214 results about "Nonlinear control" patented technology

The embodiment of the invention provides a filling production line control method and system based on a virtual reality technology. According to the embodiment of the invention, the method comprises the steps: synchronously collecting a real-time dynamic parameter set of a fluid medium, constructing a dynamic coupling factor, generating a dynamic response coefficient matrix through a fluid coupling identification model, generating a nonlinear control parameter combination according to the dynamic response coefficient matrix, and inputting the nonlinear control parameter combination into a motion trail planner. Generating an optimized motion track instruction, driving an execution mechanism to execute filling operation according to the optimized motion track instruction through a servo controller, monitoring the deviation degree in real time, reconstructing the fluid coupling identification model when the deviation degree exceeds a preset threshold value, and updating a dynamic coupling factor; the nonlinear control parameter combination is corrected through the updated dynamic coupling factor, a closed-loop interlocking control mechanism is generated, and the technical scheme provided by the embodiment of the invention not only improves the intelligent level of a filling production line, but also improves the precision and stability in the filling process.

The invention discloses a wind power operation and maintenance ship embarkation system compensation control method based on active disturbance rejection control and related equipment. The method comprises the following steps: predicting a system external wave disturbance variable of a six-degree-of-freedom embarkation compensation platform according to environmental parameters through a wave disturbance prediction model; the total disturbance quantity in the system is predicted through the extended state observer according to the real-time attitude information and the electric cylinder control signal, and then the total disturbance of the system is obtained; a nonlinear control quantity is calculated through an active disturbance rejection controller according to real-time attitude information, expected attitude information and a preset decoupling matrix, a feed-forward compensation quantity is calculated according to total disturbance of a system, then an output instruction of the controller is calculated, and compensation control is achieved. According to the embodiment of the invention, compensation control can be realized through the wave interference prediction model, the state expansion observer and the active disturbance rejection controller, and high precision and response speed are achieved. The method can be widely applied to the technical field of automation control.

The invention relates to the technical field of data processing, in particular to an activation feeder outlet baffle adjusting device and method, and the device comprises a data collection module, a parameter optimization module, a control decision module and an execution module. The data acquisition module acquires load data of a conveying belt and material state data of a discharge port in real time; the parameter optimization module adopts a genetic algorithm to dynamically optimize first-order control parameters and second-order control parameters, the control decision module triggers an adjustment strategy based on a load deviation step by step, preferentially adjusts the width of a discharge port through the angle adjustment amount of an outlet baffle, and if the deviation is continuous, a vibration amplitude adjustment instruction is generated in combination with material state data; the execution module converts the adjusting quantity into displacement and vibration driving signals. Through hierarchical cooperative control and a closed-loop feedback mechanism, the problem of nonlinear control instability caused by dynamic changes of material humidity, granularity and viscosity is solved, the output matching precision under the multivariable coupling working condition is improved, and the material viscosity and the secondary bridging risk are reduced.

The invention discloses an oil reservoir polymer flooding process oil-water distribution simulation method and device based on a cellular automaton, equipment, a medium and a product, and relates to the field of oil reservoir engineering and seepage mechanics. The method comprises the steps that rectangular grid division is conducted on an oil reservoir space, three-dimensional coordinates of cellular nodes are determined, and a cellular automaton model is constructed; according to the polymer oil displacement scheme parameters, oil reservoir state parameters at the end moment of water displacement are initialized, and boundary conditions of the mole-type cellular automaton model are established; making an evolution rule for each neighborhood cellular node in the Moire cellular automaton model; according to a numerical value stability rule, adjusting parameters of each neighborhood cellular node in the Mohr-type cellular automaton model; according to the method, a relative permeability nonlinear regulation and control rule is formulated for the Mohr-type cellular automaton model to simulate oil-water distribution in the oil reservoir polymer flooding process of the Mohr-type cellular automaton model, the starting pressure gradient is accurately simulated, and the low-permeability oil reservoir evolution process is accurately described.

The invention discloses a high-precision temperature control method and system for a photoetching machine, and belongs to the technical field of photoetching machine temperature control. The method comprises the following steps: acquiring a temperature array on the lower surface of a mask, heat flux density distribution of a bearing surface of an exposure table and a hot pressure difference signal of an air floating layer, carrying out feature coding, and constructing a high-dimensional thermal coupling situation feature matrix; a dynamic thermal weight correlation map is generated based on the matrix, exposure key nodes serve as anchor points, and explicit modeling is conducted on nonlinear thermal paths among the nodes through a graph neural network; further calculating the spatial gradient change rate of the heat flux per unit area of the mask region according to the thermal weight map, and mapping the spatial gradient change rate to an exposure table temperature control array to generate a nonlinear control function curved surface; and dynamically adjusting power output and response delay parameters of the local heating unit according to the control function, so that dynamic thermal field equalization with nanoscale precision can be realized, thermal drift is effectively inhibited, and the photoetching imaging quality and the yield of an advanced process are improved.

The invention provides a steering-by-wire vehicle variable transmission ratio design method and system based on a fuzzy neural network, and belongs to the field of vehicle steering-by-wire. According to a fixed gain method, the steering angle transmission ratio is too small at a low speed, and the sensitivity is low at a high speed; the existing intelligent algorithm excessively depends on the experience of a designer and the quality of a data sample. The method comprises the following steps: establishing a closed-loop driver-vehicle system, designing a multi-target evaluation method by using a quadratic cost function of a vehicle dynamic state, and obtaining a data relationship between a vehicle ideal variable transmission ratio characteristic and a vehicle longitudinal speed and a steering wheel angle; a nonlinear control model is established through a fuzzy RBF network, and a globally optimal solution is obtained based on nonlinear model learning. The robustness of the control system is improved, the stable and reliable transmission ratio can be provided under various vehicle conditions, and the problem that a steering system is light and flexible is solved, so that the experience feeling of a driver under various vehicle conditions is improved, and meanwhile the operation stability and safety of the vehicle are improved.

The invention discloses a control method for ultra-low-altitude flight of a large unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model of the unmanned aerial vehicle, the kinetic model comprising dynamic equations of position, speed, attitude control and control input; performing error tracking control on the attitude and the speed of the unmanned aerial vehicle by adopting a Backstepping nonlinear control method based on the dynamic model; estimating external disturbance and dynamic parameters of the unmanned aerial vehicle in real time by combining an L1 adaptive control method, and adaptively adjusting control input to compensate wind speed change; predicting a future flight state by using a model prediction control algorithm, optimizing a flight path and compensating wind speed disturbance; a safe trajectory is planned in real time through a dynamic window method, and obstacles are avoided in combination with dynamic constraints. According to the invention, through organic combination of Backstepping control, L1 adaptive control, MPC and DWA algorithms, the complex problems of wind speed disturbance, obstacle avoidance, high-precision operation and the like in ultra-low-altitude flight can be solved.

The invention discloses a single-connecting-rod mechanical arm dynamic memory event triggering fixed time control method and system, and the method comprises the steps: describing a controlled system as a nonlinear system with state time lag and input time lag through modeling; introducing coordinate transformation based on a nonlinear system, and defining a tracking error; designing a virtual control law and an adaptive updating law through the defined tracking error; the stability of the control system is ensured by providing a dynamic memory event triggering mechanism, the system comprises a sensor module, a communication module, a control system and a man-machine interaction module, and the dynamic memory event triggering control mechanism in the method is used as a novel nonlinear control strategy, so that network resources and data transmission can be saved, and the system reliability is improved. The designed fixed time control strategy enables the system to achieve stable control within strictly specified time by accurately presetting the convergence time boundary, the response speed is remarkably improved, and the method is particularly suitable for industrial automation scenes with strict requirements for time precision.

The invention relates to flight control of a tilt rotorcraft, in particular to a nonlinear super-spiral robust control method for the tilt rotorcraft, and aims to realize nonlinear control of the tilt rotorcraft on the basis of not simplifying or linearizing a dynamical model and quickly track expected speed and attitude instructions, and the technical scheme adopted by the invention is that the nonlinear super-spiral robust control method for the tilt rotorcraft comprises a nonlinear super-spiral robust control method for the tilt rotorcraft. Establishing a tilting rotorcraft dynamic model; and designing a super-spiral attitude control law based on the rotation matrix to obtain a control moment of the aircraft, then carrying out real-time approximation on an inverse model of the dynamic model by using an approximation characteristic of an RBF neural network, and calculating an actual control quantity of the aircraft by using the control moment so as to realize automatic control of the tilt rotorcraft. The method is mainly applied to design, manufacturing and flight control occasions of tilt rotorcrafts.

The invention discloses a cross-domain motion planning method and system for an air-ground amphibious robot, and relates to the technical field of robot control. The method comprises the following steps: acquiring operation constraints of the air-ground amphibious robot in a working space; performing path planning according to the operation constraint by using a particle swarm optimization algorithm; constructing a kinetic model, and constructing a wing folding angle smooth switching track by using a Gaussian pseudo-spectrum method and taking the minimum flight height decrease in the modal switching process as a target; and setting a nonlinear controller according to the kinetic model and a fuzzy algorithm, controlling the air-ground amphibious robot by using the nonlinear controller according to the optimal air-ground cross-domain path, and in addition, performing smooth switching in the flight process according to the wing folding angle smooth switching track. According to the method, an adaptive fuzzy position controller is designed to process nonlinear uncertainty in a dynamic model, and cross-domain motion planning is performed based on an energy optimal path and smooth mode switching control, so that the air-ground amphibious robot can realize stable motion and reliable operation in a cross-domain manner.

The invention relates to the technical field of unmanned aerial vehicles, and discloses a UWB-based finite time unmanned aerial vehicle cooperative control method, which comprises the following steps: acquiring relative position data of an unmanned aerial vehicle; designing a virtual piloting target, and calculating the relative position expectation of each unmanned aerial vehicle at each moment; designing a finite time controller model; designing a trajectory tracking controller model; an unmanned aerial vehicle cooperative control system is divided into an outer ring and an inner ring for respective control. According to a task flight planning application data structure, it is guaranteed that a certain distance is kept during cooperation of multiple unmanned aerial vehicle formations, nonlinear control convergence of relative trajectory errors is achieved by designing a finite time controller, the unmanned aerial vehicles are controlled to be converged to be kept at the relative distance within set time, and the unmanned aerial vehicle formations are controlled to be coordinated. Therefore, formation operation in a complex scene is realized. According to the invention, the unmanned aerial vehicle can quickly reach a task target area and realize formation keeping or changing in finite time, and task development is supported.

The invention discloses a wet-type explosion-proof multifunctional dust remover and an intelligent control system thereof, the intelligent control system comprises a processor and a memory, and the processor executes the following steps: obtaining a water level monitoring device; calculating the current water level height based on the monitoring data of the water level monitoring device; judging whether the current water level height meets a warning value condition or not; calculating the current actual flow based on the monitoring data of the water flow monitoring device; calculating a comprehensive risk value: calculating a control parameter adjustment amount through a nonlinear mapping function based on the comprehensive risk value; and controlling the operation states of a water pump, a fan and a valve according to the control parameter adjustment amount. The dust remover adopts the intelligent control system. The method has the following beneficial effects that through multi-dimensional parameter monitoring, dynamic interlocking protection and a nonlinear control strategy, the problems of existing wet dust removal equipment in the aspects of explosion-proof safety, dust removal efficiency and resource utilization are solved.

The invention belongs to the technical field of nonlinear position and attitude control of a side-rotor unmanned aerial vehicle, and aims to improve the strong nonlinear coupling effect, rotor slip flow interference and anti-interference capability of external unknown wind interference of the side-rotor unmanned aerial vehicle in a helicopter mode and realize high-precision position and attitude tracking control of the side-rotor unmanned aerial vehicle. Therefore, the technical scheme adopted by the invention is as follows: according to the adaptive robust control method for the side-rotor unmanned aerial vehicle, a nonlinear control strategy under a three-dimensional track of the side-rotor unmanned aerial vehicle is constructed by utilizing an adaptive backstepping method and robust integral symbol error control, and the nonlinear control strategy is divided into a position loop control strategy and an attitude loop control strategy; wherein a position loop control strategy is constructed by using an adaptive backstepping method, and an attitude loop control strategy is constructed by using robust integral symbol error control. The method is mainly applied to the manufacturing occasion of the side rotor unmanned aerial vehicle.

The invention discloses a hydraulic system intelligent control method considering full-state performance constraints, and the method fuses a radial basis function neural network real-time learning technology, innovatively introduces full-state preset performance function transformation, and designs a nonlinear controller considering the full-state performance constraints. Aiming at the problem of position tracking control of the hydraulic system, the method can ensure that the full-state transient performance and the steady-state performance of the hydraulic system are converged to a preset performance index range, ensures safe and reliable operation of the hydraulic system, and can learn the unknown dynamic state of the system in real time by using the radial basis function neural network to realize high-precision motion control performance. And the problem of differential explosion in traditional backstepping control of the hydraulic system can be avoided, and the influence of measurement noise on control precision is reduced.

The invention discloses an unmanned ship attitude stabilization and intelligent anti-interference method and system under a complex sea condition, and relates to the technical field of unmanned ship intelligent control and marine environment perception, and the method comprises the steps: building a sea condition model, and judging the sea surface disturbance level; performing nonlinear adaptive control on the unmanned ship based on the sea surface disturbance level; and based on a state prediction algorithm, combined control is carried out on the attitude of the unmanned ship in combination with nonlinear adaptive control. According to the method disclosed by the invention, the accuracy of attitude control pre-judgment and the environment adaptability are improved; the comprehensive performance of the unmanned ship between energy efficiency utilization and dynamic control is improved; combined control is performed by combining a state prediction algorithm and nonlinear control, so that the control system has the ability of pre-judging the attitude change trend in advance and performing dynamic correction, the control strategy conversion from passive response to active pre-control is realized, and finally, the attitude stability, the control foresight and the task continuity of the unmanned ship under the complex sea condition are remarkably improved.

The invention discloses an advertisement putting optimization method and system, equipment and a medium. The method comprises the steps of obtaining an error value between a current advertisement effect and an expected effect; according to the error value, a nonlinear PI D algorithm is adopted for calculation, and a nonlinear PI D control signal is obtained; obtaining historical data of an advertisement putting effect, and performing error prediction on the historical data by adopting a long short-term memory network to obtain error prediction data; converting the error prediction data through a nonlinear function to obtain a feedforward compensation signal; and according to the nonlinear PI D control signal and the feedforward compensation signal, advertisement putting is optimized. Compared with the prior art, the method has the advantages that the PI D algorithm is optimized by introducing the nonlinear function, and meanwhile, the LSTM (long short-term memory network) is combined for feed-forward compensation prediction, so that the response speed of the advertisement putting system is improved while the precision of the advertisement putting system is improved.

The invention discloses a nonlinear PID control method based on intelligent element driving and a fault-tolerant control system thereof, and belongs to the technical field of intelligent control. According to the method, three behavior mapping mechanisms of punishment, memory and prediction are introduced, a traditional PID controller structure is expanded, a compound error signal is constructed, and a dual-channel control strategy of instinct response and learning response is designed. The controller provided by the invention has adaptive gain adjustment capability, a dynamic adjustment law is constructed in combination with a Lyapunov stability theory, and robust control without model information is realized. Meanwhile, a fault-tolerant mechanism is provided for correcting control signal input by considering the situation of performance degradation of an actual actuator, so that the stability and tracking precision of a control system under non-ideal conditions are improved. The control method is suitable for complex nonlinear object control scenes such as underwater robots, mobile robots and parallel Delta robots.

The invention discloses a cross arm and insulator installation robot motor control method based on a T-S fuzzy model, and the method comprises the following steps: building a permanent magnet synchronous motor nonlinear model according to electromagnetism and motor dynamics; based on a sector nonlinear method, establishing a permanent magnet synchronous motor T-S fuzzy model; designing an observer to obtain a load torque estimated value; designing an L-infinity robust controller to ensure that the angular velocity of the motor can track a reference instruction; and an anti-control gain matrix solving criterion is formulated, and it is ensured that the system meets performance indexes. According to the method, the nonlinear control problem of the permanent magnet synchronous motor system is effectively solved, the influence of continuous interference signals on the system can be effectively suppressed, and the robustness of the system is enhanced.

The invention discloses a large-aperture space telescope flexible load vibration suppression method and device based on preset performance control. The method comprises the following steps: establishing a three-inertia system dynamic model for describing a large-aperture space telescope; converting the three-inertia system dynamic model into a three-inertia system state space equation; converting the three-inertia system state-space equation into a three-inertia generalized full-drive system model by adopting a full-drive system theory; adopting a pole assignment method to improve the amplitude margin and the phase angle margin of the three-inertia generalized full-drive system model; fitting response output of the three-inertia generalized full-drive system model after the amplitude margin and the phase angle margin are improved by adopting a BP neural network, and designing a preset performance function of a three-inertia system; the preset performance function is used as a tracking error boundary of the three-inertia system, proportional differential is used as a nonlinear controller for preset performance control, closed-loop control over the three-inertia system is achieved, and then vibration suppression of the flexible load of the large-aperture space telescope is indirectly achieved.

A laser power stabilizing method based on liquid crystal heating temperature control and nonlinear feedback is used for improving liquid crystal response speed and pumping laser power stability, and is characterized by comprising the following steps: step 1, carrying out physical modeling on liquid crystal according to an actual working principle of the liquid crystal to obtain a liquid crystal physical model; 2, designing a liquid crystal module heating scheme; and step 3, carrying out nonlinear controller design based on the constructed liquid crystal physical model so as to improve the laser power stability.

The application discloses an advertisement optimization method, system, device and medium. The method comprises the following steps: obtaining an error value between a current advertisement effect and an expected effect; calculating a nonlinear PID control signal by using a nonlinear PID algorithm according to the error value; obtaining historical data of the effect of the advertisement, and predicting the error by using a long short-term memory network on the historical data to obtain error prediction data; converting the error prediction data by using a nonlinear function to obtain a feedforward compensation signal; and optimizing the advertisement according to the nonlinear PID control signal and the feedforward compensation signal. Compared with the related art, the application optimizes the PID algorithm by introducing a nonlinear function, and improves the response speed of the advertisement optimization system while improving the accuracy of the advertisement optimization system by combining the long short-term memory network (LSTM) for feedforward compensation prediction.

The invention provides an anti-swing nonlinear control method and system based on a variable frequency driver, and relates to the technical field of ocean engineering equipment.The method comprises the steps that obtained system parameters of an offshore crane system are input into a nonlinear disturbance observer, and an aggregation disturbance observation value output by the nonlinear disturbance observer is obtained; determining mechanical energy, a state error vector and an anti-swing parameter of the offshore crane system based on the system parameters; processing the mechanical energy, the state error vector and the anti-swing parameter by using an energy storage function to obtain virtual energy; and inputting the aggregated disturbance observation value, the mechanical energy, the state error vector and the anti-swing parameter into a nonlinear anti-swing controller to obtain a control parameter which is output by the nonlinear anti-swing controller and corresponds to the virtual energy, so that the offshore crane system executes the control parameter. According to the invention, high-precision positioning of the load can be ensured, and swinging can be effectively inhibited.

The invention discloses an anti-parameter disturbance bidirectional converter non-linear control method and system, and the method comprises the following steps: building a three-order non-linear dynamic model of a three-phase voltage source converter, defining the reciprocal 1 / Rc of an AC side resistor R and a DC side load resistor in the non-linear dynamic model as an unknown parameter, and calculating the parameter disturbance of the three-order non-linear dynamic model; obtaining a system dynamic equation with parameter uncertainty of the three-phase voltage source converter; designing a state transformation function, linearizing a system dynamic equation, and mapping a state variable x; s30, a tracking error variable is defined as a difference value between the converted state variable and a reference track of the state variable, and the difference value is used for quantifying the deviation between the actual state and the expected state of the system; defining and designing a parameter updating law of the three estimated values, and calculating and updating unknown parameters on line according to the tracking error variable; and designing and calculating a control input law based on the tracking error variable and the three estimated values, and proving the asymptotic stability of the closed-loop system through a Lyapunov function.

The application discloses a vehicle control method, a vehicle control device, an electronic device and a readable storage medium. The method comprises the following steps: acquiring a nonlinear control quantity corresponding to a target vehicle at a current moment, wherein the nonlinear control quantity comprises an actual state quantity, an expected state quantity, an actual control input quantity and an expected control input quantity of the target vehicle; inputting the nonlinear control quantity into a target neural network to obtain a control increment, wherein the target neural network is an equivalent representation of a target function in a norm form based on a target error model, the target error model is determined based on a target dynamics model, and the control increment is a first term of a target control sequence for minimizing the target function; and performing motion control on the target vehicle according to the control increment at a next moment.

The application discloses a rocket landing control method based on multi-agent reinforcement learning in the technical field of rocket landing control methods, and the rocket landing control method comprises the following steps: establishing a rocket engine model, collecting and outputting necessary state parameters in the working process of a liquid rocket engine; establishing a vertical landing simulation model of a carrier rocket, collecting and outputting necessary parameters in the vertical landing process of the carrier rocket; defining a state space, an action space and a reward function in the engine starting process; improving on the basis of the MADDPG algorithm to realize the LCO-MADDPG algorithm; and designing, training and evaluating the MARL model. The rocket landing control method realizes nonlinear control of the vertical landing process control of the carrier rocket by means of intelligent control methods without the need of designing complex control logic.

A system and method for providing nonlinear control of a generator, comprising: a linear voltage controller and a nonlinear current controller, wherein the linear voltage controller is implemented on a microcontroller to perform: determining a reference angle for an output voltage from a first generator using a phase-locked loop; converting the reference angle and the generator output voltage from the time domain to an orthogonal reference system to generate two rotating coordinate system reference voltages; calculating the root mean square voltages of the two rotating coordinate system reference voltages and converting the root mean square voltages into an output current IfSet1; providing IfSet1 to the nonlinear current controller; and wherein the nonlinear current controller determines a control action based on the difference between IfSet1 and the output current IfFBK of a second generator.

The present invention provides a network coverage optimization method, system, and storage medium for fire monitoring. The network coverage optimization method includes the following steps: Step A, using a nonlinear control strategy and a Lévy flight mechanism to improve the SOA algorithm to obtain an improved ISOA algorithm; Step B, combining the ISOA algorithm with the PSO algorithm to form an ISOAPSO algorithm. The ISOAPSO algorithm includes inner-layer optimization and outer-layer optimization. When solving the network coverage optimization problem for fire monitoring, the inner-layer ISOA algorithm is first used to optimize sensor nodes. The sensor nodes optimized by the ISOA algorithm are then transferred to the outer-layer PSO algorithm for secondary optimization, thereby achieving the purpose of improving network coverage. The beneficial effect of the present invention is that the network coverage optimization method of the present invention can better solve the network coverage optimization problem for fire monitoring and enhance fire monitoring capabilities.

Systems and method for providing non-linear control of a generator including: a linear voltage controller and a non-linear current controller, wherein the linear voltage controller is executed on a microcontroller to perform: determining a reference angle of an output voltage from a first generator using a phase lock loop; converting the reference angle and the generator output voltage from a time domain to an orthogonal reference frame to produce two rotating frame reference voltages; calculating a root mean square voltage of the two rotating frame reference voltages and converting the root mean square voltage to an output current IfSet1; providing IfSet1 to the non-linear current controller; and wherein the non-linear current controller determines a control action based on a difference of IfSet1 and an output current of a second generator IfFBK.

The application discloses a wheel-legged robot motion control method, system and device facing an unstructured environment, a storage medium and computer equipment, and relates to the technical field of robot control. The control method comprises the following steps: constructing a simulation model of a wheel-legged robot, building a learning training scene of the wheel-legged robot, and training a nonlinear controller model by using a reinforcement learning algorithm; converting the nonlinear controller model into a nonlinear controller which can be deployed and run on an actual physical prototype platform; acquiring current motion state data of the wheel-legged robot and inputting the current motion state data into the nonlinear controller to obtain expected joint positions and expected wheel speeds of the wheel-legged robot; and inputting the expected joint positions and the expected wheel speeds into a low-level controller to output joint torques for controlling the wheel-legged robot to perform a control task. The application not only greatly shortens the development cycle of the wheel-legged robot controller, but also improves the robustness, dynamics and compliance of the wheel-legged robot in an unstructured environment.

The invention provides a micro-droplet multi-dimensional control device and system based on photoelectric cooperative control, and relates to the field of biological microfluidics. The invention provides a micro-droplet multi-dimensional control device based on photoelectric cooperative control. The micro-droplet multi-dimensional control device comprises a photoinduced charged film and an LED addressing module arranged below the photoinduced charged film. The LED addressing module comprises a PCB (Printed Circuit Board), a plurality of LED assemblies are arranged on the PCB, and each LED assembly comprises an LED and an LED coil connected with the LED; the LED addressing module is used for emitting light and driving the photoinduced charged film to generate gradient charges. According to the invention, gradient charges can be generated on the surface of the photoinduced charged film through lighting of the LED, so that the movement of liquid drops is driven, unmarked liquid drop control is realized, the space limitation of a traditional fixed light trap system is broken through, complex trajectory planning of the liquid drops is realized, and nonlinear regulation and control of light intensity gradient and surface wettability are realized.