2086 results about "PID controller" patented technology

The invention relates to the technical field of marine monitoring, in particular to a marine target visual servo tracking method, which comprises the steps of multi-modal sensor fusion, a self-adaptive visual tracking algorithm, a servo control and visual collaboration mechanism and a shielding processing and target re-identification strategy. The problem that tracking is unstable under the conditions of illumination change, ship body shaking, target shielding and the like in a traditional method is solved. The IMU, the GNSS and the visual data are fused through extended Kalman filtering, ship body shaking is compensated, and the target state estimation precision is improved; the improved D-Fi ne target detection model is combined with an online feature updating mechanism to dynamically adapt to the appearance change of the target; the prediction and correction control strategy and the double-closed-loop PI D controller cooperate to adjust the camera holder, and the tracking delay is reduced; the multi-clue shielding detection and space-time joint feature matching technology ensures accurate re-identification of the target after shielding is removed. The real-time performance and robustness of the tracking system on an embedded platform are improved, and an efficient and stable target tracking solution is provided.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention relates to the technical field of temperature equalization control, and discloses a temperature equalization control method and device for a liquid cooling over-charging system. The method comprises the following steps: carrying out grid division on the liquid cooling overcharge system, inputting thermophysical parameters, and obtaining a temperature field calculation matrix through finite difference time domain analysis; establishing a two-layer distributed control network of a station-level control layer and a pile-level control layer based on the temperature field calculation matrix; acquiring real-time temperature data of the two layers of distributed control networks, and performing temperature field mapping to obtain temperature field distribution data; performing linear prediction and nonlinear prediction calculation on the temperature field distribution data to obtain a temperature change trend and a temperature extreme point; the target temperature is calculated according to the temperature change trend and the temperature extreme point, the cascade PID controller is adopted to adjust the rotating speed of the cooling liquid pump, and the temperature balance control parameters are obtained, station-level and pile-level cooperative control is achieved, cooling resources among the multiple charging piles are reasonably distributed, and the overall cooling efficiency of the system is improved.

The invention discloses a DC-DC power supply dynamic response optimization system based on digital PID, which relates to the technical field of DC-DC power supply dynamic response optimization, and comprises a voltage and current sampling module, a reference signal generation module, an output signal generation module, a digital signal generation module, a control module and an output signal generation module, the digital PID controller calculates control output according to errors, the PWM signal generation module generates corresponding PWM signals according to the control output, the power switch driving module amplifies the signals to drive a switch tube, the load monitoring module monitors load changes in real time, output of the load monitoring module can be fed back to other modules, and power supply dynamic response optimization is achieved. According to the invention, the dynamic response performance of the DC-DC power supply is remarkably improved, the output fluctuation is reduced, the recovery time is shortened, the power supply efficiency is improved, the stability is guaranteed by adopting multiple anti-interference design, peripheral elements are reduced, miniaturization is realized, the cost is reduced, fault diagnosis is realized, and the service life is prolonged.

The invention discloses a high-precision coating die head coating thickness adjusting method based on intelligent feedback control. The method comprises the following steps: S1, constructing a data set and transmitting the data set to a controller module; s2, calculating an error and inputting the error to a fuzzy controller for preliminary control, and generating a preliminary control instruction; s3, inputting the generated preliminary control instruction into a PID controller; s4, a model prediction controller is used for predicting the change trend of the coating thickness in the future, and an adjustment signal is sent out in advance; s5, dynamically adjusting parameters of a controller module based on a dual adaptive neural network control mechanism; s6, continuously optimizing the control strategy through a'heuristic-feedback 'mechanism; s7, the control signal generated by the controller module is transmitted to the execution mechanism module; and S8, recording and storing all data in the coating process through a data processing and self-learning module, and continuously optimizing the control strategy. According to the method, fuzzy control, PID and the dual adaptive neural network are combined, and accurate control over the coating thickness is achieved.

The invention relates to the technical field of intelligent dimming control, and discloses an intelligent dimming control method and system based on an LED driver, and the method comprises the steps: carrying out the current and light intensity data collection of the LED driver under different dimming depth values, and obtaining a response characteristic data set; inputting the response characteristic data set into a convolutional block attention module and convolutional neural network combined architecture for training to obtain an LED response characteristic prediction model; the ratio of the light intensity change rate to the current change rate is calculated, and a piecewise linear dimming sensitivity index is obtained through piecewise linear function fitting; constructing a first-order inertia controller and a brightness feedback loop, and generating a brightness self-compensation control strategy; according to the LED driver, the brightness change response is predicted, and the PWM dimming control signal is generated according to the trajectory smoothing algorithm and the PID controller, so that the adaptive capacity of the LED driver to external interference and parameter change is enhanced, and the stability and reliability of the LED driver are improved.

The invention discloses a constant-temperature and constant-humidity test box temperature control system based on a multistage PID algorithm and a control method thereof. The constant-temperature and constant-humidity test box temperature control system comprises an environmental parameter detection module, an in-box parameter detection module and a multistage PID controller, wherein the environmental parameter detection module is used for collecting temperature, humidity and air pressure data outside a test box in real time; the in-box parameter detection module is used for collecting multi-dimensional environmental parameters inside the test box; when the failure of any sensor is detected, starting a fault self-diagnosis module, replacing the data of the failure sensor with a data interpolation method of a side sensor, and recording the deviation compensation amount at the same time. A multi-stage PID control architecture is adopted, and feedforward compensation, parameter adaptive optimization and fault redundancy strategies are combined, so that high-precision and anti-interference control of the temperature and humidity of the test box is realized.

The invention relates to the technical field of sewage treatment, in particular to a liftable aeration device and a sewage treatment system.The system comprises a data acquisition module for acquiring dissolved oxygen concentration, gas flow and air pressure at multiple moments before each moment in the sewage treatment process and recording the dissolved oxygen concentration, the gas flow and the air pressure as historical data sets of each moment; the feature analysis module is used for determining the gas supply stability at each moment; determining a first trend degree, a second trend degree, a first collaborative factor and a second collaborative factor of each fluctuation moment in the historical data set of each moment; determining a relative collaboration degree and a forward feedback index at each moment; the aeration control module is used for determining a proportionality coefficient and an integral parameter of a PID controller at each moment; and in combination with a PID (Proportion Integration Differentiation) controller, obtaining a gas flow control value at each moment, and controlling the concentration of the dissolved oxygen in the sewage. The gas flow can be more accurately adjusted, the control accuracy of the concentration of dissolved oxygen in sewage is improved, and the sewage treatment efficiency is improved.

The invention discloses a fishery culture pond temperature control optimization method, and belongs to the technical field of PID control optimization, and the method comprises the specific steps: S1, constructing a fishery culture pond temperature control system; s2, improving a dandelion optimization algorithm; s21, using a multi-elite opposite learning strategy to improve a mathematical model of a decline stage of the dandelion optimization algorithm; s22, improving a mathematical model of a landing stage of the dandelion optimization algorithm by using a self-adaptive segmented search strategy; s3, optimizing PID controller parameters in the temperature control system of the fishery culture pond by using an improved dandelion optimization algorithm; s4, applying the optimal three parameters Kp, Ki and Kd obtained in the S3 to a PID controller of a temperature control system of the fishery culture pond, and optimizing the temperature control of the culture pond; the fishery culture pond temperature control system is optimized through an improved dandelion optimization algorithm, and the temperature control performance and the control precision of the system are integrally improved.

The invention discloses an electric heating cooker uniform temperature control method and system for gradient temperature difference regulation and control, and relates to the technical field of temperature control, the method comprises the following steps: dividing an internal heating area of a target electric heating cooker, arranging temperature sensors, and acquiring meal temperature data of N subareas in real time through a subarea temperature sensor array; constructing a gradient temperature difference model based on meal heat conduction information and food material characteristic information, and performing deviation calculation on the meal temperature data of the N sub-regions based on the gradient temperature difference model to obtain temperature deviation data of the N sub-regions; and a PID controller is adopted to perform heating power analysis on the temperature deviation data of the N sub-regions, and the heating power adjustment amount of the N sub-regions is determined to control the target electric heating cooker to perform uniform-temperature heating control. The technical effects of realizing uniform temperature control of the electric heating cooker, effectively solving the problem of local overheating or half-cooking caused by a traditional heating mode, ensuring uniform heating of meals and improving the heating quality are achieved.

The invention relates to the technical field of irrigation control, in particular to a water and fertilizer integrated irrigation control method based on a wheat growth cycle, which comprises the following steps: collecting soil humidity and wheat growth state data, and screening key soil and biological parameters influencing wheat growth by applying a random forest algorithm; and predicting the requirements of the wheat on water and nutrition in the differential growth stage according to the key parameters. According to the method, the water and nutrition requirements of wheat in different growth stages are accurately predicted by adopting a random forest algorithm, resource allocation is directly carried out according to the actual needs of plants, the problems of resource waste and insufficient supply of a traditional irrigation system are effectively avoided, and a dynamic adjustment strategy is based on current soil and plant states and also based on a dynamic adjustment strategy. And a water and fertilizer supply plan is optimized by using historical and predicted data, the capability of responding to environmental changes is enhanced, irrigation and fertilization parameters are finely adjusted by combining real-time feedback circulation with a PID controller, and the accuracy and effectiveness of each time of irrigation and fertilization are further ensured.

The dynamic hot runner temperature control method suitable for the mold comprises the steps that S1, a temperature sensor is installed in a mold hot runner to collect flow characteristics, heat distribution and temperature original data in the material solidification process, and the temperature original data are transmitted to a temperature control system; s2, the required power adjustment amount Pc is calculated through a PID controller in the temperature control system according to the deviation value delta T between the actual temperature Ta and the set target temperature Ts, and the power adjustment amount Pc is sent to a heater for adjustment; s3, constructing a temperature prediction model M, and predicting the temperature change trend of each section of the mold hot runner through the temperature prediction model M; and S4, a multi-objective optimization function Ft is created, an optimal combination solution St is searched through a particle swarm optimization algorithm PSO, and the optimal set value combination of the hot runner temperature is maintained according to an optimization objective. According to the invention, the energy consumption is reduced while uniform temperature distribution is ensured, the temperature control precision and the response speed are obviously improved, and the requirement of human intervention is reduced.

The invention discloses an inspection robot and an inspection method thereof, and relates to the technical field of robots, and the inspection robot comprises a remote control center which is in communication connection with an environment perception and mapping module, an intelligent movement and obstacle avoidance control module, and an intelligent analysis and user interaction module; the environment sensing and mapping module integrates a laser radar, an ultrasonic sensor and an RGB-D camera, and realizes multi-dimensional environment data acquisition; the intelligent movement and obstacle avoidance control module adopts an SLAM algorithm to position and map, adopts an RRT algorithm to plan a path, adopts a CNN algorithm to recognize an obstacle, and adjusts movement through a PID controller and a kinematic model to realize intelligent obstacle avoidance; the intelligent analysis and user interaction module reduces delay through edge calculation, provides real-time data processing and a graphical user interface and displays the state and operation guide of the robot. On the whole, by integrating various sensors and advanced algorithms, efficient and intelligent inspection operation is realized, and the inspection efficiency and safety are improved.

The invention relates to the technical field of control systems, in particular to a sorbitol-based production parameter intelligent regulation and control system, which comprises a real-time feedback regulation and control module for continuously acquiring data, calculating real-time deviation and generating real-time deviation data based on real-time monitoring of temperature, pressure and concentration in a current reaction kettle. According to the invention, through real-time monitoring and dynamic calculation of parameters in the production process, variable deviations such as temperature, pressure and concentration are obtained and adjusted, and quick response processing of multi-dimensional production data is realized. By optimizing a dynamic adjustment strategy of PID controller parameters, proportion, integral and differential parameters are optimized according to real-time feedback, and the problem of adjustment lag caused by fixed parameters is solved. The current reaction condition and the raw material consumption rate are comprehensively considered in dynamic adjustment of the feeding flow, and meanwhile, the flow value matched with the current reaction demand is calculated and obtained, so that the condition that feeding and demand are disjointed in traditional regulation and control is avoided.

The invention relates to the technical field of intelligent control, and particularly discloses a linkage control method for energy-saving ventilation equipment, which comprises the following steps of: acquiring indoor and outdoor temperature, indoor carbon dioxide concentration and personnel occupancy state data in real time; according to the method, deep collaborative analysis is carried out on the indoor personnel occupancy state and the deviation between the carbon dioxide concentration and a target set value, so that joint characterization of the current indoor fresh air volume regulation and control requirement is achieved, control parameters of a fresh air volume PI D controller are dynamically optimized accordingly, and a basic fresh air volume regulation instruction is calculated. On the basis, thermal disturbance introduced by fresh air is further quantified based on the outdoor and indoor temperature difference, and the thermal disturbance serves as a feedforward signal to be compensated to an air conditioner load adjusting loop, so that dynamic adjustment of the air conditioner load is achieved. In this way, cooperative control over ventilation and air conditioning systems can be achieved, the fresh air-air conditioning equipment energy consumption coupling conflict in traditional control is restrained, and therefore the building energy consumption is reduced while the indoor environment quality is guaranteed.

The invention discloses an efficient refrigerating machine room air conditioner energy-saving intelligent control system and method, and relates to the field of air conditioner intelligent control. The method comprises the steps that air conditioner operation parameters, machine room equipment operation data and machine room environment data are collected and preprocessed; building a deep neural network by using a deep Q-learning reinforcement learning algorithm to obtain a thermal load demand of the machine room; introducing a nonlinear dynamic compensation function to calculate an air-conditioner dynamic energy efficiency coefficient and air-conditioner refrigeration power, and calculating the rotating speed of an air-conditioner compressor by referring to air-conditioner operation parameters; a PID (Proportion Integration Differentiation) controller is used for carrying out primary frequency conversion on a machine room air conditioner, and a multi-stage adjusting mechanism is used for correcting a primary frequency conversion result in real time according to machine room equipment operation data, machine room environment data change and frequency conversion conditions. According to the air conditioner energy-saving control method, efficient air conditioner energy-saving control is achieved by combining the deep Q-learning reinforcement learning algorithm, the nonlinear dynamic compensation function and the PID controller.

The invention discloses a three-temperature test method and device for a semiconductor chip, and relates to the technical field of semiconductor chip testing, and the method comprises the steps: inputting an initialization parameter set into an impedance temperature mapping model, carrying out the phase angle analysis and multi-band feature fusion through a broadband impedance spectrum analysis method, obtaining a chip junction temperature instantaneous value, and carrying out the measurement of the junction temperature instantaneous value. A PID controller is utilized to dynamically compensate a junction temperature instantaneous value of a chip, a fuzzy rule base inference engine is utilized to map the junction temperature instantaneous value into an execution instruction set, the execution instruction set is converted into a bidirectional voltage signal through pulse width modulation, a semiconductor chilling plate is driven to perform bidirectional temperature rise and drop control in a three-temperature environment, and real-time temperature change test data is synchronously acquired. According to the method, the junction temperature instantaneous value of the chip can be more accurately obtained by combining broadband impedance spectroscopy analysis with a multi-band characteristic fusion technology, and meanwhile, the PID controller is combined with the fuzzy rule base inference engine, so that a response can be quickly made under the condition that the temperature is quickly changed, and the response capability is greatly improved.

The invention provides an unmanned ship path tracking control method based on lightweight self-adaptive sight guidance. The method comprises a self-adaptive sight guidance algorithm, a foresight distance is adjusted in real time according to the current position and path deviation of an unmanned ship, ideal course information is obtained, and the path convergence speed and algorithm calculation efficiency are improved. And in the virtual simulation platform, marine environment information is combined, and unmanned ship dynamic model parameters are obtained through experiments. And constructing an objective function taking the path tracking error and the rudder angle change as evaluation indexes, and optimizing PID controller parameters by adopting an improved sparrow search algorithm. According to the algorithm, a dynamic additive updating strategy is introduced, so that the global search capability and the convergence stability are improved, and the optimal PID parameter is ensured to be obtained. The method has the advantages of being high in response speed, small in overshoot and high in path tracking precision in the complex marine environment, the real-time control performance and the environment adaptability of the unmanned ship can be remarkably improved, and the method is suitable for the unmanned ship path tracking control requirements in various task scenes.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

Disclosed are a method and system for controlling a robotic arm pressing force based on a pulse wave signal. The method comprises: acquiring a pulse wave signal of a target; acquiring an interaction force between an end of a robotic arm and the target, and determining an expected pressing force according to a mapping relation between a pressing effect and the pulse wave signal; establishing an admittance control model to convert the expected pressing force into an expected trajectory of the robotic arm; and determining, on the basis of an error between an actual trajectory and the expected trajectory of the robotic arm, a control quantity of the subsequent movement of the robotic arm by using a PID controller, so as to achieve the tracking of the expected trajectory. The present invention achieves a low-impedance and compliant interaction effect when the target of pressing is moving.

The invention discloses a fuel cell output voltage control method and system based on fuzzy neural network PID, and the method comprises the steps: building an air-cooled fuel cell voltage model based on an MATLAB / Simulink simulation platform, and obtaining the temperature changes of an air-cooled fuel cell under different working conditions; establishing an air-cooled fuel cell test bench, and obtaining a polarization curve of output voltage changing along with current; according to experimental data, calibrating the semi-empirical equation by adopting a nonlinear least square method, and comparing experimental results to verify the accuracy of the model; the input hydrogen flow is changed through the PID controller, so that stable control of the output voltage of the galvanic pile is realized, and the power generation performance of the battery is improved; a fuzzy neural network is adopted to optimize a PID controller, control parameters of PID are adjusted in a self-adaptive mode according to the error between expected voltage and actual voltage and the error change rate, and therefore a better temperature control effect is achieved. The method has the advantages of high robustness, high response speed and the like, and ensures that the voltage output of the fuel cell stack is maintained at a fixed preset value.

The invention discloses a closed-loop optimization method for cerebral apoplexy electrical stimulation treatment, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing a cerebral apoplexy electrical stimulation control model; step 2, improving a sled dog optimization algorithm, wherein an improved strategy is as follows: D1, generating an initial population by using a chaotic mapping optimization strategy fused with a Halton sequence; d2, using a dynamic mixed collaborative search strategy to improve a mathematical model in an obstacle avoidance stage; d3, using an elite Levy dynamic navigation strategy to improve the mathematical model of the lost direction stage; 3, setting parameters of a PID controller module in the cerebral apoplexy electrical stimulation control model by using an improved sled dog optimization algorithm, and obtaining optimal Kp, Ki and Kd parameters through optimization; and step 4, inputting the obtained optimal Kp, Ki and Kd parameters into a cerebral apoplexy electrical stimulation control model, and optimizing a control effect.

The invention discloses a chip-level GaAs etching temperature control method, which comprises the following steps: detecting the upper surface temperature of a GaAs wafer in the etching process in real time to obtain first temperature data; and according to a partition structure in the electrostatic chuck and the first temperature data, determining a to-be-adjusted electrostatic chuck partition and determining a second temperature of the upper surface of the carried GaAs wafer corresponding to the to-be-adjusted electrostatic chuck partition. And on the basis of the second temperature data and the target temperature, power adjustment is carried out on the heating unit of the to-be-adjusted electrostatic chuck partition through the partition temperature PID control module, and the second temperature data is adjusted to the target temperature. According to the invention, the power of the local heating unit is adjusted according to the wafer surface temperature data obtained in real time by using the partition temperature PID control module, and the temperature of each area is ensured to be uniform and stable. The control coefficient of the PID controller is optimized in an auxiliary mode through the neural network, and the self-adaptive capacity and the control precision of the system are improved.

The invention provides an automatic generation control (AGC) collaborative optimization method based on time sequence deep reinforcement learning PID (Proportion Integration Differentiation) control. Comprising the following steps: establishing a three-area multi-source interconnected power system with participation of hydroelectric power, thermal power, wind power, photovoltaic power and energy storage, and bringing wind and light storage resources into an automatic power generation control loop. The invention provides a novel AGC adaptive PID controller based on time sequence deep reinforcement learning by utilizing the time sequence perception capability of a long short-term memory neural network and combining a double-delay depth deterministic strategy gradient algorithm, and provides an efficient time sequence deep reinforcement learning solution for intelligent power grid frequency control.

The invention relates to a deformation online measurement and control method for multi-robot collaborative assembly. The method comprises the steps that multi-view surface images of workpieces in the assembly process are collected in real time through distributed robots and cameras arranged at the tail ends of the distributed robots; each view angle surface image is input into a deep learning model trained based on a digital image related technology, the optimal pixel displacement corresponding to each view angle surface image is output, and the optimal pixel displacement corresponding to each view angle surface image is converted into a spatial displacement label; the deep learning model takes an encoder-decoder as a trunk network; fusing the spatial displacement labels corresponding to the view angle surface images to obtain a fused displacement field; based on the fusion displacement field and the nominal path planning point, the tail end pose of the distributed robot is determined; the assembly error is calculated based on the reference target positioning and the tail end pose, and the PID controller adjusts the joint space of the distributed robot based on the assembly error. According to the method, the calculation overhead is remarkably reduced, and the measurement precision and robustness are improved.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The invention discloses a predictive optimization control method and system for joint debugging of bed temperature and bed pressure of a circulating fluidized bed boiler, and relates to the field of industrial automatic control, and the method comprises the steps: screening out key operation parameters affecting the bed temperature and the bed pressure from a historical data set according to the operation condition and the actual operation demand of the boiler, determining a modeling feature set influencing the bed temperature and the bed pressure; based on the boiler bed temperature and bed pressure prediction model, obtaining a boiler bed temperature and bed pressure prediction value under each load; determining an optimization target and constraint conditions to obtain a boiler bed temperature and a bed pressure target value under each load; according to the boiler bed temperature and bed pressure prediction values and the corresponding target values under each load, the error and the error change rate of each prediction value and the corresponding target value are determined; therefore, the parameter adjustment amount of the PID controller can be determined. And further determining the control quantity output of the rotating speed of the slag cooler or the frequency of the primary fan. According to the invention, the response capability of the system to emergencies can be improved, and the flexibility and accuracy of regulation and control are enhanced.

The invention discloses a blowout preventer remote automatic grease injection system based on PID control and a control method, and relates to the technical field of grease injection, and the control method comprises the following steps: an initialization stage: automatically matching the specification of a choke tube according to the diameter of a cable, and preheating sealing grease to a set temperature; in the dynamic control stage, wellhead pressure and grease injection pressure are monitored in real time, pressure errors are calculated, the flow of a grease injection pump is adjusted according to a PID algorithm, and when the cable lowering speed suddenly changes, pressure fluctuation is inhibited in advance through a differential link; and according to the safety redundancy mechanism, when the main PID controller fails, the standby PID controller is automatically switched, and emergency locking is triggered when the wellhead pressure exceeds the limit. The flow of a grease injection pump is adjusted in real time through an incremental PID algorithm and a self-adaptive adjustment method, the grease injection amount is accurately controlled, pressure fluctuation is reduced, sudden change of the cable lowering speed is predicted through a differential link, the response precision is improved, stable operation of the system is ensured through a redundant controller and an emergency locking function, PID output is corrected through a dynamic temperature-viscosity model, and the reliability of the system is improved. The system service life is prolonged.

The invention relates to a four-axis unmanned aerial vehicle PID parameter setting method based on a whale optimization algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method aims at solving the problems that in the traditional PID parameter setting process, experience is relied on, time is consumed, and optimal parameters are difficult to obtain. According to the method, the whale optimization algorithm is introduced, and the characteristics of high global search capability and high convergence speed are utilized, so that the parameters of the four-axis unmanned aerial vehicle PID controller are automatically optimized. The method comprises the steps that firstly, a kinetic model of the four-axis unmanned aerial vehicle is established, a control target of a PID controller is determined, PID parameters serve as optimization variables, and a fitness function is defined to evaluate control performance; then, performing global search in a parameter space by using a whale optimization algorithm, and gradually optimizing PID parameters by simulating a whale predation behavior; the optimized PID parameters are applied to a flight control system of the four-axis unmanned aerial vehicle, and stable attitude, height and position control is achieved. The method has the advantages that PID parameters are automatically set through the whale optimization algorithm, the low efficiency and subjectivity of a traditional trial and error method are avoided, and the parameter setting efficiency and precision are remarkably improved; meanwhile, for an unmanned aerial vehicle control system with high nonlinearity and complexity, the WOA can adjust parameters based on actual feedback under the condition that accurate modeling of the system is not needed, and dependence on system modeling is reduced.