121results about How to "Improve tracking accuracy" patented technology

The invention discloses a precise position tracking control algorithm based on real-time iterative compensation, and the algorithm comprises the following steps: taking a single axis of an X-Y motion platform as a research object, and obtaining a transfer function of the X-Y motion platform through parameter identification; the position tracker adopts a discrete PID (proportion integration differentiation) feedback control algorithm to obtain a feedback control output signal; an FIR (finite impulse filter) parameterization fixed structure controller is adopted to carry out feedforward control; a disturbance observer module is designed, disturbance observer output is compensated to control input, and a low-pass filter is introduced; and performing real-time iterative compensation control based on a Kalman filter. The Kalman filter is introduced into a real-time iterative compensation strategy provided by the invention, so that the noise influence of the system can be inhibited to a certain extent, and the anti-interference capability and the tracking precision of the system are further improved; in combination with a gating recursive unit prediction result and a model state space equation recursive prediction result, a better noise processing capability is provided by estimating a system state and a noise covariance.

The invention provides an intelligent vehicle steer-by-wire motor control method. A steer-by-wire motor adopts double closed-loop control of a corner loop and a current loop; the corner loop is controlled by an improved nonlinear active-disturbance-rejection controller, and the current loop is controlled by a fractional order PID (Proportion Integration Differentiation) controller; the improved non-linear active disturbance rejection controller comprises an improved third-order non-linear extended state observer and a non-linear state error feedback control law, wherein the non-linear state error feedback control law adopts super-spiral second-order sliding mode control. According to the method, the disturbance resistance and robustness of the steer-by-wire motor system are enhanced, the estimation precision of the observer is improved, and the tracking performance of the steer-by-wire motor is improved.

The invention discloses an intelligent laser welding workstation for a high-voltage cable. The intelligent laser welding workstation comprises an operation table, a mechanical arm, a welding head, a camera assembly, a power assembly, a clamping assembly and a control piece. A control part is installed on the outer side of the operation table and comprises an image acquisition module, an image processing module, a track planning module, a motion control module, a quality detection module, a parameter adjustment module and a synchronous control module. Visual guidance and real-time monitoring in the welding process are achieved through the camera assembly, a welding center point is accurately positioned through an image processing algorithm, the track planning module generates a smooth welding path, and the parameter adjusting module dynamically adjusts welding parameters according to a molten pool image. The synchronous control module receives a production line signal to realize full-process automation of automatic clamping, welding and loosening; efficient and stable laser welding seamlessly integrated with the cable manufacturing process is achieved, and the automation degree, welding precision and product reliability of a production line are improved.

The invention discloses a vehicle-mounted target tracker based on computer vision, and relates to the technical field of computer vision, and the vehicle-mounted target tracker comprises a sensor data collection module which is used for collecting real-time data from various sensors; the data fusion module is used for integrating the data from the plurality of sensors of the sensor data collection module and generating comprehensive information about the environment and the target; the data processing module is used for processing the complex data, extracting features and carrying out target identification and classification; according to the invention, by fusing the environment information and the target information, a more comprehensive and accurate context environment is provided for the system, the current scene can be understood more accurately, the authenticity of the target object can be judged more accurately, so that a more reasonable decision can be made, and the accuracy of the system is improved. And through combined use of an advanced target tracking algorithm, the precision, robustness and real-time performance of target tracking can be significantly improved.

The invention belongs to the technical field of target constraint tracking, and discloses a target modeling and tracking method based on elliptic trajectory constraint. The method comprises the following steps: generating elliptical orbit target motion data, and generating measurement data through a sensor; analyzing constraint conditions met by the motion of the elliptical orbit target; establishing a tracking model; introducing the elliptic trajectory constraint information as pseudo measurement into unscented Kalman filtering, and performing unscented Kalman filtering processing; and performing state estimation, and estimating an error covariance to obtain an observation result. According to the method, modeling is carried out on the motion of the elliptical orbit to describe the motion of the elliptical orbit, constraints required by a target moving along the elliptical orbit are defined, constraint conditions are incorporated into a tracking process, the constraints are integrated into unscented Kalman filtering as additional pseudo-measurements, and simulation experiments prove that the tracking precision can be greatly improved; and on the other hand, a powerful framework is established for improving the effectiveness of an elliptical target tracking mechanism.

The invention discloses a pulse signal source tracking method based on a robust Kalman filter. The method comprises the following steps: reading a target state vector and error covariance matrix at a previous moment, and observing a noise covariance matrix; a variational Bayesian parameter before iteration, a target state vector and an error covariance matrix are initialized; starting iteration, and calculating the quadratic form sufficient statistics of the observation error and the forecast error; updating variational Bayesian parameters; updating the forecast error covariance matrix and the observation noise covariance matrix; calculating a target state vector and an error covariance matrix of the iteration; when the variation of the target state vector of the current iteration and the target state vector of the last iteration is smaller than the tolerance, ending the iteration, and outputting the target state vector at the current moment, the error covariance matrix and the observation noise covariance matrix; compared with a deterministic integral sampling method, the method provided by the invention adopts adaptive volume sampling to realize higher tracking precision under lower calculation cost.

The invention relates to a full-link differentiable physical enhancement type automatic driving control method and a full-link differentiable physical enhancement type automatic driving control system. The method comprises the following steps: constructing a full-link differentiable PERL dynamical model to reconstruct real vehicle dynamics in a high-fidelity manner; establishing a micro-preview PID execution layer, calculating a transverse error and a longitudinal speed error based on a dynamic preview distance, and generating a steering wheel angle instruction and an automobile longitudinal acceleration instruction by using a PID control law supporting gradient return; and the PID gain parameter and the preview distance are adaptively adjusted in real time by using the SAC network according to the current vehicle state. According to the full-link micro closed-loop training architecture designed by the invention, end-to-end optimization of control parameters is directly guided by utilizing gradient information of dynamic residual errors, the problem of distortion of a Sim2Real model is effectively solved, the physical interpretability and smoothness of a control strategy are ensured, and meanwhile, the optimal optimization of the control parameters is realized. The trained strategy network significantly improves the path tracking precision and robustness of the vehicle under a complex working condition.

The invention discloses an intelligent agricultural machine trajectory tracking control method based on adaptive model prediction. The method comprises the following steps: establishing a discrete linear state space equation based on an agricultural machine kinematics tracking error model; a fuzzy controller is designed by taking the lateral error and the vehicle speed as input, adaptive adjustment is performed on a prediction time domain, a current optimal prediction time domain is calculated, and the trajectory tracking precision is improved; inputting the optimal prediction time domain into a prediction control model of the controller, designing an objective function according to the model, and converting a solving process into a constrained quadratic programming problem; the target function of the model prediction controller is solved, a first variable of an optimal solution is obtained, the variable serves as the control quantity increment at the current moment, and the control input of the agricultural machine is adjusted. Aiming at the problem that a fixed prediction time domain model prediction control algorithm is difficult to meet the path tracking accuracy requirement during trajectory tracking control, the trajectory tracking accuracy and the site adaptability in the agricultural machinery operation process are improved to the maximum extent.

This invention relates to the field of multi-UAV cooperative control and target detection technology, specifically to a multi-UAV cooperative search method for underwater targets, comprising: acquiring a probability distribution heatmap; determining UAV parameters and sensor parameters, and establishing the fitness of a genetic algorithm based on the time taken for multiple UAVs to complete all search tasks, total flight time, weights of high-risk hotspots, and arrival times at each target hotspot; optimizing the genetic algorithm based on the probability distribution heatmap and the fitness to obtain an optimal task allocation sequence; performing Dubins transition and spiral search based on the optimal task allocation sequence, the minimum turning radius of the UAVs, and the maximum allowable distance between adjacent routes to obtain a flight path; and for each UAV, tracking the flight path based on the real-time position of the UAV and the eccentricity of the flight path. This invention can improve the anti-interference capability and efficiency of multi-UAV cooperative target detection.

When a tool (T) is moved along a target trajectory (P) provided on a main surface of a workpiece (W), detection regions (A1, A2) through which the target trajectory (P) passes are set at at least two locations around the tool (T) by using a trajectory detection device (S) that moves together with the tool (T), a target position (Pt) of the tool (T) is determined on the basis of positions (P1, P2) of the target trajectory (P) detected in the detection regions (A1, A2), and the target position (Pt) of the tool (T) is determined on the basis of the positions (P1, P2) of the target trajectory (P). By means of the route tracking method, even if the target trajectory (P) is blocked by the tool (T), the tracking accuracy can be improved.

The invention relates to a high and low orbit satellite tracking method based on geomagnetic correction. The method comprises the following steps: constructing a geomagnetic model, calculating an azimuth angle and carrying out magnetic field interference analysis; carrying out factory calibration by utilizing a three-dimensional geometric calibration mode, including horizontal correction and vertical correction; the current azimuth angle and pitch angle are continuously corrected based on the carrier coordinate system by using the signal intensity, and the deviation is obtained so as to continuously approach the real angle; utilizing the known coordinates of the three-dimensional space to inversely calculate the current position; and the current data is corrected by using the big data, so that the current data meets the use requirements. The current attitude angle and the satellite signal receiver are used for tracking the target satellite, and then the known azimuth angle is used for correcting the geomagnetic data, so that the azimuth angle precision is improved, the cost of a phased-array antenna or a mechanical antenna is reduced, the tracking precision is improved, and the tracking speed is improved.

This application relates to a method and apparatus for time-resolved electrochemical Raman spectroscopy. The method includes: acquiring a delay time parameter, potential waveform data, and an initial trigger signal; performing event trigger detection on the initial trigger signal to obtain an event trigger timestamp; at the event trigger timestamp, converting the potential waveform data based on the initial trigger signal to obtain a potential pulse signal; the potential pulse signal is used to indicate the initiation of an electrochemical reaction; at the event trigger timestamp, determining the laser trigger delay amount corresponding to the potential pulse signal based on the delay time parameter; at the event trigger timestamp, transforming the potential pulse signal based on the laser trigger delay amount to generate a laser trigger pulse signal; wherein the laser trigger pulse signal is used to indicate the emission of a pulsed laser; acquiring the reaction current signal corresponding to the electrochemical reaction and the Raman spectrum corresponding to the electrochemical reaction under pulsed laser irradiation. This method can improve the tracking accuracy of rapid electrochemical processes.

This application discloses a method, apparatus, storage medium, and product for controlling the current of a charge-and-discharge power supply. The method includes acquiring a target adjustment command, which includes a target current increment. This target current increment is the difference between a first preset current and a second preset current. The first preset current is the target current of the previous adjustment cycle, and the second preset current is the target current of the current adjustment cycle. The method also includes acquiring a voltage increment based on multiple target parameters and the target current increment. These multiple target parameters include the battery electromotive force, battery internal resistance, and line impedance of the charge-and-discharge power supply when it was in a stable state in the previous adjustment cycle. Finally, the method includes acquiring an adjustment amount based on the voltage increment and adjusting the output current of the charge-and-discharge power supply based on the adjustment amount. This method can improve the response speed while ensuring the stability of the output current of the charge-and-discharge power supply.

The application discloses a multiscale target tracking method based on scale and position separation, comprising the following steps: inputting a template image and a current frame search image into a multiscale network respectively for feature extraction, and obtaining multiscale template features and target features correspondingly; inputting the template features and the target features into a position estimation detection head respectively for processing, and performing convolution on two output results to obtain a preliminary position estimation result; inputting the target features into a scale estimation detection head for processing to obtain a scale estimation result; clustering the preliminary position estimation result to obtain a final position estimation result; splicing the final position estimation result and the scale estimation result to obtain a prediction result; when judging that the number of processed search image frames reaches a preset number, updating the template features, and performing prediction on a next frame search image. The method improves the stability and robustness of the whole tracking process, and has high tracking accuracy.

The application discloses a kind of dual-frequency multi-component joint tracking method, equipment and medium of navigation satellite signal.The method comprises: according to the upper sideband carrier and the lower sideband carrier in dual-frequency navigation satellite signal as local carrier, tracking dual-frequency navigation satellite signal is respectively carried out, and the upper sideband and the lower sideband multi-component correlation value are obtained respectively, the carrier phase error, code phase error, subcarrier phase error of the upper sideband carrier and the lower sideband carrier obtained by jointing the upper sideband and the lower sideband multi-component correlation value obtained respectively are obtained, and the carrier phase, code phase, subcarrier phase of dual-frequency navigation satellite signal are obtained.The technical scheme of the embodiment of the application can improve the tracking precision of navigation satellite signal.

The application discloses a kind of omnidirectional mobile carrying platform adaptive path tracking control method and system based on multi-mode switching, including establishing three-mode target steering kinematics model;Fuzzy reasoning rule base based on lateral motion intensity factor is constructed;According to the current platform longitudinal velocity, current path curvature and fuzzy reasoning rule base, determine the optimal rotation mode;Establish pre-aim error model and error dynamics equation, design integral sliding mode lateral controller, obtain the desired yaw angular velocity;According to the optimal rotation mode and the desired yaw angular velocity, obtain four-wheel target speed;Based on quintic polynomial, longitudinal velocity planning is carried out, and according to four-wheel target speed, path tracking control is carried out on omnidirectional mobile carrying platform.The application can effectively reduce lateral tracking error and heading angle error, significantly improve the tracking accuracy of omnidirectional mobile platform under variable curvature curve, cargo transportation stability and narrow space passability, and can be widely applied to carrying platform motion control technical field.

The application discloses an anti-unwinding flexible spacecraft attitude tracking composite control method, first, based on the quaternion method, a flexible spacecraft attitude tracking error kinematics and dynamics model is established; then, the flexible accessory vibration mode, external environmental disturbance and model uncertainty are regarded as a lumped disturbance, a generalized proportional integral observer is designed, and the lumped disturbance is observed and estimated; finally, a non-singular terminal sliding mode surface is constructed, the lumped disturbance observation and estimation compensation of the generalized proportional integral observer is taken as a feedforward signal, and a feedforward-feedback anti-unwinding attitude tracking composite controller is designed. Through determining appropriate control gain, the closed-loop attitude tracking error system is globally finite time stable, and the anti-unwinding effect can be realized. The application can realize finite time attitude tracking of the flexible spacecraft, has high attitude tracking precision and strong robustness.

A tracking method, device and system of a target object, medium and computing device, the method comprising: acquiring a video frame comprising a target object and an initial pose of the target object when the video frame is collected; acquiring a three-dimensional model of the target object; based on the video frame, acquiring predicted pose information of the target object through a pre-trained neural network; based on the initial pose and the three-dimensional model, acquiring detection pose information of the target object; matching the predicted pose information and the detection pose information to obtain a matching result; and tracking the target object based on the matching result.

This invention discloses a LiDAR point cloud data completion system and method based on a PCN model, belonging to the field of point cloud data processing technology. It is used to complete LiDAR point cloud data with complex occlusion. The system includes a data loading module, a data processing module, a data completion module, a model training and optimization module, and a data visualization module. The method includes acquiring raw point cloud data files, reading and preprocessing multi-format point cloud files, constructing a dataset, building a point cloud data completion model based on a PCN model, training and validating the model based on the dataset, acquiring a weight file, and performing data completion on point cloud data with complex occlusion based on the weight parameters. This invention solves problems such as poor data format compatibility of existing PCN models, numerical overflow or training instability when the point cloud data coordinate range is large, and low robustness of point cloud file matching and loading, thereby improving the accuracy of sparse point cloud data completion.

The application discloses a path tracking control method and device of a vehicle, the vehicle and a medium. The method comprises the following steps: traversing each path node in a reference path, obtaining a current vehicle speed and a current pose of a current path node; determining a preview distance corresponding to the current vehicle speed based on a preset mapping rule; and predicting a predicted pose of the vehicle at a next path node according to the preview distance. According to the current pose and the predicted pose, it is determined whether the vehicle deviates. If not, the next path node is traversed. If yes, the current vehicle speed is reduced to obtain a target vehicle speed, and the next path node is traversed. When the last path node is traversed, a vehicle speed sequence of each node is generated, and the vehicle is controlled to track and drive based on the vehicle speed sequence. Thus, the preview distance and the vehicle speed are dynamically associated, the future pose change of the vehicle is predicted in real time, and whether there is a deviation risk is identified in advance. If there is, the deviation of the vehicle in the transverse direction is corrected through longitudinal speed reduction, and accurate tracking is realized.

The utility model discloses a bearing seat and tracking support relates to photovoltaic power generation technical field, and this support includes main beam, bearing seat, photovoltaic bearing and non -driven stand. Bearing seat is enclosed by the stamping cover plate and stamping bearing base of mutual adaptation, and the inside holds photovoltaic bearing, and the stamping bearing base is combined by two same stamping sub -units, and the bottom is connected with non -driven stand. Photovoltaic bearing is integral type spherical high molecule structure, and the sleeve joint is in main beam outside, and its spherical outer contour is adapted with bearing seat inner wall, can realize main beam angle adjustment, and the stamping sub -unit has H type and Y type two, is connected with non -driven stand wing plate or directly with waist board through corbel adapter, and the bearing seat axial both ends are equipped with flanging, and the base is equipped with the strip. The utility model discloses through stamping structure reduces the cost, shortens the period, and realizes flexible angle -adjusting with the help of spherical bearing, and the adaptability is strong, is applicable to large -scale photovoltaic power station.

The invention discloses a three-level T-type inverter grid-connected control method based on a sliding-mode observer, and relates to the technical field of grid-connected control. According to the three-level T-type inverter grid-connected control method based on the sliding-mode observer, a preset time sliding-mode control scheme based on the sliding-mode observer is provided, a continuous Sigmoid function is introduced to replace a nonlinear sign function to construct a sliding-mode observation reaching law, signals can be effectively and smoothly controlled, and the control precision of the three-level T-type inverter grid-connected control method based on the sliding-mode observer is improved. The inherent high-frequency buffeting phenomenon of traditional sliding mode control due to a sign function is remarkably suppressed, and meanwhile, the convergence speed and tracking precision of the system are improved. On the basis of observing power grid voltage serving as external disturbance through a sliding-mode observer, feedforward compensation is performed on the external disturbance, and the feedforward compensation is applied to a sliding-mode control reaching law of preset time so as to suppress buffeting and accelerate the convergence process of output current. And the timeliness and the stability of grid-connected control of the three-level T-type inverter based on the sliding-mode observer are improved.

The present application provides a control method and related equipment for a phase-shifted full-bridge converter, the method comprising: obtaining an inductor current value at the start time of a current switching cycle of an output filter inductor, and a duty cycle at the start time of a previous switching cycle of a secondary side equivalent circuit on a secondary side rectifier side; based on a first lumped disturbance estimation model, obtaining a first inductor current estimation value and a first lumped disturbance estimation value according to the inductor current value at the start time of the current switching cycle and the duty cycle at the start time of the previous switching cycle, wherein the first lumped disturbance estimation model is constructed according to a linear extended state observer; based on a second lumped disturbance estimation model, obtaining a lumped disturbance total estimation value at the start time of the current switching cycle according to the first inductor current estimation value and the first lumped disturbance estimation value, wherein the second lumped disturbance estimation model is constructed according to an extended state observer; and controlling the conduction of a switch tube in a primary inverter side of a next switching cycle based on the lumped disturbance total estimation value.

This invention relates to the field of fast tool servo control technology, and in particular to a dual-stroke fast tool servo control system. It comprises a two-stage displacement control system with two identical displacement controllers. Each displacement controller consists of a parallel PI control module and an error compensation module. The PI control module generates a primary adjustment control signal based on the tracking error signal; the error compensation module includes an inverse function module and a time delay module. The former restores the error displacement drive signal, and the latter acquires historical displacement drive signals. The two-stage controllers control their respective displacement components, gradually reducing the tracking error. Initially, the PI control module uses a proportional term for rapid correction to ensure initial tracking accuracy of the high-frequency signal; the error compensation module directly compensates for the current error without delay, gradually learning to approximate the desired signal as the error converges. The two modules work together to avoid the delay problem of the integral term, ensuring continuous high-precision tracking of high-frequency periodic signals and improving machining accuracy.

The invention discloses a modular photovoltaic shed power station rapid assembling and supporting device, and belongs to the field of photovoltaic power stations, the modular photovoltaic shed power station rapid assembling and supporting device comprises a bottom plate, an electric rotating disc is arranged at the upper end of the bottom plate, a mounting seat is fixedly connected to the upper end of the electric rotating disc, a mounting rod is rotatably connected to the upper end of the mounting seat, and the outer wall of the mounting rod is fixedly connected to a mounting frame; a photovoltaic panel is arranged in the mounting frame; the upper end of the electric rotating disc is fixedly connected with a fixing plate, and the outer wall of the fixing plate is fixedly connected with a motor. According to the invention, the motor drives the gear and gear block linkage mechanism, thereby achieving the automatic adjustment of the inclination angle of the photovoltaic panel, and effectively improving the sun tracking precision and power generation efficiency. Meanwhile, a pure mechanical wind power induction and disconnection protection mechanism is utilized, a driving part is automatically separated in strong wind, the photovoltaic panel is prevented from being located at a high-risk windward angle, power generation optimization and passive safety protection functions are achieved, extra energy consumption is not needed, and the structure is simple and reliable.

The invention relates to the technical field of integrated navigation, in particular to a positioning method and system based on optical angle measurement and astronomical-inertial integrated navigation. According to the method, firstly, a star image and a navigational satellite image are obtained at the same time by shooting a star map, after recognition and processing, the attitude of a camera relative to a geocentric inertial coordinate system is calculated by utilizing the star, and then the coordinate of the navigational satellite in the coordinate system is determined. And obtaining angle information of the navigation satellite relative to the observer under the earth coordinate system through coordinate transformation. Optical angle measurement information is fused with attitude, speed and position information output by an inertial navigation system, a Kalman filtering state equation and an observation equation are constructed, and finally real-time high-precision calculation and updating of the position of the observer are achieved. According to the method, the accuracy of optical angle measurement, the limited distance reference of celestial navigation and the autonomous continuity of inertial navigation are fused, and the navigation positioning accuracy and reliability under the complex environment and the satellite denial condition are effectively improved.

The application discloses a control strategy based on combination of a pre-filter friction compensator and improved repetitive control, and specifically comprises the following steps: step 1, a pre-filter friction compensator is added to a P / PI cascade controller to improve the anti-friction interference capability of a driving system; step 2, a transfer function of an output voltage of the driving system and a tracking error is determined; step 3, specific parameters of a LuGre model in the pre-filter friction compensator are obtained; step 4, a repetitive controller is designed; step 5, an action time period of the repetitive controller determined in step 4 is set at motion commutation to obtain a segmented repetitive control; and step 6, the segmented repetitive control obtained in step 5 is combined with the designed pre-filter friction compensator to form an improved repetitive control framework. The method combines the pre-filter friction compensation control with the segmented repetitive control, greatly improves the error and convergence rate at the motion commutation of each cycle, and reduces the influence of the friction interference on the product processing quality.

This invention discloses a label-free real-time blood vessel tracking method driven by dynamic blood oxygenation features, relating to the fields of medical image processing and computer vision. The method includes: acquiring dynamic blood oxygenation signals from a target tissue region; preprocessing the blood oxygen concentration change data; constructing a spatiotemporal matrix of blood oxygenation features; performing adaptive feature extraction using an improved 3D convolutional neural network; establishing a multi-scale blood vessel contour prediction model; dynamically predicting and updating the blood vessel contour using a Kalman filter algorithm; outputting the real-time trajectory of the blood vessel center and boundary contours; and introducing a dynamic feedback mechanism to achieve continuous and stable blood vessel tracking. This invention achieves highly secure blood vessel tracking, significantly improving tracking accuracy, robustness, and real-time performance under tissue movement and complex background interference by utilizing dynamic blood oxygenation features.

This invention relates to the field of autonomous navigation technology for indoor mobile robots, specifically disclosing a SLAM method for indoor mobile robots, comprising: acquiring environmental image information of the indoor mobile robot; performing ray sampling and attention-based encoding fusion on an RGB-D image sequence to obtain first optimized parameter information; performing pose optimization processing on the initial pose information according to a target total loss function to obtain tracked frames with pose information; determining keyframes for mapping based on the tracked frames; performing ray sampling and attention-based encoding fusion on the keyframes to obtain second optimized parameter information; and performing joint pose and mapping optimization processing on the second optimized parameter information according to the target total loss function to obtain pose and map construction results. The SLAM method for indoor mobile robots provided by this invention can improve positioning accuracy in complex indoor environments.

This invention discloses an artificial intelligence-based photovoltaic (PV) tracking bracket method, relating to the field of solar photovoltaic power generation technology. The method includes: collecting multi-dimensional environmental perception data and uploading it to an edge computing node; predicting the irradiance distribution for the next 30 seconds using a convolutional neural network and a long short-term memory network; inputting the prediction results, solar angle, and atmospheric attenuation coefficient into a lightweight fully connected neural network to generate a dual-axis tracking angle command; dynamically correcting the angle using a gradient ascent algorithm in conjunction with PV output power feedback; and performing closed-loop regulation through a low-latency CAN bus-controlled servo drive unit. This invention aims to solve the problems of existing PV tracking systems, such as lag response, large control deviation, reliance on high-cost sensors, and lack of multi-dimensional environmental fusion perception capabilities under complex weather and unsteady illumination conditions. It achieves high-precision, robust real-time tracking with low-density sensor configuration, significantly improving power generation efficiency and system adaptability.