102 results about "Equilibrium control" patented technology

A balance control method for a wheel-legged robot is provided. The robot includes a moving wheel, n links, and n rotating joints, the moving wheel being connected to a first link through a first rotating joint of the n rotating joints, and the n links being connected in series through n−1 rotating joints other than the first rotating joint, n being a positive integer greater than 1. The method includes: acquiring a state quantity of the wheel-legged robot at a first moment; determining dynamics model parameters according to a dynamics equation of the wheel-legged robot and the state quantity at the first moment; establishing a sliding surface according to the state quantity at the first moment; calculating rotation torques of the n rotating joints according to the sliding surface and the dynamics model parameters; and controlling the rotating joints according to the rotation torques of the n rotating joints.

The invention discloses a surface-mounted permanent magnet synchronous motor sensorless high-performance sliding-mode control method, and the method comprises the steps: designing an improved self-adaptive terminal sliding-mode switching function for the problem of high-frequency buffeting of a sliding-mode observer caused by the fixed gain design of the switching function; and dynamically adjusting the gain of the switching function according to the sum of the stator current error and the integral thereof under the two-phase static coordinate system so as to suppress high-frequency buffeting and improve the estimation precision. In order to further improve the performance of the position sensorless control system, a reaching law in a sliding mode controller is optimized, a novel composite gain self-adaptive sliding mode reaching law is designed, the reaching law combines a self-adaptive gain term, a nonlinear exponential term and a linear term, the system state is accelerated to approach when being far away from a sliding mode surface, buffeting is reduced when the system state approaches, and the control precision of the position sensorless control system is improved. Therefore, the dynamic response speed and buffeting suppression capability of the control system are balanced.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention discloses an automatic shipment balance control method, which belongs to the technical field of automatic shipment control, and comprises the following steps: scanning a cabin of a berthing empty ship, establishing a cabin three-dimensional model, constructing a wharf coordinate system and a ship coordinate system, and obtaining ship type data; planning a shipping strategy according to the ship type data, setting shipping turns and blanking point coordinates and target material height of each turn, and determining safe operation parameters of the chute tube; according to the coordinates of the blanking point of the current turn and the safe operation parameters of the chute tube, the outlet of the chute tube is controlled to be aligned with the coordinates of the blanking point of the current turn based on the wharf coordinate system by adjusting the motion parameters of the ship loader, and blanking is conducted when it is monitored that the material height of the current turn reaches the target material height; controlling the outlet of the chute tube to transfer to the blanking point coordinate of the next round; refreshing the cabin three-dimensional model in the loading process, obtaining the posture of the ship body, and updating the coordinates of the blanking point; and stopping the shipping operation until the shipping volume reaches the standard. The method can improve the automatic shipment balance control precision.

The embodiments disclose a financial transaction platform comprising a Stability Verification Engine coupled to a Transaction Input and configured to calculate stability functions using a Lyapunov Stability Engine to evaluate whether a proposed transaction converges toward equilibrium within a Stability Valley defined by threshold values in a Stability Function Database, a Deterministic Consensus Protocol coupled to the Stability Verification Engine and configured to process validated transactions through pre-commit verification, mathematical proof generation, and finality attestation to ensure irreversible acceptance, a Cascade Prevention System coupled to the Deterministic Consensus Protocol and configured to apply progressive intervention levels including enhanced verification, fee adjustment, transaction size limits, processing delays, and emergency stabilization when abnormal conditions are detected, and a Transaction Output coupled to the Cascade Prevention System and configured to forward transactions confirmed as stable and finalized to downstream predictive management and interface node systems.

The invention relates to a balance control algorithm for gravity center trajectory planning and real-time tracking of a humanoid robot, in particular to the technical field of robot control, and realizes dynamic balance control through a closed-loop process of state sensing, online gravity center trajectory re-planning, hierarchical control law calculation and joint torque output and execution. A layered dynamic modeling and control framework is constructed, a variable-height inverted pendulum model is combined with model prediction control, and a gravity center and zero moment point track feasible in dynamics is generated online; virtual restoring force is generated through a gravity center impedance controller, a task priority inverse dynamics solver is used, multi-task execution is coordinated on the premise that balance tasks are guaranteed to be absolutely preferential through a null-space projection technology, theoretical torque is converted into current instructions to drive joints to move, and through efficient collaboration and real-time updating of dynamic models of all levels, the balance tasks are optimized. According to the invention, deep collaboration of various technical features is realized, and the problems of poor adaptability, slow response and poor balance capability of a traditional method in a dynamic environment are effectively solved.

The invention relates to the technical field of robot attitude balance control, and provides a robot attitude balance control method and system. Before the feet of the robot make contact with the supporting face or at the initial stage of contact, preset micro-force excitation is actively applied to the supporting face for detection, and mechanical response information and foot displacement information between the feet and the supporting face are collected in real time; based on the information, the system can estimate the local physical characteristics of the supporting surface in real time, and dynamically control and adjust the balance control strategy of the robot according to the estimated local physical characteristics. Through active detection and foresight adjustment, the characteristic change of the supporting surface can be identified and adapted before or at the initial stage of instability, so that the control conflict and vicious circle possibly brought by passive compensation are avoided.

The invention discloses a self-balancing control method applied to a heteroaxial two-wheeled vehicle, the vehicle carries a scissor-type double CMG module, the module comprises two CMG units, and the method comprises hardware decoupling: using the scissor-type double CMG to counteract course disturbance torque generated when the CMG works on a physical level; role separation: redefining a CMG system and a steering system; feedforward coordination: during turning, not depending on the CMG always generating a restoring moment to maintain a 0-degree inclination angle, calculating a steady-state inclination angle (phi lean) required at the speed and the steering angle through a dynamic model feedforward; and track planning: by coordinating track planning, it is ensured that the inclination action of the CMG and the steering action of the steering are perfectly synchronized in time, and balance is dynamically maintained. The problem of CMG saturation is solved, high-precision decoupling control is achieved, turning is smooth and safe, full-speed-domain coverage is achieved, and efficiency and energy saving are achieved.

The invention relates to a multi-objective optimization control method for an intelligent networked electric vehicle based on data driving and an application thereof. The method comprises the following steps: extracting a vehicle model for a single intelligent networked electric vehicle and obtaining a state representation of the vehicle model in a vehicle queue; constructing a data-driven prediction model based on a subspace identification method and historical data, and constructing a linear input and output mapping relation; constructing a multi-objective optimization function, and executing optimization control based on a multi-objective optimization result; the method is applied to a controller of an intelligent network-connected electric vehicle in a vehicle queue, the controller collects acceleration and braking operation data to track torque and outputs control quantity for executing optimization control, and queue control is achieved. According to the method, the problem of control performance degradation caused by modeling errors or system complexity is effectively solved; a multi-objective optimization problem is introduced, so that the practicability and robustness of the system under a complex working condition are enhanced; the control performance is balanced while the prediction precision is kept, and the operation safety, the energy saving performance and the cooperation efficiency of the intelligent network connection electric vehicle queue are effectively improved.

The invention provides a multi-source cooperative operation and dynamic adjustment method for a power distribution network, and relates to the technical field of power system operation and control, and the method comprises the steps: building a multi-level physical characteristic model based on the collected network topology parameters of the power distribution network and the physical operation parameters of all distributed energy devices; on the basis of the multi-level physical characteristic model, a master-slave collaborative optimization control architecture is utilized to obtain collaborative balance control conditions; based on the master-slave collaborative optimization control architecture, the collaborative balance control condition and the initialized energy management interaction signal, distributed iterative calculation is carried out to obtain an energy management interaction signal and a power control strategy; and based on the real-time operation state data and the power control strategy of the power distribution network at the current moment, performing rolling optimization calculation in a limited time domain by using a model prediction control method to obtain a real-time control instruction for driving each distributed energy device to act. According to the invention, the problem that multi-body operation and dynamic optimization in the power distribution network are difficult to coordinate in the prior art is solved.

The present application relates to a kind of narrow body vehicle balance control method based on control moment gyro, belong to narrow body vehicle active roll control system field.The dynamics equation of control moment gyro is established, the active roll dynamics model of narrow body vehicle is calculated to obtain the roll angle error and roll angular velocity error of vehicle, then the active roll controller is designed using sliding mode control method to control the roll angle error and roll angular velocity error, and the rotational angular velocity of control moment gyro frame is obtained;Finally, the control moment gyro on narrow body vehicle is adjusted in real time, and vehicle balance is realized.The vehicle roll scheme in the present application uses control moment gyro device, with high control precision, fast response speed, and independent of suspension system, can realize greater balance torque.By controlling the frame angular velocity of control moment gyro, the dynamic balance of narrow body vehicle when steering can be realized, to assist the safe steering of driver.

The invention relates to the field of automatic balance weight forklifts, in particular to an automatic balance weight forklift control method and system based on multi-source data, and the method comprises the steps that the multi-source data of a sensor are obtained; compensating the weighing value of the left pallet fork and the weighing value of the right pallet fork according to the real-time attitude angle of the forklift, and generating an equivalent left weighing value and an equivalent right weighing value under the horizontal reference system; calculating a static gravity center offset component and a dynamic inertia offset component; performing addition or weighted summation on the static gravity center offset component and the dynamic inertia offset component to obtain a dynamic gravity center offset; inputting the real-time center-of-gravity offset into a balance controller, and judging whether the absolute value of the center-of-gravity offset reaches or exceeds a preset stable safety threshold value or not by the balance controller; and if so, triggering a balance control strategy. Through hierarchical feature construction and physical constraint fusion of multi-source data, high-precision and low-delay dynamic gravity center estimation is realized, so that the effect of dynamically adjusting a forklift control strategy is achieved.

The invention discloses an intelligent landing leg automatic balance control system and control method, and belongs to the technical field of landing leg control. The method is used for solving or improving the technical problems that the anti-shake capability is insufficient under the dynamic load and the sensor error affects the balance precision in the existing scheme. Through the technical advantage of independent operation of each module, the high-precision data of the sensing module, the intelligent strategy of the decision control module, the precise action of the execution module, the risk prevention and control of the security module, and the deep cooperation of the data-strategy-action closed loop, control-protection double tracks and scene adaptation optimization among the modules, the safety of the system is improved. The full-process efficient linkage of data acquisition, dynamic control and safety guarantee is realized, and the dynamic load adaptive capacity, the balance control precision and the system reliability are effectively improved.

The invention discloses a building comprehensive management control method and device and a medium, and belongs to the technical field of building automation control, and the method comprises the steps: building a dual-medium coupling model between an air medium and a building structure medium, and carrying out the parameterization; establishing a heat flux balance equation based on a parameterization result; building operation data and environment data are collected and processed; the air medium enthalpy difference change rate and the building structure body temperature change rate are calculated and substituted into the heat flux balance equation, the real-time heat flux difference is obtained, and a coupling state vector is generated; and inputting the coupling state vector into a dynamic balance optimization control equation to execute iterative solution, obtaining a control variable, and generating a control signal to adjust a building operation state. According to the method, an energy transfer rule is accurately described through double-medium coupling modeling, a heat flux balance equation is established to realize dynamic balance control, control variables are divided into air side and building structure body side adjustment components to realize multi-system collaborative optimization, and the control precision and the energy-saving effect are improved.

The invention discloses a Nash equilibrium control method for a data-driven unmanned system under incomplete information. The method comprises the following steps: constructing a distributed dynamic state feedback controller for an unmanned system, and converting a Nash equilibrium solving problem into an output adjustment problem; in the off-line stage, a continuously excited control input sequence is applied to the unmanned system, a corresponding state sequence, a state derivative sequence, a measurement noise sequence and an external signal sequence are collected, and unmanned system data matrixes are constructed respectively; based on the unmanned system data matrix, a Nash equilibrium search problem is converted into a semi-definite programming problem and solved, and a dynamic feedback control gain matrix is obtained; and in the online stage, the controller of each agent dynamically adjusts a decision based on a control signal generated by the dynamic feedback control gain matrix, so that the unmanned system is converged to a Nash equilibrium point. According to the method, an offline and online scheme of data driving and no precise modeling is adopted, robust anti-interference and distributed equilibrium convergence are achieved, and the method is suitable for a complex unmanned collaborative scene.

The present disclosure relates to the technical field of intelligent robots, and specifically provides a robot, a control method and device thereof, and a storage medium. A robot control method includes obtaining first attitude information of a robot and determining second attitude information of a standing surface according to the first attitude information, determining a target dynamic equation according to a dynamic equation of the robot and a dynamic equation of the standing surface, establishing a control optimization task of the robot based on the target dynamic equation, obtaining target torques of each joint of the robot based on the first attitude information, the second attitude information and the control optimization task, and controlling movements of each joint of the robot according to the target torques. In the present disclosure, through dynamic analysis and optimization control of the attitude of the robot and the attitude of the standing surface, the balance problem of the robot on a dynamic standing surface is solved, the balance control of the robot on the dynamic standing surface is realized, the application scenario of the robot is expanded, and the control stability of the robot is improved.

The invention relates to an electricity-hydrogen-chemical-storage-heat multi-energy coupling system intelligent regulation and control simulation verification platform and method based on a distributed architecture, and the platform comprises an energy management and control platform which employs a cross-time-scale layered regulation and control architecture and comprises an optimization scheduling layer and a cooperative control layer; the optimization scheduling layer generates a multi-time scale scheduling instruction from a second level to a day level based on a multi-target dynamic optimization function; the cooperative control layer receives an instruction of the optimization scheduling layer, and executes real-time electricity-hydrogen-storage-heat cooperative frequency and voltage regulation control through a power balance controller, a heat supply network balance controller and a hydrogen transmission balance controller; the distributed simulation system is used for truly simulating dynamic behaviors of the physical system; and the timing system is used for providing a global clock synchronization signal for the energy management and control platform and the distributed simulation system. According to the method, a high-precision, high-real-time and high-reliability simulation verification means is provided for coal-based chemical green low-carbon transformation, the engineering risk can be remarkably reduced, and the flexibility and economical efficiency of the system are improved.

The invention discloses a flexible interconnection device energy balance control method, system and equipment based on multi-port power interaction, and a medium, and belongs to the technical field of flexible interconnection device energy balance control, and the method comprises the steps: collecting multi-port flexible data; obtaining a system state estimation value; adjusting the droop control coefficient of each port by adopting a self-adaptive adjustment mechanism; calculating reference power and reference voltage of each port according to the droop control coefficient and the system state estimation value; calculating the energy balance control quantity of each port based on a Lyapunov energy function; calculating a multi-port coordination control quantity; generating a total control quantity of each port; the fault is judged by comparing the residual signal with a fault detection threshold value; and outputting the power regulation quantity and the voltage regulation quantity of each port according to the total control quantity. According to the multi-port coordination control algorithm, the power coupling effect between the ports is effectively processed through the coordination coefficient between the ports and the double-layer control structure, and the system oscillation is reduced.

This invention discloses a force balance control method for a six-dimensional active cellular micro-force sensor. The micro-force sensor includes a probe, a moving platform, a base, and six measurement branches connecting the moving platform and the base. Each measurement branch consists of a flexible beam, a sensing layer, and a driving layer. The method includes: when the probe at the end of the micro-force sensor is subjected to an external measuring force, the flexible beam deforms accordingly; the real-time pose of the moving platform is measured by the sensing layer of each measurement branch; the difference between the real-time pose and the desired zero pose is used to obtain an error signal, which is then fed into a robust sliding mode predictive controller to generate a corresponding control law; the control law then passes through a damping compensator and a decoupler to obtain a control input voltage signal, which is applied to the driving layer. Under the voltage, the driving layer undergoes bending deformation, causing the moving platform pose to return to the desired equilibrium position. This invention effectively solves the problem of complex lumped interference and achieves the goal of fast response, strong robustness, and high stability force balance control.

The invention relates to the technical field of shield tunneling machine control, in particular to a self-balancing control system and method for a slurry shield, and the method comprises the following steps: in the tunneling process of a shield tunneling machine, obtaining the pressure of a working bin and the liquid level of an air cushion bin in real time; entering the pressure control mode according to the received instruction for selecting the pressure control mode; entering the liquid level control mode according to the received instruction for selecting the liquid level control mode; in the pressure control mode, the pressure of the working bin is maintained near the pressure target value of the muddy water bin; and in the liquid level control mode, the deviation, obtained in real time, of the air cushion bin liquid level and the air cushion bin liquid level target value is judged, and according to the deviation obtained through judgment, the rotating speed of the sludge discharging pump is adjusted so that the liquid level of the air cushion bin can tend to the air cushion bin liquid level target value. According to the invention, cooperative control of pressure and liquid level is realized, so that the slurry shield keeps stable pressure and moderate liquid level in the tunneling process, and the automation characteristic improves the control precision, the response speed and the safety of the system.

The invention discloses a vehicle self-balancing control method and device, equipment, a vehicle, a program product and a storage medium, the vehicle comprises two wheels arranged front and back, and the method comprises the steps that the driving state of the vehicle in a self-balancing mode is detected, and first state data is obtained; the first state data comprises vehicle head steering state data, vehicle body inclination state data and vehicle speed related data; determining first control data of the vehicle according to the first state data, wherein the first control data comprises vehicle head steering control data and vehicle speed related control data of the vehicle; the vehicle head steering of the vehicle is adjusted according to the vehicle head steering control data, and the vehicle speed related state of the vehicle is adjusted according to the vehicle speed related control data, so that the vehicle approaches or achieves a control target; wherein the control target comprises a preset vehicle body inclination state, and the vehicle is in a driving self-balancing state under the preset vehicle body inclination state.

The application relates to the technical field of industrial control, and provides a differential pressure balance control method and system for a plugging and draining device, which comprises the following steps: obtaining a pressure signal of the plugging and draining device, and performing segmented processing on the pressure signal; extracting characteristic parameters reflecting the fluid state from the pressure signal after the segmented processing; judging a flow mode in which the fluid is located according to the characteristic parameters; and adjusting a differential pressure balance control strategy according to the flow mode, wherein: when the flow mode is a stable state, a conventional differential pressure feedback adjustment mode is adopted; when the flow mode is an unstable state, a trend estimation is performed on the pressure signal to obtain an overall differential pressure change trend, and the opening of a control valve is adjusted according to the overall differential pressure change trend, while the adjustment speed of the control valve is limited, and the minimum opening of the control valve is maintained. The application can avoid misjudgment and excessive adjustment caused by instantaneous fluctuation of a traditional control system.

This invention proposes a method based on the total discharge outlet NO x This invention relates to a denitrification equilibrium control method that combines ammonia slip correction with other methods, and pertains to the field of flue gas denitrification control in thermal power plants. Specifically, it provides a method based on NO from the total exhaust outlet. x A denitrification equilibrium control method that combines concentration and ammonia slip correction, which involves adjusting the total NO emission at the discharge outlet. x The hourly average concentration of NO is introduced into closed-loop control to dynamically calculate the NO concentration for the current hour. x NO emission surplus x As a baseline setting value, and combined with the NO values ​​at both entrances x The concentration difference is corrected by a baseline setpoint based on weighted load allocation. At the same time, fuzzy inference is used to constrain and correct the ammonia escape concentration on both sides. Finally, the opening of the ammonia injection valves on both sides is precisely adjusted by a model predictive controller, thereby minimizing the consumption of reducing agent while ensuring that environmental emissions meet the standards.

The application discloses an unmanned bicycle motion control method and system with a balance flywheel and an unmanned bicycle, and solves the control problem of the unmanned bicycle under the condition that system parameters cannot be accurately measured. The method decouples the control of the unmanned bicycle into three parallel tasks of speed control, steering control and balance control, adopts a proportional-integral (PI) controller to realize the speed control, adopts a first-order filter to realize the steering control, and adopts an adaptive neural network controller to realize the balance control. The control method proposed by the application effectively solves the balance control problem of the unmanned bicycle under different speeds and different steering angles under the condition that system modeling is inaccurate, and can identify uncertain parameters and functions in the system model.

The invention provides a balance control method, system and equipment for a double-wheel-leg robot and a medium, and relates to the technical field of robots, the mass center dynamics of the double-wheel-leg robot is equivalent to a wheel type inverted pendulum model, a dynamic model is established through a Lagrange method, and the movement and mass center balance of the double-wheel-leg robot can be accurately described; the dynamic constraints of the driving wheel and the centroid inclination angle in the dynamic model are solved, and the centroid dynamics can be effectively simplified; through the centroid trajectory planning model, the expected driving wheel position and speed can be fused into centroid trajectory planning, the advancing position and the advancing speed are tracked while centroid balance control is achieved, the under-actuated characteristic of the robot is met, meanwhile, the design of a controller is simplified, the tracking precision is improved, and the performance of a control system is guaranteed.

The invention provides a graph neural network prediction model construction method for thin-wall part sectioning inner support pressure, and particularly relates to the technical field of machine learning model construction. According to the method, a system coupling topological graph is constructed, nodes correspond to supporting petals of a sectioning inner supporting clamp, and connecting edges are defined according to mechanical linkage between the supporting petals and relevance of geometric weak areas of the acted thin-wall parts and have weight coefficients representing coupling strength. A graph neural network pressure coupling relation observer model is constructed based on the graph, nodes are mapped into calculation units, connection edges are mapped into information transmission paths, and weight coefficients are used as prior modulation factors. A model training sample set is obtained, input features comprise a supporting valve target pressure control instruction and the like, and an output label is a real coupling interference value. A model is obtained through supervised training, the defects of a traditional model are overcome, a special model which does not need a high-cost full-sensor array and accurate physical parameters and can accurately predict the pressure coupling relation is obtained, and a prediction basis is provided for self-adaptive equilibrium control.

This disclosure provides a two-wheeled vehicle control method and device, a two-wheeled vehicle, and a storage medium. By acquiring first attitude data and first driving state data of the two-wheeled vehicle, the control priorities and corresponding control parameters for the power, steering, and braking of the two-wheeled vehicle are determined based on the first attitude data and the first driving state data. At least one of the power, steering, and braking of the two-wheeled vehicle is coordinated and controlled through the control priorities and control parameters, so that the two-wheeled vehicle is in a balanced state. This achieves self-balancing control of the two-wheeled vehicle, improves the driving stability of the two-wheeled vehicle, and enriches the data basis for self-balancing control by using multi-source data such as attitude data and driving state data. This improves the accuracy and robustness of control and solves the problem of disconnect between self-balancing control and driving state.

This invention discloses a general fast modulation method for a VIENNA rectifier, comprising: 1) establishing a mathematical model of nine switches based on the mathematical model of the VIENNA rectifier, obtaining the equality constraints between the duty cycle of the nine switches and the modulation wave; 2) establishing two equations satisfying the control voltage constraint through newly defined variables, and providing a general solution satisfying the equality constraint conditions; 3) obtaining six states based on the different directions of the three-phase current; obtaining inequality constraints under different states based on these six states; and obtaining the upper and lower limits of the free variables based on the inequality constraints under the six states; 4) solving for the compensation amount for neutral point potential balance control starting from the control equation for the capacitor neutral point potential control of the VIENNA rectifier. The modulation model and solution process of the VIENNA rectifier disclosed in this invention have a small computational workload, significantly reducing the computation time compared to the SVPWM modulation technology of three-level converters.

The invention discloses a testing machine balance control method and device, electronic equipment and a storage medium, a to-be-tested testing machine is connected with a vibration exciter, and the method comprises the following steps: in response to a leveling instruction for the to-be-tested testing machine, detecting strain data of the to-be-tested testing machine and parameter information of the vibration exciter; according to the strain data and the vibration exciter parameter information, matching a target leveling action sequence from a preset leveling action sequence library; according to the target leveling action sequence and the vibration exciter parameter information, determining a vibration excitation adjustment value; the vibration exciter is driven to output the excitation force according with the excitation adjustment value; and correcting the strain data by adopting the exciting force until the corrected strain data meets a preset balance condition. According to the embodiment of the invention, the vibration exciter can be controlled to automatically execute the leveling strategy through the preset leveling strategy until the strain data converges to the preset balance condition, the non-repeatability of manual operation is avoided, and the precision and efficiency of test preparation are improved.

The application discloses a dynamic balance control method, device and equipment of a fan system and a storage medium, wherein the fan system comprises an impeller, at least two electromagnets, a pressure sensor arranged at the bottom of any one of the electromagnets, and a base arranged at the bottom of the remaining electromagnets, the base has the same height as the pressure sensor, and the dynamic balance control method comprises the following steps: acquiring at least one historical pressure value and at least one current pressure value of the pressure sensor corresponding to the current rotating speed of the impeller; judging whether the absolute value of the difference between any historical pressure value and any current pressure value is greater than or equal to a preset pressure value, and if yes, adjusting the input current value of the at least one electromagnet. In the application, the input current of the electromagnet is adjusted according to the comparison result of the absolute value of the difference between the historical pressure value and the current pressure value corresponding to the current rotating speed and the preset pressure value during the operation of the fan system, so that the impeller of the fan system reaches dynamic balance, and the calibration efficiency of dynamic balance is improved.