37 results about "Differential coefficient" patented technology

The invention relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, and the method comprises the steps: firstly collecting the operation data of a system, constructing a state vector of reinforcement learning, and enabling a reinforcement learning agent to fully understand the current operation condition of the system; then, the state vector is input into a reinforcement learning strategy network, an action for adjusting a control strategy is generated by the network, and the action can be used for modifying the proportion, integral or differential coefficient of PID and can also be used for adjusting the prediction step length, weight coefficient or constraint strength of model prediction control, so that the adaptive capacity of a controller to external changes is enhanced; then, a reward signal is constructed according to a response result of the reference controller; the reward function comprehensively considers the error size, the steady-state characteristic, the system energy consumption, the control smoothness and the stability requirement, so that the reinforcement learning not only pays attention to the error minimization when optimizing the strategy, but also considers the low energy consumption, the smooth action and the anti-interference performance at the same time.

The invention relates to the technical field of refrigeration equipment control, in particular to a wine cabinet compressor self-adaptive control method and system based on environmental parameters. The method comprises the steps that the external environment temperature, the internal actual temperature and the target storage temperature set by a user of the wine cabinet are collected in real time; an environment thermal potential energy index representing external environment thermal invasion intensity is constructed by calculating static deviation characteristics and dynamic change rate characteristics of the environment temperature; and correcting a proportionality coefficient of a PID control algorithm in real time based on the environmental thermal potential energy index, calculating a compressor control quantity through the PID algorithm in combination with a preset integral coefficient and a differential coefficient, mapping the compressor control quantity into a target operation frequency, driving the wine cabinet compressor to operate, and realizing self-adaptive control of the wine cabinet compressor based on the environmental parameters. According to the method, the environment thermal load change can be actively sensed, the control lag is eliminated, the smoothness of steady-state operation is considered while the thermal shock is inhibited, and the control precision of the wine cabinet compressor is improved.

This application provides a papermaking pulping control method, system, storage medium, and program product, relating to the field of industrial process automation control technology. By performing weighted and differential arithmetic operations on current and historical errors based on preset proportional, integral, and differential coefficients, the ideal control increment is calculated, initially determining the adjustment force required to eliminate system errors. A weighted smoothing mechanism based on filter coefficients is introduced, i.e., by multiplying the ideal control increment with preset filter coefficients and combining this with the multiplication of the increment calculated at the previous moment with the complement of the filter coefficients, two partial results are generated and summed. This ensures that the commands received by the electric regulating valve are smoothly corrected, thereby eliminating high-frequency valve jitter and system oscillation caused by signal noise without reducing the system response speed, achieving high-stability control of the pulping process.

The invention relates to the technical field of charging piles, and discloses a power module fan rotating speed adjusting method based on fuzzy PID control, and the method comprises the steps: obtaining an air inlet temperature fuzzy value according to a preset air inlet temperature given value and a plurality of detected air inlet temperature real-time values of a power module; quantizing the air inlet temperature fuzzy values to the corresponding fuzzy domains so as to respectively calculate the membership degrees of the air inlet temperature fuzzy values in the respective corresponding fuzzy domains; and determining an incremental proportion coefficient real-time adjusting quantity, an incremental integral coefficient real-time adjusting quantity and an incremental differential coefficient real-time adjusting quantity in an incremental PID control scene according to each membership degree, so as to determine a fan rotating speed adjusting quantity of the power module based on the above. According to the method, the fuzzy PID control mode is combined on the basis of conventional PID control, the influence of internal and external factors on fan conversion control can be effectively reduced, the control precision of the fan rotating speed of the power module is improved, and the fan operation stability of the power module is further improved.

The invention relates to a servo press control method and system based on multi-modal perception, and the method comprises the steps: collecting and preprocessing multi-modal data reflecting the operation state of a servo press in real time, the multi-modal data comprising stamping tonnage, sliding block position, stamping speed, hydraulic flow, mechanical deformation and safety perception information; multi-modal data fusion is realized by using a mixed strategy combining weighted fusion and a BP neural network; based on the servo press system state reflected by the fusion data, parameters of a PID controller for controlling the current of a motor are adjusted in a self-adaptive mode, and the method comprises the steps that the current operation condition is automatically judged to be no-load, light-load or heavy-load; dynamically adjusting a proportionality coefficient, an integral coefficient and a differential coefficient of a PID (Proportion Integration Differentiation) controller for controlling the current of the motor in combination with a Ziegler-Nichols setting method and a fuzzy control rule; and under the control of the self-adaptive PID controller, an S-shaped acceleration and deceleration curve is adopted to plan the movement track of the sliding rail according to the fused data and a preset stamping process curve.

The invention relates to the technical field of water conservancy automation control and edge computing, in particular to a self-adaptive PID control system for a water conservancy reservoir environment, which comprises an edge computing gateway, a water level sensor, a flow sensor and a servo execution mechanism, fluid static geometric parameters are obtained through a topological mapping module, an initial state matrix of a fluid momentum state observer is established, a momentum decoupling module inputs a dam front water level, upstream incoming water flow and opening data to update the state matrix, and transient fluctuation energy characteristics and real reserve residual characteristics are generated through decoupling. The priori fitting module calculates proportion, integral and differential coefficients according to the parameters, and the closed-loop evolution module generates a control instruction and corrects bottom resistance parameters; physical separation of real reservoir capacity change and surface wave disturbance is achieved, invalid oscillation of the gate is reduced, control stability under the flood peak working condition is improved, and the service life of an executing mechanism is prolonged.

The application discloses an intelligent PID control method for the temperature of a reaction kettle, and comprises the following steps: constructing a reaction kettle temperature model, and adopting a first-order inertia plus delay to describe the dynamic characteristics of the reaction kettle temperature; constructing a Kalman filter PID control model based on an improved RBF neural network; performing normalization processing on the deviation value of the target temperature of the reaction kettle and the real-time temperature; inputting the normalized temperature deviation value into the Kalman filter PID control model, outputting a temperature control signal to an adjusting valve, and adjusting the temperature of the reaction kettle. The improved RBF neural network algorithm is used to intelligently set the PID control parameters, and the Kalman filter is introduced, so that the temperature system can still realize fast response under the environment of nonlinearity, large lag and disturbance. Through real-time dynamic adjustment of the proportional coefficient, the integral coefficient and the differential coefficient, overshoot is inhibited, the steady-state error is reduced, and the system stable time is accelerated.

The invention provides a CPPS small interference stability control method of fuzzy adaptive PID based on LSTM, and belongs to the technical field of power and small interference stability control, and the method comprises the steps: collecting historical network delay data to train an LSTM model, and obtaining an LSTM prediction model; the rotating speed deviation (error) and the rotating speed deviation change rate (error change rate) of the synchronous generator are collected; predicting the network delay at the current moment by using an LSTM prediction model, and dynamically setting the proportionality coefficient, the integral coefficient and the differential coefficient of the PID controller through a fuzzy logic decision in combination with the error and the error change rate; and inputting the set proportionality coefficient, integral coefficient and differential coefficient into a PID controller to generate a control signal, and enabling the control signal to act on a synchronous generator of the information physical power system to realize small-interference stable control of the information physical power system. According to the invention, the problem of system instability caused by network delay randomness in CPPS is solved.

The present application relates to the technical field of refrigeration equipment control, and particularly relates to a wine cabinet compressor adaptive control method and system based on environmental parameters. The method comprises the following steps: collecting the external environmental temperature of the wine cabinet, the actual internal temperature and the target storage temperature set by the user in real time; constructing an environmental heat potential energy index representing the intensity of external environmental heat invasion by calculating the static deviation characteristic and the dynamic change rate characteristic of the environmental temperature; based on the environmental heat potential energy index, correcting the proportional coefficient of the PID control algorithm in real time, and then combining the preset integral coefficient and the differential coefficient, calculating the compressor control amount through the PID algorithm and mapping it into the target operating frequency to drive the wine cabinet compressor to operate, thereby realizing adaptive control of the wine cabinet compressor based on environmental parameters. The present application can actively perceive the change of environmental heat load, eliminate control lag, suppress heat shock while taking into account the smoothness of steady-state operation, and improve the control accuracy of the wine cabinet compressor.

The invention relates to a motor control method and device based on an EO-BP network, electronic equipment and a storage medium. The method is applied to a motor control system based on an EO-BP network, the system comprises at least one group of sub-control loops, each sub-control loop comprises a sub-controller and a sub-system, the sub-controller is in communication connection with the sub-system, and the sub-system outputs a proportionality coefficient, an integral coefficient and a differential coefficient to the sub-controller. The method comprises the steps of obtaining a current state parameter and a target state parameter of a motor; determining a proportionality coefficient, an integral coefficient and a differential coefficient of the sub-controller based on the current state parameter, the target state parameter and the sub-system; and the sub-controller determines motor control parameters of the motor according to the current state parameter, the target state parameter, the proportionality coefficient, the integral coefficient and the differential coefficient. According to the invention, the PID control is combined with the neural network, and the BP neural network is used for setting the three parameters of the PID controller, so that the unsatisfactory control effect of the control system caused by the difficulty in determining the PID control parameters is avoided.

The invention discloses a fuzzy PID (Proportion Integration Differentiation) synchronous control system for intelligent prestress tensioning, and particularly relates to the technical field of hydraulic control of constructional engineering, and the fuzzy PID synchronous control system for intelligent prestress tensioning comprises a control module; a servo driving module; an acquisition module; according to the system, through a hierarchical fuzzy rule base, a control strategy is dynamically switched according to different stages of initial tensioning and load holding, a proportionality coefficient is strengthened in the initial tensioning stage so as to quickly respond to the pressure increasing requirement, overshoot is avoided, a differential coefficient is strengthened in the load holding stage so as to improve the stable pressure holding capacity, and the load holding pressure fluctuation is effectively controlled within + / -0.8%; the precision and the stage adaptability of the tensioning process are obviously improved; pID parameters can be dynamically optimized without manual intervention, control misalignment caused by working condition fluctuation is avoided, and the self-adaptive capacity and long-term stability of the system in a complex construction environment are greatly improved.

The application relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, which comprises the following steps: firstly, collecting system operation data and constructing a state vector of reinforcement learning, so that an intelligent agent of reinforcement learning can fully understand the current operation condition of the system; then, inputting the state vector into a reinforcement learning strategy network, generating an action for adjusting a control strategy by the network, the action can be used for modifying proportional, integral or differential coefficients of a PID, or can be used for adjusting a prediction step, a weight coefficient or a constraint strength of model predictive control, so as to enhance the adaptability of the controller to external changes; then, constructing a reward signal according to a response result of a benchmark controller; and a reward function comprehensively considers error size, steady-state characteristics, system energy consumption, control smoothness and stability requirements, so that the reinforcement learning not only focuses on error minimization when optimizing the strategy, but also considers low energy consumption, action smoothness and anti-interference.

The invention discloses an alternating current load simulation method and circuit for a high-power test. The method comprises the following steps: acquiring an initial bandwidth of an alternating current signal corresponding to a target load and a resistance value of a conversion resistor in a preset current source circuit; based on the initial bandwidth and the resistance value of the conversion resistor, determining a plurality of coefficients of a preset PID proportional integral differential controller, the plurality of coefficients including a proportionality coefficient, an integral coefficient and a differential coefficient; determining a control voltage signal through a PID (Proportion Integration Differentiation) controller based on the plurality of coefficients; a target load is simulated in the current source circuit based on the control voltage signal and the conversion resistance. The technical problems that the traditional high-power test AC load simulation impedance precision is low, the actual AC load characteristics cannot be comprehensively simulated, and PID parameter setting is difficult are solved.

The application relates to the technical field of printer fixing unit temperature control, in particular to a heating control method of a printer fixing unit, which comprises the following steps: for each heating control period, obtaining a current temperature value and a target temperature value of the fixing unit, and determining a heating duty cycle by using a PID control algorithm based on a temperature difference value between the current temperature value and the target temperature value, wherein proportional, integral and differential coefficients of the PID control algorithm are updated based on the temperature difference value of the corresponding heating control period; determining a corresponding Duty order based on the heating duty cycle; determining a total number of half-wave periods that need to be heated in the current heating control period based on the Duty order; and performing heating control on the fixing unit based on the total number of half-wave periods that need to be heated. The application can perform fine-grained heating control on the fixing unit, so that rapid response and stable control of temperature can be realized in each heating period.

The invention discloses an electronic paper power supply dynamic adjustment method and system based on PID control and state learning, and the method comprises the steps: collecting the output voltage Vout (t) of a DCDC converter of an electronic paper power supply at each moment in real time in a sampling period, and calculating the deviation value e (t) of the output voltage Vout (t) and a preset target value Vref in real time; when the DCDC converter is in a PID control mode and the absolute value of the deviation value e (t) at any moment is larger than or equal to a preset precision threshold value, preset PID parameters are obtained, the duty ratio adjusting quantity is calculated according to the PID parameters and the deviation value e (t), and the PID parameters comprise a proportionality coefficient, an integral coefficient and a differential coefficient; and updating the PWM duty ratio of the DCDC converter according to the duty ratio adjusting quantity, and outputting the updated PWM duty ratio to the DCDC converter. According to the method and the system, when the load changes or the input voltage fluctuates, the output voltage of the DCDC converter can be quickly adjusted, and the time for recovering the voltage to be stable is shortened.

The invention provides a fuzzy self-adaptive Q learning control method and system for sewage treatment, and the method comprises the steps: obtaining the dissolved oxygen concentration as a system state, and constructing a nonlinear optimization problem; performing dynamic adjustment on proportion, integral and differential coefficients by adopting a Mamdani type fuzzy inference rule in combination with fuzzy logic, performing defuzzification through a centroid method to obtain adaptive PID parameters, and forming a fuzzy adaptive control strategy; an online Q learning framework is further constructed, a Q function is approximated by using the evaluation network, a network generation strategy adjustment amount is executed, and network weight is optimized based on a Bellman equation and a particle swarm algorithm; finally, fuzzy control and Q learning output are fused, control input is generated through a coupling coefficient to adjust an oxygen transfer coefficient, and tracking control over the dissolved oxygen concentration is achieved. According to the invention, the tracking control precision of the dissolved oxygen concentration and the system operation stability can be improved.

The invention provides a PID (Proportion Integration Differentiation)-controlled rotor aeroelastic balancing calculation method and device. The method comprises the following steps: setting a balancing target value; obtaining an initial value; a corresponding PID control channel is established, a controlled object of each PID controller is determined, the controlled object is a balancing target, parameters of a proportionality coefficient, an integral coefficient and a differential coefficient of each PID controller are set, and then the PID controllers are established; the PID controller controls the rotor aeroelastic calculation module to calculate and feed back the value of the controlled object, it is judged that the fed-back value of the controlled object reaches the balancing target value, if the value of the controlled object reaches the balancing target value, control over the rotor aeroelastic calculation module is stopped, and aeroelastic balancing is completed. The method can adapt to changes of the external environment and the self state of the helicopter, and can also be used for rotor balancing in the transient and maneuvering flight process. While the balancing precision is maintained, the problems of low calculation efficiency and poor convergence of a traditional balancing method at a high speed are solved.

The invention relates to the technical field of air pressure control, and particularly discloses a butterfly valve, a butterfly valve control method, electronic equipment and a storage medium. Determining an air pressure state prediction value and a target air pressure change rate of the current control period based on the initial feedback air pressure value, a historical feedback air pressure value of the previous air pressure control period and a historical air pressure change rate; based on the initial feedback air pressure value, the air pressure state prediction value and the target air pressure change rate, determining an error correlation quantity in the current air pressure control period; determining a proportionality coefficient and a differential coefficient of a controller in the butterfly valve based on the error correlation quantity and a preset hyperbolic secant function, and determining an integral coefficient of the controller in the butterfly valve based on the error correlation quantity and a preset exponential function; and based on the proportionality coefficient, the differential coefficient and the integral coefficient, the electric control signal for controlling the valve opening degree in the butterfly valve is determined, and the method can effectively avoid the overshoot problem in the environment change process.

This invention discloses a segmented control method and device for an electric compressor based on a CO2 heat pump system. The method includes: PID control of the compressor speed, using the difference between the target temperature and the actual temperature as input and the compressor speed control quantity as output; real-time acquisition of the difference between the target temperature and the actual temperature for segmented control: in the rapid adjustment phase, the three PID parameters are set relatively large; in the slow adjustment phase, the three PID parameters are set relatively small; in the stable phase, the three PID parameters are set to fixed values; after the actual temperature first reaches the target temperature, the integral and derivative coefficients are adjusted according to the temperature overshoot; finally, the electric compressor of the CO2 heat pump system is controlled according to the compressor speed control quantity. This invention can effectively reduce system overshoot in actual control, enabling the actual temperature to quickly stabilize around the target temperature ±1 degree, and enabling rapid stabilization of the air conditioning vent temperature, improving driving reliability and comfort.

The invention provides a control method, system and equipment of a roller welding machine and a medium. The control method comprises the steps that the material type and the material thickness of a first material to be welded currently of the roller welding machine and the set reference speed of a welding roller set motor in the roller welding machine are obtained; determining a target proportionality coefficient, a target integral coefficient and a target differential coefficient corresponding to the current working condition according to the material type and the material thickness; determining a tension deviation between a current actual tension value of the first material and a set tension value; calculating the tension deviation by using the target proportionality coefficient, the target integral coefficient and the target differential coefficient to determine the PID speed compensation amount; based on the PID speed compensation amount, a reference speed is set, and PID target parameters of an unwinding motor of the first material are determined; according to the PID target parameters, PID control is conducted on the rotating speed of an unwinding motor of the first material. The precision of material tension control can be improved, and then the consistency of product quality is improved.

The invention discloses a vacuum coating valve intelligent control system based on a self-adaptive PID algorithm, and the system comprises a multi-source process data collection module which is used for collecting and preprocessing multi-source process data, and forming multivariable time sequence data; the variable diagram construction module is used for mapping the multivariable time sequence data into a vacuum coating variable diagram; the improved CSPNet model parameter generation module is used for outputting a candidate proportionality coefficient, a candidate integral coefficient and a candidate differential coefficient through cross-stage feature extraction and working condition prediction; the initial control quantity generation module is used for calculating an initial control quantity; the self-adaptive PID parameter updating module is used for obtaining the updated PID parameters; and the PID control execution module is used for closed-loop intelligent adjustment of the vacuum degree and the process gas flow. According to the invention, through combination of the improved CSPNet model and the adaptive PID adjustment, intelligent and stable control of the vacuum coating valve is realized.

The invention discloses a foaming printing temperature precise control system of an automatic elliptical printing machine, and relates to the technical field of temperature control, the foaming printing temperature precise control system comprises a temperature precise control system main body, and the temperature precise control system main body comprises a temperature sensor module and a foaming effect detection module. A'production parameter-real-time temperature-foaming effect 'three-in-one closed-loop regulation and control system is constructed, the problem that traditional fixed parameter PID control cannot adapt to multi-batch production is effectively solved, the system uses a fabric material, a slurry formula and pattern density as core input parameters through an improved PID algorithm based on a BP neural network and built in the central control module, and the real-time temperature and the foaming effect of the fabric are effectively controlled. PID proportion, integral and differential coefficients are dynamically optimized by combining real-time temperature data, fed back by the temperature sensor module, of the single bedplate and key indexes, such as foaming height, height uniformity and color uniformity, obtained by the foaming effect detection module, so that self-adaptive correction of temperature control parameters is realized.

The invention discloses a water-fertilizer-gas regulation and control method, system and device, electronic equipment and a storage medium, and relates to the technical field of agricultural irrigation. The water-fertilizer-gas regulation and control method comprises the following steps: acquiring a first water-fertilizer-gas regulation and control parameter from a sensor, inputting an error value between the first water, fertilizer and gas regulation and control parameter and a preset water, fertilizer and gas regulation and control parameter and an error value change rate into the trained neural network model to obtain a first parameter correction amount of a proportional-integral-differential control module; and updating at least one of a proportional coefficient, an integral coefficient and a differential coefficient of the proportional-integral-differential control module. Thus, the accuracy of the parameter control signal output by the proportional-integral-differential control module can be improved, then the water, fertilizer and gas regulation and control parameters can be precisely regulated and controlled, the overshoot or oscillation phenomenon caused by insufficient control precision is effectively avoided, and the stability and the irrigation effect of the irrigation system are ensured.

The application discloses a control method for motor start-stop impact section flexible suppression, comprising the following steps: based on the current working condition stage, calling the corresponding working condition preset nonlinear regulation rule, correcting the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd of the nonlinear PID controller to adapt to the motor dynamic characteristics in different start-stop stages; the nonlinear regulation rule is determined based on the nonlinear regulation coefficient, the speed error determined by the motor target speed and the actual speed, and the reference coefficient; on the basis of correcting the Kp, Ki and Kd parameters of the nonlinear PID controller, the motor stator current and electromagnetic torque operation data are collected in real time, and the control parameters are corrected based on the collected data; the nonlinear PID controller continuously outputs the regulation voltage or regulation current based on the real-time corrected control parameters, and completes the motor start-stop whole process impact suppression. The flexible and accurate control of the motor start-stop impact is realized, the control precision is improved, and the CT performance is improved.

The invention provides a composite control method and system based on a fuzzy control algorithm and PID, and the method comprises the steps: obtaining an output error and an error change rate of a system in real time, and obtaining a state variable of the system; taking the output error, the error change rate and the state variable as input, and outputting a proportionality coefficient, an integral coefficient and a differential coefficient at the same time through a fuzzy inference engine; forming a parameter self-tuning PID (Proportion Integration Differentiation) controller by utilizing a proportionality coefficient, an integral coefficient and a differential coefficient, and calculating a control signal; and the control signal is output to a servo driver of the servo load simulator system to control an execution mechanism to act. All working conditions and nonlinear self-adaption of controller parameters are achieved through the fuzzy-PID fusion controller, and compared with a traditional PID controller, response can be made stably through self-adaption adjustment of the parameters in the face of load sudden change and other complex working conditions, overshoot is effectively reduced, the adjustment time is shortened, and the inherent high precision of the PID controller can be kept.

The application provides a control method, system, device and medium of a roll welding machine, comprising: obtaining a material type and a material thickness of a first material to be welded by the roll welding machine currently, and a set reference speed of a motor of a welding roller group in the roll welding machine; determining a target proportional coefficient, a target integral coefficient and a target differential coefficient corresponding to a current working condition according to the material type and the material thickness; determining a tension deviation between a current actual tension value and a set tension value of the first material; performing operation on the tension deviation by using the target proportional coefficient, the target integral coefficient and the target differential coefficient to determine a PID speed compensation amount; determining a PID target parameter of a unwinding motor of the first material based on the PID speed compensation amount and the set reference speed; and performing PID control on a rotating speed of the unwinding motor of the first material according to the PID target parameter. The application can improve the accuracy of material tension control, and further improve the consistency of product quality.

The present disclosure provides a virtual scene generation method, device and equipment for rehabilitation training, which can be applied to the technical field of rehabilitation training. The method comprises: collecting physiological signals of a target object by using a collection instrument when the target object makes a behavior action using a rehabilitation training device in a first period; performing feature extraction on the physiological signals to obtain physiological state features; processing the physiological state features by using a control model for the first period to obtain control coefficients corresponding to the physiological state features, the control coefficients including a proportional coefficient, an integral coefficient and a differential coefficient, the proportional coefficient being used to constrain the difference between the physiological state features and a preset state feature, the integral coefficient being used to constrain the difference between historical physiological state features of multiple historical periods; the differential coefficient being used to constrain the change amplitude of the difference between the physiological state features and the preset state feature; based on the control coefficients, controlling the virtual scene of the first period to change the scene to obtain a virtual scene for a second period.

The invention discloses a fuzzy PID (Proportion Integration Differentiation) control parameter online optimization method based on multi-population collaborative PSO (Particle Swarm Optimization), which comprises the following steps of: establishing a mathematical model of a control object, and solving a transfer function of the control object according to structural characteristics and a working principle of a system; establishing a fuzzy PID controller, setting a fuzzy rule and configuring related parameter values; operating an improved PSO particle swarm algorithm, wherein an algorithm optimization space is a value interval of a proportionality coefficient, an integral coefficient and a differential coefficient corresponding to the fuzzy rule; the speed and the position of each particle are updated, the fitness value of each particle is obtained in the learning process, and the extreme values of individual particles and population particles are updated in each iteration; and optimizing the output parameters of the fuzzy controller according to the result of the PSO particle swarm algorithm. According to the method, a gradient-oriented local optimization mechanism is introduced, the global search capability and convergence stability of the PSO algorithm are improved, parameters of the fuzzy controller are dynamically adjusted by improving the PSO algorithm, and the control performance of the system is improved.

This invention relates to the field of water conservancy automation control and edge computing technology, specifically an adaptive PID control system for a water conservancy reservoir environment, including an edge computing gateway, a water level sensor, a flow sensor, and a servo actuator. A topology mapping module acquires the static geometric parameters of the fluid and establishes an initial state matrix for a fluid momentum state observer. A momentum decoupling module inputs upstream water level, upstream inflow, and opening data to update the state matrix, generating transient fluctuation energy characteristics and actual storage residual characteristics. A priori fitting module calculates proportional, integral, and differential coefficients based on these characteristics. A closed-loop evolution module generates control commands and corrects the bottom resistance parameters. This invention achieves physical separation between actual reservoir capacity changes and surface wave disturbances, reduces ineffective gate oscillations, and improves control stability and actuator lifespan under flood peak conditions.

The application provides an AMT clutch actuator control method and system based on fuzzy PID, and belongs to the technical field of gear shifting control. A clutch position sensor acquires the clutch position at a sampling time. The position error and the position error increment of the clutch requested position and the current position are calculated. The position error and the error increment are calculated through fuzzy processing to determine the fuzzy relationship of the proportional coefficient, the integral coefficient and the differential coefficient in the PID coefficient corresponding to the position error and the position error increment, and to obtain the correction coefficient and the position error. The output of the PID controller at the k time is adjusted according to the correction coefficient and the position error, and is used as the control amount of the AMT clutch actuator. The application does not need manual calibration of the clutch control parameters, can calculate the PID output according to the error and the error increment of the clutch requested target position and the actual clutch position, realizes the control of the clutch position, realizes the effect of good self-adaptation, and has higher control precision.