner cartridge powder, and discloses a method and a system for improving the uniformity of toner cartridge powder, which are used for continuously sampling powder flow to obtain a real-time weight distribution matrix. And when abnormal flow is detected, noise is eliminated, processed data are input into a fuzzy PID controller, and feeding parameters are dynamically adjusted. Meanwhile, powder is promoted to be uniformly dispersed in combination with a vibration frequency modulation technology, and a weight change trend is predicted by utilizing a long-short-term memory network algorithm. In addition, the method further comprises an anomaly detection and fault diagnosis mechanism, control parameters are continuously adjusted through closed-loop optimization, and finally a batch quality tracing system is established. By means of the method, the precision and consistency of toner cartridge powder conveying can be effectively improved, and intelligent control and quality management in the production process are achieved.

The invention provides an argon flow and micro-positive pressure linkage control method for an electroslag furnace. The method comprises the steps that the oxygen content and micro-positive pressure signals in the furnace are collected; the future furnace condition is predicted through the deep belief network model; optimizing initial parameters of the fuzzy PID controller in an off-line manner by using a particle swarm algorithm; on the basis of the real-time signal and the prediction result, adaptively adjusting controller parameters on line; and finally, an argon flow and dust removal valve opening control instruction is output in a linkage mode. According to the method, future furnace conditions are predicted through the deep belief network model, the control advancement is realized, and the problem of large lag of the system is solved; offline parameter optimization is carried out on the fuzzy PID controller through a particle swarm algorithm, and excellent initial performance adapting to different working conditions is provided for the fuzzy PID controller; the parameters of the controller are adaptively adjusted through online fuzzy reasoning, so that the system has robustness for coping with a dynamic process; finally, accurate and stable linkage control of the oxygen content in the furnace and the micro-positive pressure is achieved through linkage calculation of the argon flow and a dust removal valve opening instruction.

The invention discloses a permanent magnet synchronous motor parameter optimization method based on an improved moth flame particle swarm algorithm. In order to solve the problems that a traditional PID controller is difficult in parameter setting, poor in adaptability, insufficient in control precision and the like under complex working conditions, in the strategy, an MFPSO algorithm fuses a particle swarm optimization (PSO) algorithm and a moth flame optimization (MFO) algorithm. An adaptive inertia weight and Gaussian mutation strategy is introduced, so that particles can focus on local fine search, and the speed of convergence to an optimal solution is accelerated. The optimization capacity of the algorithm in different stages is enhanced by adopting a nonlinear acceleration coefficient, and local optimum is effectively avoided. A grouping evolution mechanism is designed to promote intra-group information sharing and inter-group competition, the population diversity is enriched, and the ability of the algorithm to jump out of a local optimal solution is further improved. The fuzzy PID controller based on the MFPSO algorithm optimization parameters can better meet the requirements of high-performance control of permanent magnet synchronous motors in different fields, and has application prospects and economic values.

The invention discloses an intelligent control method for precise grinding of a robot, and the method comprises the steps: obtaining the information of a to-be-ground workpiece and initial grinding parameters, and the initial grinding parameters comprise the load gravity of the tail end of the robot; generating a target contact force based on the to-be-ground workpiece information and the initial grinding parameters; constructing a gravity compensation model through preset coordinate parameters based on the load gravity at the tail end of the robot; in the grinding process, grinding force data are collected in real time; on the basis of the grinding force data, actual contact force is obtained through a gravity compensation model; comparing the actual contact force with the target contact force to generate a contact force deviation and a deviation change rate; based on the contact force deviation and the deviation change rate, a force-position hybrid control instruction is output through a fuzzy PID controller; and on the basis of the force-position hybrid control instruction, control adjustment parameters of the robot are generated. By forming a complete intelligent control closed loop, constant-force grinding can be achieved, and the problems of uneven surface roughness, excessive grinding and the like are effectively solved.

The invention discloses a new energy automobile electric compressor preheating structure and a control method, and relates to the technical field of new energy automobile heat management. The method comprises the three steps of heat source collaborative recovery, heat demand dynamic distribution and self-starting collaborative execution, specifically, motor stator waste heat and compression refrigerant waste heat are recovered through a heat conduction fin array and a spiral flow channel, and a shared heat conduction medium is guided in; based on a fuzzy PID controller, a heat flow guide valve is dynamically adjusted to distribute heat flow according to the lubricating oil way temperature, the suction superheat degree and the shell temperature; the generator is triggered to be rapidly heated in a locked-rotor mode at the ultralow temperature, the preheating requirement is pre-judged in combination with a heat attenuation model, the structure comprises a temperature sensor set, a three-way heat flow guide valve and an embedded MCU processor, efficient waste heat utilization is achieved, external energy consumption is not needed, preheating energy consumption is effectively reduced, and the low-temperature starting performance and operation stability of the compressor are remarkably improved.

The invention provides an AGV safety navigation and obstacle avoidance system based on an intelligent storage environment and a control method. The method comprises the following steps: acquiring three-dimensional point data of an intelligent storage environment through a laser radar, and generating a three-dimensional grid chart after processing and removing interference points; and acquiring the distance between the obstacle and the AGV by using a millimeter wave radar, integrating the distance with the three-dimensional grid diagram, updating the space relationship between the obstacle and the AGV by combining the position of the AGV, and outputting a dynamic environment state quantity with a timestamp. The dynamic environment state quantity is input into a reinforcement learning path planner, a goods shelf dense environment is recognized, and a collision-free steering angle and speed instruction is generated; and the steering flexibility and the braking force of the AGV are dynamically adjusted according to an instruction through a fuzzy PID controller, so that safe navigation and obstacle avoidance in a goods shelf dense environment are realized. According to the invention, the safe navigation and obstacle avoidance capabilities of the AGV in an environment with dense intelligent storage shelves are improved.

The invention relates to a distributed power supply-based grid-connected management optimization method and system, and the method comprises the following steps: S1, obtaining various distributed power supply operation data, power grid side operation parameters and external environment data, and carrying out the partitioning storage of the corresponding distributed power supply data; s2, predicting the output of each distributed power supply by using an output prediction model according to the real-time operation data and the external environment data of each distributed power supply; s3, predicting a load curve by combining load mode recognition and XGBoost regression on the basis of the power grid side operation parameters; s4, based on the output and load curve prediction result of each distributed power supply, adopting hierarchical multi-objective optimization scheduling to obtain an optimal scheduling scheme; and S5, adjusting the power of each distributed power supply based on a fuzzy PID controller according to the optimal scheduling scheme, and dynamically tracking a scheduling instruction. According to the invention, dynamic, optimized and safe management of grid-connected operation of the distributed power supply is realized.

The invention relates to the technical field of ventilation equipment control, and discloses a ventilation clerestory self-adaptive adjustment control method and system according to environment change, and the method comprises the steps: deploying a distributed environment sensor array in a ventilation clerestory and inside and outside the ventilation clerestory, collecting time sequence data and equipment operation state data, and obtaining initial data; an abnormal data cleaning algorithm based on wavelet transformation is adopted to process initial data collected by a sensor, an environment parameter coupling degree matrix is constructed, and the correlation degree between parameters in the data subjected to abnormal data cleaning is analyzed; extracting global features from the cleaned and analyzed initial data, and outputting an environment prediction result; making a decision in the continuous action space to generate an optimal control instruction set; a fuzzy PID controller is adopted to control the ventilation clerestory according to the control instruction, the running state and environmental parameters of the ventilation clerestory are monitored in real time, and when abnormity occurs, a safety protection mechanism is triggered immediately; efficient ventilation is achieved, and the indoor environment requirement is accurately met.

The invention discloses a data machine room liquid cooling and air cooling cooperative control system and method based on working condition self-adaption, and belongs to the technical field of data machine room heat dissipation control. At present, a data center of a data machine room faces severe challenges during high-density deployment: the traditional air cooling heat dissipation efficiency is low and the energy consumption is high, a liquid cooling system is high in deployment cost and complex in operation and maintenance although the heat dissipation effect is good, and meanwhile, an existing hybrid cooling system lacks an effective dynamic cooperation strategy. For this purpose, a sensing-decision-control three-layer closed-loop control framework is adopted, data are accurately collected in real time by means of an equipment state monitor, a heat sensor and an environment sensor, then intelligent analysis is conducted through a heat load prediction model and a fuzzy PID controller, and the working states of the liquid cooling subsystem and the air cooling subsystem are dynamically adjusted; and optimal balance between heat dissipation efficiency and energy consumption is achieved. The flexibility and energy efficiency of the data center heat dissipation system can be effectively improved, the operation and maintenance cost is greatly reduced, and the system is widely suitable for various high-density data center heat dissipation scenes.

The invention provides a compass steering control method for a vehicle driven by a hub motor, and belongs to the technical field of vehicle chassis control. Comprising the following steps that kinematics analysis of compass steering is carried out on a vehicle, expected rotating speed design is carried out, and an expected rotating speed curve and expected wheel speeds of all driving wheels are obtained; acquiring the actual slip rate of each driving wheel; on the basis of discretization state space representation of the dynamic model of the slip rate, a model prediction control method is adopted to design an objective function, and a predicted slip rate is obtained; a compass steering fuzzy PID controller is constructed, and a feedforward compensator is used for correcting the initial torque of the compass steering fuzzy PID controller; the deviation between the expected wheel speed and the actually measured wheel speed of each driving wheel and the deviation change rate serve as the input of the compass steering fuzzy PID controller, the parameter correction amount of the compass steering fuzzy PID controller is output, the PID control amount is further calculated according to the parameter correction amount, and the yaw stability of the vehicle in the compass steering process is guaranteed.

The invention relates to the technical field of automatic control, and discloses a large-scale organ uniform controllable cooling method and system based on deep reinforcement learning and fuzzy control. The method comprises the following steps: constructing a high-precision temperature sensor array, and collecting multi-point temperature data on the surface and in the organ in real time; based on a biological heat transfer theory inverse problem model, reconstructing an organ three-dimensional temperature field in real time; a depth deterministic strategy gradient algorithm is adopted to optimize a fuzzy proportional-integral-differential controller. The depth deterministic strategy gradient algorithm takes minimization of organ internal temperature gradient as a primary optimization target, and parameters of a fuzzy PID controller are adjusted on line through interactive learning of an intelligent agent and a cooling environment, so that the power and the rate of a cooling module are accurately controlled. By means of the method, intelligent prediction and dynamic optimization of the cooling process can be achieved, the temperature gradient in the organ is smaller than 10 K / cm while it is guaranteed that the cooling rate reaches 15 K / min, and the success rate of low-temperature preservation of the large-scale organ and the activity of the large-scale organ after resuscitation are improved.

The invention relates to the technical field of mass spectrometers, in particular to an intelligent function optimization method for an inductively coupled plasma mass spectrometer, which comprises the following steps: S1, multi-parameter real-time monitoring: acquiring plasma torch temperature, ion lens voltage and mass spectrum detector noise parameters in real time through an embedded sensor array; s2, intelligent control adjustment: dynamically adjusting the power of a radio frequency generator and the carrier gas flow by adopting a self-adaptive fuzzy PID controller; s3, stability prediction modeling: establishing a plasma stability prediction model based on a deep belief network DBN, and outputting optimization parameters according to real-time acquired data; s4, virtual verification optimization: simulating the influence of parameter adjustment on mass spectrum resolution through a digital twin system; through multi-sensor monitoring, self-adaptive control and digital twinborn verification, the analysis precision and stability are remarkably improved, the matrix interference problem is effectively solved through the intelligent pretreatment technology, reliable operation of equipment is guaranteed through a three-level emergency mechanism, and manual intervention and maintenance cost are greatly reduced.

The invention discloses a thermal power generating unit-flywheel energy storage interconnected power grid AGC control method, device and equipment, a medium and a product, and relates to the field of automatic power generation control. The method comprises the following steps: establishing a thermal power generating unit-flywheel energy storage interconnected regional power grid AGC control system; determining that a controller in the system comprises an outer loop fuzzy PID controller and an inner loop PD + 1 controller with a filter coefficient; establishing a fuzzy control rule table of PID control parameters in the outer loop fuzzy PID controller; optimizing parameters in an inner ring PD + 1 controller by adopting a particle swarm optimization algorithm to obtain optimal parameters; an ACE deviation signal is adjusted through an outer loop fuzzy PID controller by using a fuzzy control rule table, an outer loop control signal is obtained, and then an AGC adjustment instruction is obtained by using an inner loop PD + 1 controller with optimal parameters; and decomposing the AGC adjustment instruction into a thermal power generating unit control signal and a flywheel energy storage control signal, and responding. The frequency stabilization performance can be improved.

The control method comprises the steps that the rotating speed of a high-temperature-stage inverter compressor is mainly comprehensively determined by the high-temperature-stage exhaust temperature, the high-temperature-stage condenser temperature, the high-temperature-side outlet temperature of an intermediate heat exchanger and the high-temperature-stage return air temperature; through dynamic adjustment of the first fuzzy PID controller, the working condition and energy efficiency of the high-pressure side of the system are optimized; the rotating speed of the low-temperature-stage frequency conversion compressor is mainly comprehensively determined by the temperature in the box, the real-time rotating speed of the high-temperature-stage compressor, the low-temperature-stage exhaust temperature, the temperature of the intermediate heat exchanger and the low-temperature-stage return air temperature, and the rotating speed is dynamically adjusted through the second fuzzy PID controller so as to ensure that the temperature in the box is accurate and stable and is matched with the high-temperature-stage capacity in real time. Through cooperative control of the parameter groups, the problems of low efficiency and unstable operation caused by mismatching of two-stage capabilities of the cascade system are solved, and efficient, stable and reliable operation of the system under all working conditions is realized.

The application belongs to the technical field of agricultural machinery controller optimization, and more specifically relates to a method for optimizing motor control of a corn planter based on a chaotic sparrow search algorithm. The method improves the initialization population through a Logistic chaotic sequence, improves the quality of the initial solution, and enhances the global search ability of the algorithm. At the same time, the method of introducing Gaussian variation is used to strengthen the local search ability and improve the search precision. Chaotic disturbance is also used to help the algorithm jump out of the local optimum and continue to search for the global optimal solution. The optimized fuzzy PID controller adaptively adjusts the control parameters and rules to achieve more accurate and stable control. This improvement not only enables the corn planter to achieve smooth transition, reduce oscillation and impact during acceleration and deceleration, but also provides high-precision and stable motor control performance. By optimizing the parameters of the controller, CSSA can adaptively adapt to different working conditions and improve the performance of the corn planter in various practical applications.

The invention discloses an aircraft assembly modularized clamp and a control method. The clamp comprises a T-shaped sliding module, a supporting module is arranged at the upper end of the T-shaped sliding module, an arc-shaped sliding rail is arranged at the upper end of the supporting module, at least two moving modules are arranged on the sliding rail, and an arc-shaped rack is arranged on the inner side of the sliding rail in the length direction; a cylindrical gear meshed with the rack is arranged on the movement module, and the cylindrical gear is driven by a servo motor to roll on the rack so as to drive the movement module to slide on the sliding rail; a telescopic suction cup assembly is arranged at the upper end of the movement module and connected with a part through a suction cup at the end. The control method comprises steps S1 to S6. The system adapts to complex part curved surfaces, in order to improve the self-adaptive capacity of the system, a self-adaptive fuzzy PID controller is adopted for conducting real-time self-regulation and control on clamping force, deviation and the deviation rate serve as input variables, the dynamic characteristics of the system can be captured more comprehensively, and therefore the system is adjusted in time.

The invention discloses a double-closed-loop fuzzy PID control method for a variable-frequency speed regulation system of a coal mining machine. The method comprises the following steps: inputting an initial target rotating speed; generating a dynamic target rotating speed adaptive to the current working condition; the fuzzy PID controller adaptively adjusts PID parameters according to the speed error between the actual output speed of the motor and the dynamic target rotating speed, and the speed error is accurately converted into expected torque current; the PI current controller outputs a PWM control signal to the coal mining machine variable frequency transmission mechanism based on a current error between the actual torque current and the expected torque current of the motor; and the coal mining machine variable frequency transmission mechanism adjusts output voltage and frequency according to the PWM signal and drives the traction motor to adjust the rotating speed, so that the actual torque current of the motor accurately tracks the expected torque current, and the actual output speed of the motor is consistent with the dynamic target rotating speed. Therefore, rapid, low-overshoot and anti-saturation efficient operation of the coal mining machine under complex disturbance is realized.

The invention discloses a plasma activated water preparation system based on fuzzy PID (Proportion Integration Differentiation) adaptive temperature and power matching cooperative control. The plasma activated water preparation system comprises a human-computer interface, a main controller, a temperature / power sensor, a parallel double fuzzy PID controller, an intelligent coordinator, a power / temperature actuator and a plasma reaction machine, a temperature / power sensor sends a water body temperature Tfb and a discharge power Pfb to a main controller in real time, a temperature fuzzy PID controller and a power fuzzy PID controller output suggested powers Ut and Up, an intelligent coordinator outputs a final power instruction and a cooling control signal after arbitration according to a state machine that Tfb is smaller than or equal to Tset normal power, Tfb is larger than Tset and Pfb is larger than Pmin and power is preferentially reduced, and Tfb is larger than Tset and Pfb is smaller than or equal to Pmin and is forcibly cooled. And the power actuator continuously adjusts the discharge power according to the discharge power, and the temperature actuator implements auxiliary heat dissipation, so that the water temperature is accurately locked in the optimal active matter generation interval under the nonlinear and time-varying working conditions, the discharge intensity is ensured not to be lower than Pmin, and high-activity, high-energy-efficiency, continuous and stable plasma activated water preparation is realized.

The invention discloses a self-adaptive hybrid control grinding method and system for a steel rail welding seam. The method comprises the steps that a welding seam three-dimensional model is obtained through 3D vision, and material removal amount distribution is calculated; intelligent layered path planning is conducted based on the distribution, specifically, process threshold layering is set, section sampling is conducted to generate tool points, collision-free optimization is conducted, an initial polishing path is generated, and expected contact force is matched; in the polishing process, the contact force is collected in real time through a six-dimensional force sensor; feedforward control is carried out on the material removal amount based on visual prediction, feedback compensation is carried out through a fuzzy PID controller on the basis of force deviation fed back by a force sensor, motion parameters of the robot are dynamically adjusted, and double-layer self-adaptive control of visual intelligent planning and force control accurate execution is achieved. The system comprises corresponding modules. The problems that an existing method is poor in adaptability to geometric differences of the welding seams and lacks macroscopic planning are solved, and high-precision, high-efficiency and high-consistency automatic grinding of the steel rail welding seams is achieved.

The invention relates to the technical field of ship power systems, in particular to a ship power system energy efficiency optimization control method based on artificial intelligence, and the control method comprises the steps: S1, carrying out the real-time collection and preprocessing of multi-source data; s2, power system energy efficiency state modeling based on an LSTM-CNN fusion model; s3, solving energy efficiency optimization parameters based on an improved particle swarm algorithm; and S4, carrying out optimal control instruction execution and feedback adjustment based on fuzzy PID (Proportion Integration Differentiation). According to the ship power system energy efficiency optimization control method based on artificial intelligence, the convergence speed can be improved through precise modeling of the LSTM-CNN fusion model and dynamic optimization of the improved particle swarm optimization, compared with a traditional control method, the comprehensive energy consumption rate of the ship power system can be reduced, the fusion model can rapidly capture dynamic changes such as sea conditions and loads in real time, and the control method is suitable for large-scale popularization and application. And the fuzzy PID controller can realize overshoot-free adjustment, so that the operation and maintenance cost can be effectively reduced, and the equipment failure rate is reduced.

The invention discloses a compliance control method in an amphibious environment and a flexible robot, and relates to the field of amphibious robot control, the bottom of each leg of the amphibious robot is provided with a corresponding elastic element, and the amphibious robot designs a multi-sensor integration algorithm switching working mode based on an inertial measurement unit IMU and a depth gauge. And corresponding passive compliance control algorithms are selected in different working modes to guide the motion posture of the robot, and meanwhile, the poses of motors on the legs are controlled based on the output of the feed-forward fuzzy PID controller, so that the position of the robot is controlled by controlling the rotation angles of the motors. The invention provides a compliance control method in an amphibious environment and a flexible robot, which are suitable for an amphibious robot of which the bottom of each leg is provided with a corresponding elastic element, and the adaptability and reactivity in a complex environment are enhanced by designing a corresponding kinetic model and a control strategy.

The present disclosure relates to the field of rail transit technology, and particularly relates to a heavy-haul train group cooperative control method and device, equipment and medium. The present disclosure determines the safety of the heavy-haul train cooperative control environment according to the segmented data of the laser radar and the speed and distance of the detected object to the solid-state laser radar. When the solid-state laser radar detects that the cooperative control environment is safe, that is, within the cooperative control distance range, in addition to the heavy-haul train in front, there is no other object, the solid-state laser radar will obtain the actual distance and speed of the heavy-haul train in front for cooperative control, and use the distance error and speed of the heavy-haul train as input and the speed regulation gain of the heavy-haul train as output to establish a heavy-haul train cooperative control regulation system, and optimize the minimum error adaptive fuzzy PID controller based on the improved particle swarm optimization algorithm, thereby shortening the distance of the heavy-haul train cooperative control, improving the railway transportation capacity, and effectively solving the shortcomings of the traditional control system in the dynamic environment.

The present application relates to the technical field of control system, and especially relates to a vacuum medium frequency furnace operation regulation and control method and system for gas atomization powder production. The method comprises the following steps: acquiring the furnace body inclination angle, the actual residual mass of the metal liquid in the crucible and the actual flow rate of the crucible in the gas atomization pouring process of the vacuum medium frequency furnace in real time; determining the crucible erosion index at the current time; determining the fluid sensitivity factor at the current time; determining the dynamic scaling coefficient for adjusting the fuzzy PID controller output gain at the current time; and performing the operation regulation and control of the vacuum medium frequency furnace based on the improved fuzzy PID controller. By constructing the crucible erosion index and the fluid sensitivity factor, the present application dynamically optimizes the fuzzy PID control gain, matches the crucible state change in real time, solves the problem of poor adaptability of the fixed rule, improves the regulation and control precision and stability of the vacuum medium frequency furnace, and ensures the stable operation of the gas atomization powder production process.

The invention provides an electric locomotive body stabilizing system, and belongs to the technical field of electric locomotives, the electric locomotive body stabilizing system comprises a sensor unit, a control unit and an execution unit, the sensor unit is responsible for monitoring and acquiring various motion parameters of a locomotive in real time, and providing data support for the control unit; and the control unit is responsible for receiving data acquired by the sensor unit, processing and analyzing the data, and then sending a control instruction to adjust the action of the execution unit. According to the electric locomotive body stabilizing system, snaking motion and side rolling vibration of a locomotive body are effectively restrained through the active transverse shock absorber and the anti-snaking damper, the transverse stability is improved, meanwhile, the relative displacement and wheel-rail contact force of the bogie and the locomotive body are monitored in real time through the displacement sensor and the wheel-rail force sensor, and the stability of the locomotive body is improved. The damping coefficient of the shock absorber is dynamically adjusted through a self-adaptive fuzzy PID controller, the secondary suspension rigidity is dynamically adjusted in combination with an air spring pressure adjusting system, and vertical impact is relieved.

The invention relates to the technical field of control systems, in particular to a vacuum intermediate frequency furnace operation regulation and control method and system for gas atomization powder preparation. The method comprises the steps that the furnace body inclination angle of the vacuum intermediate frequency furnace in the gas atomization pouring process, the actual residual mass of molten metal in a crucible and the actual flow rate of the crucible are obtained in real time; determining a crucible erosion index at the current moment; determining a fluid sensitivity factor at the current moment; determining a dynamic scaling coefficient used for adjusting the output gain of the fuzzy PID controller at the current moment; and performing operation regulation and control on the vacuum intermediate frequency furnace based on the improved fuzzy PID controller. By constructing the crucible erosion index and the fluid sensitivity factor, dynamically optimizing the fuzzy PID control gain and matching the crucible state change in real time, the problem of poor adaptability of a fixed rule is solved, the regulation and control precision and stability of the vacuum intermediate frequency furnace are improved, and stable operation of the gas atomization powder preparation process is guaranteed.

The invention discloses a multi-AGV formation keeping control method based on IPSO-fuzzy PID. The multi-AGV formation keeping control method is characterized by comprising the following steps: (1) establishing a dynamic self-adaptive multi-AGV pilot following formation control model; (2) constructing a fuzzy PID controller, setting a fuzzy domain of input and output variables, a membership function and a fuzzy control rule, and obtaining the correction of PID parameters through fuzzy reasoning and defuzzification; (3) constructing an improved particle swarm optimization algorithm with dynamic balance of global search and local optimization, improving the inertia weight and learning factor of a particle swarm, and setting a fitness function; and (4) optimizing the input quantization factor and the output scaling factor of the fuzzy PID controller by using an IPSO algorithm, assigning optimized parameters to the controller, and adjusting the linear velocity and the angular velocity of the following AGV. The formation keeping precision, the response speed and the anti-interference capability can be improved.

The invention discloses a liquid cooling system for an electromagnetic shielding cabinet and a control method. The liquid cooling system integrates a power sensor, a temperature sensor array, a fuzzy PID controller, a frequency conversion circulating pump, an electric proportional valve, a heat exchanger, a liquid cooling distribution unit and an Internet of Things monitoring module. The control method comprises the following steps: performing multi-source data synchronous acquisition and state fusion analysis; carrying out thermal load prediction and multi-time scale prediction by using a physical-based neural network; a fuzzy PID controller is constructed into a differentiable neural network structure, and fuzzy rules and membership function parameters are optimized online by using a depth deterministic strategy gradient algorithm; a digital twinborn model-based actuator dynamic compensation and multi-modal health monitoring fusion is introduced, and early fault early warning and local training are carried out. The heat dissipation efficiency and the operation stability of the liquid cooling equipment are ensured, and the heat dissipation performance and the temperature uniformity of the liquid cooling system are improved.

This invention relates to the field of chemical safety and intelligent control technology, particularly to an intelligent temperature control method and system for explosion-proof hydrogen peroxide storage tanks based on multimodal monitoring. The method involves collecting multidimensional data such as temperature, pressure, and H2O2 concentration using a sensor array. After denoising via Kalman filtering and wavelet transform, an improved WMDS-DBSCAN algorithm is used to identify local anomalies. The method combines the unsteady-state heat conduction equation and the RF-ADI algorithm to predict hotspot diffusion trends. An adaptive monitoring grid is constructed using weighted Delaunay triangulation, and a risk area probability distribution is output through a SAM-LSTM-CNN hybrid neural network. Temperature control is achieved by adjusting the nozzle opening using a dynamically limited fuzzy PID controller. This invention integrates multimodal data and intelligent algorithms to achieve accurate anomaly detection, risk prediction, and adaptive temperature control, forming a closed-loop optimization mechanism that effectively improves the operational safety and reliability of hydrogen peroxide storage tanks.