59 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 an air conditioner chilled water PID parameter optimization method based on an improved sparrow search algorithm. The method comprises the steps that population initialization, a discoverer updating formula and a follower updating formula in the sparrow search algorithm are improved; a return water temperature difference signal of the air conditioner chilled water system is detected and input to the improved sparrow search algorithm optimization PID controller, and the improved sparrow search algorithm dynamically adjusts the proportionality coefficient, the integral coefficient and the differential coefficient of the PID controller; and the improved sparrow search algorithm iteratively calculates an optimal parameter combination, and the PID controller performs optimal control of the chilled water system based on the optimal parameter combination. The improved sparrow search algorithm carries out population initialization by introducing a reverse learning strategy optimized by a refraction principle so as to enhance the diversity of an initial population; respectively introducing sine and cosine transform and a Cauchy variation mechanism in a position updating stage of the discoverer and the follower; and the PID controller dynamically adjusts Kp, Ki and Kd based on an improved algorithm so as to realize optimal parameter setting.

The embodiment of the invention provides a non-directional force sensor control method and system based on real-time force trend prediction, and the method comprises the steps: collecting a real-time force signal sequence generated when a target mechanical device carries out the contact operation, and inputting the real-time force signal sequence into a pre-trained force trend prediction model; and obtaining a force direction trend prediction result output by the force trend prediction model, generating a multi-dimensional force compensation signal according to the matching degree of the force direction trend prediction result and the current closed-loop control parameter, adjusting a proportion-integral-differential coefficient in the current closed-loop control parameter based on the multi-dimensional force compensation signal, generating an updated closed-loop control parameter, and outputting the updated closed-loop control parameter. The updated closed-loop control parameters are input into an actuator of the target mechanical device, and the contact operation track of the target mechanical device is dynamically adjusted through the actuator according to the updated closed-loop control parameters, so that the multi-dimensional force amplitude data output by the non-directional force sensor are converged to a preset force control target interval. The response speed and stability of the force control system can be improved.

The invention discloses an automobile semi-active suspension PID parameter adaptive adjustment and vibration control method and system based on reinforcement learning, and the method mainly comprises the following working steps: 1, constructing a reinforcement learning environment comprising a suspension dynamics high-fidelity simulation model and a PID controller; 2, defining a state space including a vehicle body vertical acceleration error e (t), an error integral term summa e (t) dt, an error differential term de (t) / dt, a suspension dynamic stroke zdef (t), a driving vehicle speed v (t) and road surface unevenness estimation xi (t); and 3, defining an action space including a proportion coefficient increment delta Kp, an integral coefficient increment delta Ki, a differential coefficient increment delta Kd and an output compensation amount delta e (t) of the PID controller. According to the method, the problem that the suspension performance is reduced due to the fact that control parameters of a traditional PID control method are fixed under the non-linear and time-varying working conditions is solved, and the riding comfort and the handling stability of the vehicle are remarkably improved.

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.

A liquid-cooling dynamic flow control method includes performing by a control device: obtaining at least one temperature difference between a set temperature and at least one current operating temperature of at least one power component; obtaining a set of target control parameters corresponding to the at least one temperature difference according to a pre-stored parameter table, wherein the pre-stored parameter table records the correspondence between a plurality of preset temperature intervals and a plurality of sets of preset control parameters, and each of the plurality of sets of preset control parameters includes a proportional coefficient, an integral coefficient and a differential coefficient; using the target control parameter and the at least one temperature difference to obtain a heat dissipation control parameter; and adjusting a rotational speed of a liquid-cooling pump with the heat dissipation control parameter.

The invention discloses an underwater brushless direct current motor control method based on fuzzy PID, and belongs to the technical field of underwater brushless direct current motor control. The problems that in an existing fuzzy PID technology, a brushless direct current motor is insufficient in control precision, a control rule is difficult to adapt to changes, and the parameter self-adjusting capacity is poor are solved. Input parameters and output parameters of fuzzy PID are set, the input parameters are rotation speed errors and rotation speed error change rates, and the output parameters are correction coefficients of proportionality coefficients, correction coefficients of integral coefficients and correction coefficients of differential coefficients; genetic gene coding is carried out on the membership function and the fuzzy rule of the fuzzy PID, and genetic gene coding is initialized; selecting a fitness function; a genetic algorithm rule is formulated, genetic manipulation is carried out by adopting a fitness proportion method and an elitist strategy combination rule, and then a new individual is generated based on a self-adaptive crossover and variation rule; and calculating the individual fitness based on the generated new individual, repeating until iteration is finished, and decoding to obtain an output parameter.

The invention discloses an unmanned surface vehicle robust track tracking method based on track curvature adaptive segmentation control, and relates to the technical field of unmanned surface vehicle autonomous navigation control, and the method comprises the steps: calculating the track curvature of a current leg in real time, and dividing a control section based on a preset threshold; executing a corresponding control strategy according to a control section, dynamically adjusting a proportionality coefficient, an integral coefficient and a differential coefficient by adopting fuzzy PID, and generating a steering lag prediction compensation amount based on a planning energy optimal speed; and steering compensation is carried out based on the steering lag prediction compensation amount, so that steering lag is reduced. And finally, judging whether the terminal point of the current flight segment is reached or not so as to obtain an optimized flight path tracking result. Through a curvature-driven three-section control strategy, a dynamic foresight distance dual constraint and an energy consumption optimization mechanism, the problems of tracking drift, control switching oscillation and energy waste of a traditional method in a sharp turn leg are solved, and the tracking precision and the system robustness under a complex track are remarkably improved.

The invention relates to an electric compressor hierarchical control method and system based on Fuzzy-PID and climate coupling, belongs to the field of new energy automobile thermal management systems, and aims to solve the problems of sudden energy consumption increase caused by climate mismatch, system oscillation caused by multi-physical field coupling deficiency and reliability reduction caused by multi-target conflict in traditional PID control. A season recognition layer judges a winter / summer / transition season mode through a 24-hour Gaussian weighted temperature algorithm; the fuzzy reasoning layer is fused with three variables of temperature deviation, humidity deviation and battery SOC change rate to dynamically output PID parameter correction; and the PID execution layer is combined with the seasonal influence factor, the SOC attenuation function and the humidity gain function to dynamically adjust the proportional / integral / differential coefficient to control the rotating speed of the compressor. Collaborative optimization of long-term climate characteristics and short-term disturbance is achieved, and the energy efficiency ratio of the system is increased; non-linear increase of power consumption in a high-humidity environment is inhibited; and cabin comfort, battery safety and energy consumption multi-target global optimization are compatible.

The invention discloses an underground water pumping and in-situ remediation system and a control method thereof. The system comprises a data acquisition module, a fuzzy PID control module, a repairing agent adding control module and an execution mechanism. The data acquisition module is used for acquiring pH value and conductivity parameters in the underground water treatment process; the fuzzy PID control module is used for adjusting the suction rate according to the deviation between the parameters and the set target value, and comprises three fuzzy rule libraries for respectively adjusting the proportion coefficient, the integral coefficient and the differential coefficient of a PID controller; the repairing agent adding control module adjusts the adding amount of a repairing agent according to the control signal; and the executing mechanism comprises a suction pump and a repairing agent dosing pump. The system further comprises a random number module which generates random disturbance parameters conforming to preset probability distribution and dynamically corrects control output. The suction pump is used for sucking and adding a repairing agent through a composite pipeline with an inner and outer pipe structure. The system can stably and efficiently control the groundwater remediation process, and the remediation effect 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 present invention belongs to the field of laboratory environment control technology, and in particular relates to an artificial intelligence-based intelligent monitoring and regulation method for laboratory temperature and humidity. The method first establishes laboratory temperature and humidity PID control and designs temperature and humidity PID controllers. Next, an objective function is constructed, integrating indicators such as overshoot and rise time and performing normalization processing, and optimizing temperature and humidity regulation performance by adjusting weight coefficients. Then, an improved starfish optimization algorithm (SFOA) is used to optimize the proportional, integral, and differential coefficients of the PID controller through parameter setting and population initialization. Finally, a simulation is performed to obtain a response curve. Compared with traditional PID control technology, the present invention can more efficiently and accurately regulate temperature and humidity, effectively improving the stability of the experimental environment and the reliability of experimental results.

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 provides an active air supply type transformer substation main transformer chamber cooling method and system, and the method comprises the steps: obtaining an oil temperature set value Ts, a top oil temperature measurement value Tc, and a radiation temperature Td, calculating a temperature difference value delta T according to Ts and Tc, calculating a correction value delta T according to Td through a proportional function Fw, and inputting the difference value between the temperature difference value delta T and the temperature correction value delta T into a PID algorithm, temperature control feedback used for adjusting the air supply amount is output; the PID algorithm comprises a proportionality coefficient Kp, a differential coefficient Kd and an integral coefficient Ki, and the integral coefficient Ki is equal to 0. According to the method, the integral part in the PID algorithm is deleted, and the correction value delta T is introduced, so that the corrected temperature difference value delta T is directly associated with the top oil temperature measurement value Tc, and frequent start and stop of the fan are avoided while the response speed of the fan for cooling is increased.

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.

A unified variable force / variable admittance control method and system based on neural energy functions is disclosed. The method includes: 1. Correcting the force sensor measurements based on the solved zero-point offset, and performing gravity compensation on the force / torque data based on the end-effector's gravity and center of mass position; 2. Defining a unified force / admittance control model for the robot and performing stability analysis; 3. Introducing a variable stiffness encoded energy function and a variable proportional / differential encoded energy function to learn variable stiffness parameters and variable proportional / differential coefficients online; 4. Solving for the analytical expressions of state-dependent variable admittance parameters; 5. Solving for the analytical expressions of the unified variable force / variable admittance control formula; 6. Calculating the pose adjustment amount and correcting the robot's current trajectory based on the pose adjustment amount. This invention achieves compliant and precise control of contact forces, enabling the system to maintain compliance while ensuring the stability and consistency of force output when interacting with the external environment.

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 invention discloses a microwave radiometer receiver temperature control method and system, and relates to the technical field of radiometers, and the method comprises the following steps: building a first-order pure lag transfer function of a microwave radiometer receiver temperature control model according to empirical data; based on historical temperature control data, establishing and updating a fuzzy rule table of a proportionality coefficient, an integral coefficient and a differential coefficient of receiver temperature control, and adding a multi-stage integral coefficient; dynamically executing integral separation control according to a real-time error and an error change rate based on the fuzzy rule table; and the integral action range is regulated and controlled through the multi-stage integral coefficient, and the heating temperature control quantity is output. Through combination of the fuzzy PID controller and a multistage integral separation mechanism, rapid and accurate temperature control of the microwave radiometer receiver in an environment temperature sudden change scene is realized.

The present application relates to the field of data analysis technology, and in particular to a method, device, and computer equipment for determining the clutch half-engagement point pressure. The method comprises: predicting the target pressure corresponding to the clutch based on the gearbox oil temperature of the target vehicle; predicting the target voltage corresponding to the clutch motor in the clutch based on the target pressure and the numerical control coefficient of the target vehicle; wherein the numerical control coefficient includes a numerical control proportional coefficient, a numerical control integral coefficient, and a numerical control differential coefficient; and performing error correction on the initial value of the clutch half-engagement point based on the target voltage to obtain the clutch half-engagement point pressure. When the present application uses the target voltage determined based on the numerical control coefficient to perform error correction on the initial value of the clutch half-engagement point, the target pressure obtained by the error correction can be matched with the actual vehicle condition of the target vehicle, thereby ensuring that the driving power of the target vehicle is not affected and improving the driving stability of the target vehicle.

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 relates to the technical field of fuzzy PID (Proportion Integration Differentiation) control, in particular to a hydrogen energy unmanned aerial vehicle dynamic hybrid control system based on fuzzy PID and an endurance optimization method. Change values are paired with proportion, integral and differential coefficient initial values by calculating change rates of a pitch angle, a roll angle and a vertical speed; comparing the power variation amplitude with a set threshold value by adopting a PID control algorithm to obtain a trend mark, selecting proportion, integral and differential coefficient adjustment amounts according to a trend to form an adjustment coefficient set, realizing parameter presetting in a pre-control stage, and combining an attitude error, an error variation amount and a power difference, so as to obtain a control result; combining a multi-target particle swarm optimization algorithm to carry out condition check on the upper limit and the lower limit of a combination result, a fuel cell power value and a lithium battery electric quantity value, rejecting non-conforming combinations and reserving effective records, calculating a comprehensive value of attitude errors and power differences for compliant combinations, and screening high-quality groups according to an ascending order; and extracting proportion, integral and differential coefficients to form a control instruction set.

The invention relates to a fuzzy PID adaptive control method for an auxiliary fan in a fan performance test system, and belongs to the technical field of fan performance test and control. In the performance test system, the target air volume and the actual air volume are collected in real time, the air volume deviation and the change rate of the air volume deviation serve as fuzzy reasoning input, PID parameters are set on line, and the PID parameters are used for driving an auxiliary fan to achieve self-adaptive adjustment. According to the method, proportion, integral and differential coefficients are dynamically corrected by using fuzzy logic rules, so that the auxiliary fan can quickly adjust and stabilize the air volume under the condition of working condition change or disturbance, fluctuation and deviation are reduced, and the air volume is ensured to be accurate and controllable in the test process. Therefore, the efficiency and consistency of working condition establishment of the fan performance test system are remarkably improved, and a reliable control means is provided for high-precision measurement of performance parameters and automation of the test process.

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.