37 results about "Self-tuning" patented technology

This invention discloses a multi-parameter coupling-based deep peak-shaving full-condition optimization control method for coal-fired power units. Addressing the problems of existing technologies, this invention constructs a multi-parameter coupling index system, calculates the comprehensive coupling strength between each parameter and the load, and selects core parameters. Dynamic clustering is used to finely divide the full-condition operation from 20% to 100% rated load into multiple operating zones. An LSTM-GRU dual-channel deep learning prediction model is established for each operating zone. A three-layer hierarchical control architecture is designed, comprising load optimization scheduling, boiler-turbine coordination control, and combustion optimization execution. The boiler-turbine coordination layer uses multivariate model predictive control and online self-tuning of multi-objective weights through fuzzy inference. The combustion execution layer uses the NSGA-III multi-objective evolutionary algorithm to optimize air distribution. A constraint-adaptive tuning mechanism based on safety margin is established to form a closed-loop iterative optimization across all operating conditions. This invention can significantly improve the load response speed, main parameter stability, and combustion economy of the unit during wide-load operation.

This invention provides a leakage-type adaptive space robot control method with self-tuning of reinforcement learning parameters, belonging to the field of space robot control technology. The control method includes: acquiring the current operating state of the space robot system, the control input from the previous moment, and the control parameters from the previous moment, as the current observation state; inputting the observation state into a policy network to acquire the current control parameters; outputting the current control parameters to a leakage-type adaptive robust controller to acquire the current control input; applying the current control input to the space robot system to drive it to perform a task; acquiring the current driving state of the space robot system; evaluating the driving state and acquiring the evaluation result; and adjusting the parameters of the policy network based on the evaluation result. This invention achieves adaptive parameter optimization by online tuning of key parameters in the leakage-type adaptive robust control structure.

The application relates to the technical field of automatic control, and particularly discloses an adaptive pressure regulation system for a secondary fermentation process of sparkling wine, which comprises a sensing and collecting module, a feature extraction module, an adaptive decision module and a collaborative control module. The scheme introduces a model reference adaptive control and single neuron PID fusion strategy, realizes real-time self-tuning of controller parameters, dynamically adjusts the control behavior according to uncertain factors such as the change of yeast activity and temperature fluctuation in the fermentation process, effectively suppresses overshoot while ensuring response speed, and significantly improves the steady-state precision and dynamic quality of pressure control; through the organic combination of conflict detection, edge computing, closed-loop feedback and quantitative evaluation, the secondary fermentation of sparkling wine is upgraded from single-tank independent control to multi-tank intelligent collaboration, the control precision is ensured, the system response speed, resource utilization efficiency and scalability are significantly improved, and a complete technical solution is provided for large-scale and high-quality sparkling wine production.

The present application relates to the field of aerospace technology, especially to a kind of earth observation satellite attitude controller parameter self-tuning method, the scheme includes: obtaining the desired direction and actual direction of satellite, and determining the control torque of flywheel, the satellite is earth observation satellite;Based on the control torque, obtain initial PID parameter, initial PID parameter is PID controller simulation parameter;Based on the PID controller simulation method of flywheel physical constraint, construct the fitness function of PID controller simulation parameter based on different satellite imaging mode;Satellite attitude control is simulated by PID controller simulation parameter, based on fitness function and initial PID parameter, the optimal fitness corresponding to target PID parameter under each satellite imaging mode is determined by iterative updating through particle swarm algorithm, the optimal solution of PID parameter is target PID parameter by using particle swarm algorithm combined with fitness function for iterative updating, the accuracy and efficiency of parameter setting are improved.

This invention provides a method, system, and device for controlling the thermal test temperature of satellite internal equipment. During satellite thermal testing, the temperature values ​​of each internal device are acquired by thermistors. Based on a relay feedback self-tuning algorithm and current and power models of each heater, control parameters of the thermal test temperature control system with a PID controller are obtained. Based on these control parameters, the temperature value of each device is compared with its corresponding preset target temperature value to obtain the temperature deviation value of each device. Based on the PID controller, the current control increment corresponding to each temperature deviation value is obtained. The control current value of each device in the previous control cycle is acquired. The control current value of each device in the previous cycle is added to its corresponding current control increment to obtain the heating current corresponding to each device. The heaters corresponding to each device are controlled to heat the equipment according to the corresponding heating current, reducing the satellite development cycle and cost, and achieving precise temperature control.

This invention relates to the field of permanent magnet synchronous motor (SPMSM) control technology, and discloses a downhole SPMSM temperature rise suppression I-F control strategy based on optimal power angle self-tuning. It aims to solve the problems of low-frequency oscillations and severe stator winding temperature rise caused by conservative current setting in traditional low-speed I-F control of downhole motors. The method first derives the optimal target power angle that balances high disturbance rejection and low heat generation by constructing a stiffness-heat dissipation comprehensive evaluation model. Then, with torque overload margin as a constraint, the optimal power angle is closed-loop tuned through adaptive stator current adjustment. This invention significantly suppresses redundant heat loss while maintaining high system disturbance rejection stiffness, effectively reducing the risk of downhole thermal failure; simultaneously, it optimizes system damping, suppresses low-frequency oscillations, and ensures reliable equipment operation.

The invention relates to the technical field of operation monitoring of numerical control equipment, in particular to an intelligent fault diagnosis method and system for a high-speed lathe. The method comprises the following steps: acquiring operation data of a high-speed lathe, and constructing a production observation channel and a diagnosis observation channel; collecting observation data sequences of the production observation channel and the diagnosis observation channel, calculating a diagnosability quota and generating a semi-health locking identifier; judging whether the high-speed lathe meets a self-tuning condition or not according to the diagnosable quota and the semi-health locking identifier; entering a self-tuning sandbox mode when the high-speed lathe does not meet the self-tuning condition, and updating control parameters of the high-speed lathe; and generating fault diagnosis information based on the machine tool state identifier and the observation data sequence of the diagnosis observation channel. According to the intelligent fault diagnosis method for the high-speed lathe, self-tuning of control parameters of the high-speed lathe and intelligent fault diagnosis are completed on the premise that the diagnosis capability is not sacrificed.

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

This invention discloses a PFC-full-bridge LLC resonant converter employing a novel control scheme. The input voltage is controlled by a PFC circuit to track the voltage waveform and optimize the current harmonic coefficients. The PFC circuit acquires the output voltage and voltage reference signal, passes them through a first subtractor into the voltage loop, then through a first multiplier and combines them with the input voltage. After comparison with the input current, the signal enters the current loop, controlling the BOOST converter to obtain the optimized high-voltage DC. This high-voltage DC then enters the subsequent LLC topology, where a BP-PID controller controls the power transistors to obtain the DC voltage required by the user. The BP-PID controller mainly consists of a self-tuning regulator and a load-mode controller. The self-tuning regulator incorporates a BP neural network to adjust parameters for self-stabilization; the load-mode controller acquires electrical signals for power calculation to determine which control scheme is suitable for the load.

This application relates to the field of circuit breaker technology, and particularly to a self-tuning method for overload long-delay protection characteristics and a circuit breaker intelligent controller. The method includes: acquiring the equivalent current value, current thermal time constant, current reference current, and current safety factor; calculating the current thermal capacity utilization rate through a first-order thermal network; determining that thermal compensation conditions are met; performing thermal compensation on the current thermal time constant and the current reference current based on the current thermal capacity utilization rate; generating a current protection curve; correcting the compensated reference current and the current safety factor through cold-state simulation verification to obtain the final reference current and the final safety factor; and correcting the compensated thermal time constant through thermal simulation verification to obtain the final thermal time constant. This method enables the tuning process to perceive the actual thermal state of the protected object, reduces the interference of residual states on the results, and improves the objectivity of the performance evaluation of the protection device.

The application discloses a kind of based on knowledge guide's quick reinforcement learning PID parameter self-tuning method and system, it is related to automation control technical field, it solves the problems of low training efficiency, poor initial strategy reliability, insufficient adaptability to industrial system time lag of existing reinforcement learning PID self-tuning scheme.It is based on TD3 algorithm to construct PID closed-loop control framework, decouples Actor network into sub-networks;PID parameter setting prior knowledge is concretized as correlation function, and a supervised data set is generated to pretrain sub-network to embed prior knowledge;Introduce action-state alignment mechanism to compensate system time lag, complete network joint training optimization based on designed reward function, and finally output optimal PID parameters from trained Actor network.The application greatly improves training efficiency and convergence reliability, adapts to industrial time lag system, realizes PID parameter adaptive optimization control.

This invention discloses a self-tuning temperature control method, system, device, and storage medium for a glove box, belonging to the field of automatic temperature control technology. The method includes: acquiring real-time temperature data inside the glove box based on the temperature acquisition module; triggering a self-tuning operation when switching to the current operating mode of perovskite thin film preparation: obtaining the temperature deviation based on the pre-acquired current target temperature and real-time temperature data, and performing temperature self-tuning calculations based on the pre-acquired current operating condition identification parameters and the temperature deviation to obtain proportional-integral-derivative (PID) parameters; obtaining a switching time modulation strategy based on the PID parameters, and then controlling the water cooling device to regulate the temperature of the glove box based on the switching time modulation strategy. This invention discloses a self-tuning temperature control method, system, device, and storage medium for a glove box, enabling precise temperature control inside the glove box.

This invention relates to a fuzzy neural network-based self-tuning PID control system for corrugated board warpage deformation, applied in the field of corrugated board production technology. The system includes: a data acquisition unit for real-time acquisition of raw paper moisture content, preheating cylinder temperature, ambient relative humidity, production line speed, and composite tension; a warpage detection device for detecting the amount of board warpage; a fuzzy neural network controller for outputting PID parameter adjustment values; a PID parameter self-tuning unit for real-time calculation of PID parameters; a PID controller for calculating control values; an actuator unit for adjusting operating parameters; and a graded trigger optimization module for graded online fine-tuning based on a superior product threshold and a qualified product threshold: no update when deviation ≤ superior product threshold, data is stored only when superior product threshold < deviation ≤ qualified product threshold, and an update is triggered when deviation > qualified product threshold. This invention solves the problems of low control accuracy and frequent ineffective adjustments in existing technologies, improving system stability and adaptability.

This application relates to an intelligent constant-temperature water heating control method, system, device, and medium. The method includes: acquiring multi-source water temperature data and performing adaptive Kalman filtering on the multi-source water temperature data to obtain a real-time state estimate; based on the real-time state estimate, performing feedback fuzzy inference to obtain a feedback control command, and based on the real-time state estimate, performing digital twin feedforward predictive simulation inference to obtain a feedforward control command; based on the feedback control command and the feedforward control command, performing adaptive weight fusion to obtain a preliminary optimized control command; based on a feedforward neural network compensation model, compensating the preliminary optimized control command to obtain a theoretical control command; and based on hard safety constraints, applying safety constraints to the theoretical control command to obtain a water heating control command. This method enables online self-tuning of PID parameters and active compensation for measurable disturbances, thereby achieving constant-temperature water heating control.

The present application relates to a kind of servo motor position-speed double ring parameter coordination self-tuning method and system, and it is related to servo motor control field.This technical scheme realizes Kp and Ki full-automatic setting, completely get rid of the dependence on artificial experience, can quickly obtain optimal parameter without manual intervention, greatly shorten the debugging cycle, reduce debugging cost, and the setting result consistency and repeatability are excellent, perfect adaptation batch product's standardization parameter calibration demand.Moreover, the scheme solves the coupling problem of Kp and Ki, by setting, detection, adjustment and multidimensional matching detection index, automatically corrects the mismatch problem of double ring parameters, eliminates the hidden danger of reaching position steady-state oscillation caused by traditional single-loop setting from the root.In addition, a multi-objective comprehensive evaluation system is established, which realizes the collaborative optimization of position accuracy and motion smoothness.Finally, relying on hybrid search strategy and history rollback mechanism, the efficiency and safety of Ki adjustment are considered.

This invention discloses a self-tuning method and system for three-parameter control parameters of a shaking table based on a genetic algorithm. The method includes: in displacement control mode, inputting a wideband, small-amplitude white noise displacement signal to a shaking table carrying a specimen, measuring the displacement response of the shaking table, identifying the closed-loop transfer function of the system displacement, and then calculating the open-loop transfer function of the system displacement; based on the open-loop transfer function of the displacement, adding a three-parameter controller and a signal generator to construct a closed-loop control system, and deriving the acceleration transfer function of the system; initializing the population based on a genetic algorithm, using the parameters of the three-parameter controller as the independent variables of the genetic algorithm, designing a self-tuning function, and using the genetic algorithm to iteratively optimize the self-tuning function until the termination condition is met, thereby obtaining the optimized three-parameter control parameters. This invention improves the system bandwidth and solves the problem of shaking table parameter tuning when considering the influence of the specimen.

The present application relates to a kind of self-adaptive collaborative control method and system of intelligent integrated brushless vector stirrer, belong to industrial stirring control software technical field.The method uses the distributed architecture of "edge computing+cloud cooperation", and edge software is based on hierarchical modular design, and core includes multidimensional perception data preprocessing, multi-modal adaptive vector control, soft and hard collaborative drive control, hierarchical intelligent protection and fault diagnosis, cloud edge collaborative interaction management and other modules.It realizes the intelligent identification of three types of working conditions of low viscosity homogeneous phase, high viscosity heterogeneous phase and sediment suspension by support vector machine classifier, and dynamically switches magnetic field orientation control strategy;Adopt particle swarm optimization algorithm to online self-tuning PID parameter;Early warning is realized by constructing fault diagnosis model based on BP neural network;Adopt "early warning-current limiting-shutdown" three-level protection mechanism;Efficient cooperation between cloud and edge is realized by lightweight MQTT protocol;And plug-in architecture is used to support rapid adaptation and extension.

This invention relates to an active dual-chamber cylindrical piston gas flow standard device and testing method. The device includes a fixed base, a balance cylinder fixedly installed on one side of the fixed base, a metering cylinder installed on the other side of the fixed base, a piston metering rod disposed in the balance cylinder and the metering cylinder, and a drive assembly disposed on the piston metering rod; the drive assembly is located between the balance cylinder and the metering cylinder. The testing method involves the following steps: This invention solves the thrust imbalance problem caused by uneven force distribution under high pressure conditions in traditional single-chamber piston structures by adopting a dual-chamber cylindrical piston structure with symmetrically arranged balance and metering chambers. This invention integrates a genetic algorithm optimization, LSTM state recognition, and fuzzy control intelligent PID control strategy to construct a closed-loop regulation mechanism with parameter self-tuning, state self-judgment, and disturbance self-adaptation. The three mechanisms work together to improve the system's adaptability, stability, and intelligent regulation capability in scenarios involving small flow rates and high-pressure gas flow control.

This invention discloses a method for controlling the target coolant temperature of an engine. The method includes: determining whether the original target coolant temperature maintenance conditions are met; if so, maintaining the original target coolant temperature; if not, entering dynamic optimization of the target coolant temperature: when the atmospheric temperature is lower than a first preset temperature; if the original target coolant temperature is greater than the actual coolant temperature, determining the target coolant temperature based on the original target coolant temperature, the target coolant temperature adjustment characteristic value, the actual coolant temperature, a preset temperature value, and a first low-temperature threshold; if the original target coolant temperature is not greater than the actual coolant temperature, determining the target coolant temperature based on the original target coolant temperature, the target coolant temperature adjustment characteristic value, the actual coolant temperature, and a preset temperature value. This invention improves the target coolant temperature control method under conditions of fan operation or knocking, or suppressing knocking, while also balancing the achievement of engine torque; it also avoids control parameter deviations caused by engine component aging, and is applicable to parameter self-tuning after the engine's life cycle has progressed.

The present application relates to the field of vehicle dynamics control, especially to a kind of distributed mine truck stability coordination control method based on dynamic envelope boundary. Including: establishing dynamics model to generate mass center side slip angle-yaw rate phase plane, combined with equivalent front and rear axle limit side slip angle to construct dynamic stability envelope boundary, actual state point is mapped to phase plane and is determined to be located inside or outside boundary according to intersection position, if inside, then adopt model predictive control to solve target additional yaw moment, if outside, then calculate the shortest distance and rate of change of state point to boundary and solve the moment through improved particle swarm self-tuning PID controller, finally, through quadratic programming, longitudinal tire force of each wheel is optimized and distributed and torque is output.The present application can quantify the stability boundary and deviation degree of double rear axle mine truck, and effectively balance the steering flexibility and limit stability.

The application discloses an unmanned hybrid sailboat cooperative heading control system and relates to the technical field of unmanned sailboats. The unmanned hybrid sailboat cooperative heading control system comprises a wind and wave coupling disturbance observation module, an adaptive sliding mode control module, a control distribution and energy optimization module, an actuator anti-saturation module, a control parameter self-tuning module and a dynamic training module. The application can improve the heading control precision of the unmanned sailboat in all-weather environments, reduce the risk degree of losing maneuverability or even drifting out of control of the unmanned sailboat in extreme working conditions such as no wind, weak wind or strong current against the wind, and improve the sailing economy, avoiding the difficulty in cooperative control and low energy efficiency of the traditional unmanned sailboat under the hybrid power system.

This invention provides a parameter self-tuning method for grid electromotive force (EMF) feedforward control, comprising the following steps: charging the DC-side capacitor voltage of a voltage source converter to its rated value; generating a three-phase modulation wave signal based on the grid EMF feedforward control parameters and the current closed-loop control parameters, and generating drive control signals for corresponding power electronic devices through a selected modulation strategy to achieve grid connection of the voltage source converter; automatically correcting the grid EMF feedforward control parameter k1 based on the DC-side capacitor voltage value after grid connection and the rated value of the DC-side capacitor voltage; and gradually decreasing the current closed-loop control parameter k1. p The system adjusts the grid electromotive force (EMF) feedforward control parameter k2 to 0, and simultaneously corrects it. Based on the adjusted EMF feedforward control parameters k1 and k2, a new three-phase modulation wave signal is generated. This invention enables self-tuning of the EMF feedforward control parameters, effectively suppressing grid-connected inrush current and achieving safe grid-connected operation of the voltage source converter based on EMF feedforward control.

The application provides a modular intelligent control method and device based on a hydraulic cylinder system, equipment and medium, collects hydraulic cylinder system operation data, extracts current working condition characteristics through analysis, identifies the working scene of the hydraulic cylinder system, collects historical experience data and system design parameters, decouples the control task of the hydraulic cylinder system into multiple independent algorithm units, each unit is built-in with a basic control law for a specific scene, forms a configurable modular algorithm library, dynamically selects and combines corresponding algorithm units from the modular algorithm library according to the working scene of the hydraulic cylinder system, generates the final control instruction adapted to the scene through a neural network, and realizes scene adaptive control; the application realizes adaptive matching of complex working conditions through online scene identification and neural network dynamic weight distribution, and achieves tracking accuracy improvement, overshoot reduction and performance stable operation in the whole life cycle in combination with a parameter self-tuning mechanism.

This application provides a standard working time dynamic self-tuning method based on the PID algorithm, applied in the field of production management technology. It obtains a dataset of measured time consumption for a target task over multiple execution cycles, preprocesses the dataset to remove outliers, and obtains an effective working time feature set. The logical deviation between this effective working time feature set and the current target reference value is determined, and a PID control algorithm is executed based on this deviation to achieve precise control of the working time data. During the iterative update of the target reference value, an ELM prediction model is used to dynamically adjust the control parameters of the PID control algorithm based on historical deviation trends. Finally, when the fluctuation range of the target reference value and the prediction error meet a preset threshold, the final standard working time is output, ensuring the stability and accuracy of the standard working time. This method has the advantage of dynamically adapting to changes in the production environment and improving the accuracy and stability of the standard working time.

The application is suitable for the technical field of circuit breaker, and particularly relates to an overload long-delay protection characteristic self-setting method and a circuit breaker intelligent controller. The method comprises the following steps: obtaining an electric current equivalent value, a current thermal time constant, a current reference current and a current safety factor; calculating a current thermal capacity usage rate through a first-order thermal network; determining that a thermal state compensation condition is met, and performing thermal state compensation on the current thermal time constant and the current reference current according to the current thermal capacity usage rate; generating a current protection curve; correcting the compensated reference current and the current safety factor through cold state simulation verification to obtain a final reference current and a final safety factor; and correcting the compensated thermal time constant through thermal state simulation verification to obtain a final thermal time constant. The method can sense the actual thermal state of a protection object in the setting process, reduces the interference of residual states on the result, and improves the objectivity of performance evaluation of the protection device.

The invention discloses an unmanned aerial vehicle anti-disturbance adaptive balancing method and system based on multi-modal sensing, and relates to the field of intelligent control, and the method comprises the steps: collecting data through a multi-modal sensing array, and estimating full state parameters in real time through tight coupling fusion and factor graph optimization; calculating residual error characteristics of a state estimation value and a measured value, and identifying a disturbance type through matching with a disturbance mode library; key features are extracted, and disturbance intensity and action points are quantized through an analytic hierarchy process; constructing a hierarchical control framework, reconstructing a control law according to disturbance characteristics, and carrying out online self-tuning on parameters by adopting a particle swarm algorithm; predicting state deviation by combining dynamics and a disturbance model, and planning a smooth disturbance avoidance track; and the control distribution is optimized by monitoring the health condition of the actuator and applying quadratic programming. The method has the advantages that a full-chain anti-disturbance system is constructed by combining hierarchical adaptive control, disturbance avoidance trajectory planning and actuator fault-tolerant distribution, and the balance stability and operation reliability of the unmanned aerial vehicle in a complex disturbance environment are improved.

The application discloses a design method of a primary frequency modulation controller of a pumped storage unit, and belongs to the technical field of power system frequency stability control. The method comprises the following steps: collecting historical data of the unit; designing a primary frequency modulation controller of an MRAC architecture, which comprises a nominal control input and an adaptive disturbance compensation input; constructing a multi-index parameter adaptive optimization model; collecting system state data in real time, correcting model parameters online, and updating the optimization model; and combining a recursive least square method to optimize controller parameters until convergence. The application combines online parameter identification and model reference adaptive control, realizes dynamic self-tuning of controller parameters, offsets the influence of unknown disturbances through an adaptive compensation mechanism of disturbance upper and lower bound estimation, and comprehensively considers multi-dimensional frequency modulation performance of a parameter optimization model. The method overcomes the limitations of a traditional fixed parameter controller, improves the frequency modulation accuracy and robustness of the pumped storage unit under variable working conditions and strong disturbance scenes, and guarantees the frequency stability of a power grid.

This invention belongs to the field of unmanned system control and discloses a high-performance control method and system for unmanned aerial vehicle (UAV) systems with multi-source heterogeneous interference in extreme environments. The method includes: constructing a dynamic model of the UAV with strong coupling, nonlinearity, and multi-source heterogeneous interference; designing an online learning-type parameter self-tuning controller and using a neural network to adaptively adjust the control parameters; designing a time-varying state observer to estimate the system state in real time; constructing a unified estimation compensation signal for multi-source heterogeneous interference; filtering and compensating for aperiodic disturbances using an adaptive low-pass filter; adaptively compensating for periodic high-frequency disturbances using a quasi-resonant control component; and collaboratively solving to form a composite control quantity to suppress multi-source heterogeneous interference. This invention can simultaneously suppress aperiodic broadband disturbances and periodic high-frequency disturbances, effectively compensate for strongly coupled nonlinear dynamics, and achieve high-precision attitude tracking and robust stable flight of the UAV in extreme environments.

This invention relates to an intelligent control system for an electromagnetic wave water processor, belonging to the field of water treatment technology. It includes a multi-parameter detection module, an electromagnetic wave generation module, a frequency sweep optimization module, a fuzzy self-tuning control module, and a mode-coordinated controller. The multi-parameter detection module collects water temperature, conductivity, pH value, and flow velocity in real time; the frequency sweep optimization module scans and determines the optimal operating frequency for the current water quality; the fuzzy self-tuning control module generates frequency adjustment amounts based on real-time changes in water quality parameters for closed-loop regulation; and the mode-coordinated controller realizes a dynamic coordinated closed loop of frequency sweeping, tracking, and re-sweeping. This invention, through deep coordination of frequency sweep optimization and fuzzy self-tuning control, combined with pre-compensation and adaptive frequency sweeping stepping, has the advantages of strong water quality adaptability, stable treatment effect, and optimized energy consumption.