120results about How to "Improve tracking performance" patented technology

The invention discloses an autoregressive visual target tracking algorithm based on token fusion, and the algorithm specifically comprises the following steps: 1, obtaining a public visual target tracking data set, and converting the data set into a central coordinate format; 2, preprocessing an initial template of a given video sequence, an online template at the current moment and a search image to obtain visual embedding of the initial template, a dynamic template and the search image; the method comprises the following steps: preprocessing an angular coordinate sequence of a target frame in previous historical N frames of a current search image of an input video to obtain historical track word embedding of the current search image; 3, constructing an autoregressive visual target tracking model based on token fusion, and designing a loss function; step 4, obtaining a final autoregressive visual target tracking model based on token fusion; the method solves the problem of insufficient model feature extraction capability caused by insufficient utilization of spatio-temporal information and redundant background information of a search area in an existing method.

The invention provides a servo hydraulic system optimization control method and system for a press machine test platform, and relates to the technical field of press machines, and the method comprises the steps: building a state space expression and building a mathematical model of a servo hydraulic system based on dynamic characteristics by deducing mathematical expressions of all components of the system; building a predictive control algorithm based on the mathematical model, solving a quadratic programming problem by adopting a rolling optimization strategy through building a state space predictive model and determining a multi-objective optimization function to obtain an optimal control sequence, and taking a first element of the optimal control sequence as control input at the current moment; feedforward compensation and pressure feedback compensation are introduced, the feedforward compensation constructs a compensation model based on an inverse system of a press transfer function and pre-corrects a control signal, and the pressure feedback compensation designs a digital filter based on a pressure error, fuses a feedforward compensation signal with a pressure feedback compensation signal and superposes the fused signal with the control signal; and obtaining a final control signal.

The invention relates to the technical field of camera adjustment, in particular to a camera adaptive adjustment method and system and a storage medium. The method comprises the following steps: acquiring a multi-source environment sensing data set and an initial environment image; performing environment feature recognition on the initial environment image according to the multi-source environment sensing data set to obtain a scene environment feature map; performing semantic scene segmentation on the scene environment feature map to obtain a semantic scene macroscopic feature map; performing time sequence attention target tracking on the scene environment feature map to obtain a target vehicle motion feature map; performing feature constraint fusion on the semantic scene macroscopic feature map and the target vehicle motion feature map to obtain a target vehicle-scene motion semantic map; and performing parameter adjustment on the camera according to the target vehicle-scene motion semantic map to obtain a camera parameter adjustment strategy set. According to the invention, the performance and the image quality of the camera in a changeable environment are improved.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention discloses an unmanned aerial vehicle target intelligent identification and perception system and method based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting the importance feature of each target region, calculating a region importance score, generating a target region monitoring plan, and obtaining continuous frame images; an improved YOLOv5 model is adopted, continuous frames of images are input, a bounding box of a suspected target wild animal is output and mapped to an actual geographic coordinate system, and actual position coordinates are obtained; key points of the t-th frame image are detected, coordinates of the key points are extracted, visibility scores and confidence coefficients are calculated, and feature descriptors are formed; performing target detection and key point detection on the (t + 1) th frame of image to obtain actual position coordinates and feature descriptors of candidate targets, performing track matching, updating the actual position coordinates and the feature descriptors of the suspected target wild animals if the actual position coordinates and the feature descriptors are matched, and defining new target animals if the actual position coordinates and the feature descriptors are not matched; counting according to the track matching result to obtain the total number of the target wild animals.

The invention discloses a target detection tracking method and system based on automatic detection, and relates to the technical field of target detection tracking, and the method comprises the following steps: carrying out the target position and category labeling of an image through a labeling tool based on an image data set of a typical target, and obtaining a training data set with a label, establishing a target detection model according to a YOLO architecture, and introducing a dynamic weighted loss function; according to the training data set, a training batch is generated by using a hierarchical sampling strategy, an optimized training sample is obtained through an online difficult case mining mechanism, and iterative training is performed on the target detection model based on the generated training batch and the optimized sample; and performing target detection on the real-time video stream by using the trained target detection model to obtain the tracking position and category information of each frame. According to the method, the problem of class imbalance can be effectively solved, the detection capability of rare targets is improved, and meanwhile, the overall performance of the model is improved through difficult case mining.

The invention relates to the technical field of laser radars, in particular to an unmanned vehicle road obstacle sensing system based on a laser radar, and the system comprises a laser radar data collection module which adjusts the scanning frequency of the laser radar, collects point cloud data from static and dynamic obstacles, and obtains preliminary scanning data; and filtering the preliminary scanning data, and removing noise data. According to the invention, by calculating the dynamic characteristics of the point cloud data, the key point and the moving track of each obstacle are extracted, and the ability of sensing the obstacles in the dynamic environment is optimized. By adopting a time sequence analysis method, the moving state and track of the obstacle can be directly deduced according to the point cloud change, so that the ability of tracking the moving obstacle in a complex dynamic scene is improved. In the spatial reconstruction process, geometric subdivision is utilized to reconstruct point cloud data, and dynamic information is combined with a static point cloud structure, so that the analysis capability of spatial data is enhanced.

The invention provides a target tracking method and device for a fast moving small target, and the method comprises the steps: monitoring the position change of a target in continuous frames of an original target tracking data set, and estimating the motion speed of the target through calculating the difference of the positions of the target of adjacent frames; dynamically adjusting the sampling probability of the video frame according to the target movement speed; according to the adjusted sampling probability of each frame, probability resampling is carried out on the original target tracking data set, and a fast moving target tracking training set is generated; determining hyper-parameters of a speed sensitive loss function based on image features of the fast moving target tracking training set; and performing iterative optimization training of the target tracking model by using the fast motion target tracking training set and the speed sensitive loss function to obtain an optimized target tracking model suitable for the fast motion small target. The problems of inaccurate tracking, loss and the like in a high-speed moving small target tracking process in the prior art can be solved.

The invention provides a low-energy-consumption three-dimensional point cloud single-target tracking method based on a spiking neural network, and the method comprises the steps: obtaining the three-dimensional point cloud data of a previous frame and a current frame of a target, designing a voxel grid module, and dividing the point cloud into 3D voxels; a feature extraction module is designed, a three-stage pulse convolution block is used as a feature extractor, voxelization point cloud features are extracted, a Transform encoder layer is added, and global context information of the point cloud is captured through a self-attention mechanism; a part-to-part motion modeling module is designed, continuous point cloud features on a time sequence are modeled and coded part by part through a two-dimensional pulse convolution block, and feature information changes of a previous frame and a current frame in corresponding space parts are obtained; and designing a regression prediction module, and inferring the relative motion of the target through a pulse regression prediction module. According to the method, energy consumption is reduced, efficiency is improved, and meanwhile it is guaranteed that the network accurately tracks the target in diversified scenes and motion states.

The invention belongs to the technical field of force / position hybrid control of mechanical arm systems, and particularly discloses a finite-time self-adaptive impedance control method and system for a flexible joint mechanical arm system. In order to solve the problems that the control effect of a mechanical arm system is affected by nonlinear and unknown system model parameters at present and derivation is complex in backstepping design, the fuzzy self-adaptive finite time impedance controller is designed based on the impedance control technology so as to achieve hybrid control over the track and force of the tail end of the mechanical arm. In the design process of the controller, a real control law is constructed to realize finite time stability of the system; and meanwhile, command filtering and fuzzy self-adaptive technologies are introduced to solve the problems of calculation explosion in backstepping design and unmodeled dynamics of a mechanical arm system. By means of the control method, the influence caused by system uncertainty can be effectively overcome, the position tracking error of the mechanical arm system is reduced, and meanwhile it can be guaranteed that all signals in a closed-loop system are bounded within finite time.

The invention discloses an automatic driving path planning and tracking control method, which belongs to the technical field of automatic driving and comprises the following steps of: 1, generating a global path and a tracking path according to a current position and a planned target point in a vehicle driving process; 2, constructing a local cost map by adopting a multi-sensor fusion method, carrying out local path planning, and generating a vehicle real-time obstacle avoidance path; 3, evaluating and selecting a tracking path, an obstacle avoidance path and a lane keeping and changing path, and deciding an optimal local path; 4, performing transverse / longitudinal control on the vehicle by using a path tracking transverse / longitudinal control system; the method has the advantages that the tracking path conforming to the kinematics law can be planned for the vehicle, the vehicle can autonomously run according to the set speed and path according to the expected path output by the planning module and under the cooperation of the integrated navigation system, and the problems that in the prior art, single vehicle path planning and tracking control cannot be achieved are solved.

The invention discloses an on-line estimation method and system suitable for cluster type intelligent electric meter error analysis, and the method comprises the steps: building an intelligent electric meter error on-line calculation model through an energy conservation principle based on the characteristic that most intelligent electric meters are distributed according to a tree topology structure in reality, and solving the model to obtain an error estimation value. On this basis, non-uniformity of error information contained in measurement data in time is considered, a weight coefficient represented by attenuation factors is introduced to construct a loss function of a calculation model, different attenuation factors are adopted according to the condition number of a coefficient matrix to improve the tracking capability of an algorithm, an error estimation value is obtained by deriving the loss function, and the tracking precision of the algorithm is improved. And the error estimation value of the electric energy meter is updated in real time through newly collected data, so that the solving precision of the error estimation value is improved.

The invention relates to a cattle face image dynamic detection and tracking method based on ViT-YOLOX-Dynamic Head, and the method comprises the steps: obtaining a cattle herd face video, and carrying out the data preprocessing; constructing a cattle face image dynamic detection and tracking model based on a YOLOX model; performing training to obtain a trained cattle face image dynamic detection and tracking model; and obtaining a to-be-detected cattle face image, performing preprocessing, inputting the preprocessed to-be-detected cattle face image into the trained cattle face image dynamic detection and tracking model, and obtaining a cattle face image detection result. According to the method, the capability of capturing context information of the model is enhanced, the parameter efficiency of the model is fully utilized, global information in the image can be better captured, and the method has higher recognition capability for cow face targets with complex backgrounds and different scales; a dynamic head method is adopted to perform multi-scale feature fusion and adaptively adjust feature levels by associating spatio-temporal features and facial features, so that the cow face detection and tracking capability of the model in different scales and complex backgrounds is improved.

The invention discloses a robust visual inertial odometer method and device in a dynamic scene, and belongs to the technical field of visual inertial odometers. The method comprises the following steps: step 1, acquiring and preprocessing a grayscale image, a depth image and IMU (Inertial Measurement Unit) information to acquire depth information; 2, performing feature rough matching based on depth information and inter-frame IMU integral, and constructing a corresponding descriptor and a matching function by using gray information and depth information of feature points; step 3, carrying out shake state detection by using IMU information, after a shake state is detected, carrying out re-extraction on feature points, and carrying out uniform distribution on the re-extracted feature points according to a gridding method; and step 4, constructing an inverse depth residual by using the re-extracted feature points and the depth information obtained by triangularization, and designing an optimization function. According to the invention, stable positioning in a complex dynamic scene can be realized by using the depth camera under an indoor condition, and the robustness of positioning is greatly improved.

The invention discloses a learning effect evaluation system and method for artificial intelligence image generation training courses, and relates to the technical field of computers. In order to solve the technical problem that in the prior art, a learning management system cannot feed back learning conditions of students in time, the technical scheme provided by the invention is that the system comprises a plurality of modules used for collecting and analyzing data in the learning process of the students, and comprises a student single course homework quality evaluation module, the quality evaluation module is used for performing quality evaluation on a single homework of a student in an artificial intelligence image generation course to obtain a quality score; the overall learning ability evaluation module is used for carrying out comprehensive learning state analysis on the quality scores and the homework process logs of the multiple homeworks of the students to obtain overall learning ability scores of the students; and the visual feedback module is used for generating learning diaries of the students according to the change of the homework quality and the learning ability of the students. The method is suitable for being applied to learning effect evaluation work of artificial intelligence image generation courses.

The invention provides an unmanned underwater vehicle trajectory tracking control system based on preset time and performance, and aims to improve trajectory tracking precision and stability in underwater operation tasks. The method comprises the following steps: establishing an unmanned underwater vehicle mathematical model; designing a preset time preset performance function, and introducing an adjustable function changing along with time to enable the boundary function to have a smooth characteristic in a convergence process; a time-varying gain auxiliary system is adopted to process an input saturation problem; designing a disturbance observer to realize rapid attenuation of disturbance, and enhancing the robustness of the system by introducing a residual adjustment function based on a smooth nonlinear model; and finally, integrating the interference observer and an auxiliary system to deduce a trajectory tracking scheme. The result shows that the longitudinal speed fluctuation is reduced by 69.2%, and the swaying speed fluctuation is reduced by 32.7%; the north position tracking performance is improved by 30.8%, the east position tracking performance is improved by 16.7%, the speed fluctuation is effectively reduced, and the tracking performance, the system stability and the anti-interference capability are improved.

The invention discloses a KCF relocation method based on multi-core DSP fusion target detection and motion information. The method is used for tracking and detecting a tracking target in an image. The method comprises the following steps: S1, carrying out global detection on an initial image of an input image, marking all objects, and selecting a tracking target for directional tracking from the objects; s2, confirming the total frame number m of the image to be read after the tracking target is determined, t = 1, 2... m, and extracting the tracking target characteristics of the first frame image; s3, on the basis of the tracking target features of the first frame of image, tracking target feature acquisition and tracking target trajectory data capture are performed on the second frame of image to the mth frame of image frame by frame; and S4, arranging the trajectory data of the tracking target and outputting a trajectory diagram of the tracking target. According to the invention, motion detection of the tracking target in the image is realized.

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 discloses a target tracking method for a hyperspectral video. The method comprises the following steps: initializing weight parameters in a hyperspectral target tracking algorithm; reading an initial frame of a tracking video as an initial template of target tracking and an online updating template image, and reading position information of a target in the initial frame; reading the next frame of the tracking video and cutting the next frame as a search image for target tracking; an initial template, an online updated template image and a search image serve as input of a hyperspectral target tracking algorithm, a three-channel image is obtained through dimensionality reduction of a dimensionality reduction module, then the three-channel image is input into a feature extraction network after low-rank adaptive learning, obtained feature information is input into a target position decoder, and a target position is obtained. Outputting the predicted position of the target in the current frame and the confidence of the target, and judging whether to update the online update template image or not according to the confidence; continuously repeating each frame of the tracked video and recording the predicted position of the target in each frame of the video; the method has the advantages of high target tracking accuracy, strong extraction feature representation, low training cost and the like.

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 belongs to the technical field of radars, and discloses a radar clutter trace point screening method using target short-time motion characteristics. The invention provides a radar clutter trace point screening method based on target motion characteristic parameter constraint and a short-time stability principle from trace point layer research on the basis that target motion generally has continuous and stable space-time relevance and clutter trace points appear randomly. According to the method, a set of clutter trace point screening method rules is provided based on target motion characteristic parameter constraints and short-term stability of target motion by only utilizing time and position basic information carried by trace points, clutter trace points can be effectively eliminated in a dense clutter background, target trace points can be effectively extracted, and the target trace points can be effectively extracted. And an effective technical support is provided for radar weak target detection and tracking under the clutter background.

The invention provides an aerial target tracking method based on a continuous discrete model, which comprises the following steps of: firstly, dividing a sensor sampling interval into a plurality of sub-step lengths, estimating a target state and a covariance by utilizing a volume point approximation method, and predicting the state and the covariance of the next step by utilizing a target state process continuous model; and repeating until the state and covariance prediction at the next moment is obtained, then constructing a measurement irrelevant function, and carrying out irrelevant conversion on measurement. And finally, updating the state and covariance prediction of the target by using measurement and irrelevant conversion measurement so as to realize the estimation of the state and covariance of the target, and repeating the steps until the estimation of the state of the target within the observation time is completed. According to the method, the target state model is closer to reality, the target information carried by measurement is fully utilized, the aerial target tracking performance is improved, and the tracking advantage of the aerial target under strong background noise is particularly obvious.

The invention discloses a biaxial complete decoupling cutter servo device and a design method thereof, and the biaxial complete decoupling cutter servo device comprises a compliant mechanism platform, a piezoelectric stack I, a piezoelectric stack II, a diamond cutter, a measuring baffle plate, a capacitive displacement sensor I, a capacitive displacement sensor II and an upper cover plate, the diamond cutter is installed on the cutter holder platform, the compliant mechanism platform further comprises a topology compliant mechanism, a first input guide mechanism, a second input guide mechanism and an output guide mechanism, the output guide mechanism is located at the front end of the compliant mechanism platform, and the rear end of the topology compliant mechanism is connected with the front end of the first input guide mechanism and the front end of the second input guide mechanism. According to the method, the compliant mechanism is designed by adopting a topological optimization method, and the optimal mechanism configuration-parameter design can be sought under the condition that a design domain and specified input and output effects are given and a series of constraint conditions are met, so that the mechanism performance is optimal.

The present invention provides a closed-loop tracking system and method for collaborative control of miss amount and trajectory prediction, which can improve the tracking capability of the optoelectronic tracking system for highly maneuverable targets and meet the actual needs of satellite-borne optoelectronic tracking systems. The system of the present invention uses the three-dimensional motion information of the target to predict the miss amount and feedback control, which improves the prediction accuracy of the target motion, thereby improving the closed-loop control accuracy of the miss amount, enhancing the tracking capability of the moving target, and avoiding the accuracy loss caused by only using the two-dimensional motion information of the target and lacking one-dimensional motion information during the feedback control of the miss amount. The closed-loop tracking method for collaborative control of miss amount and trajectory prediction of the present invention obtains the movement information of the miss amount through the three-dimensional motion information of the target, and performs composite closed-loop control of the miss amount and trajectory prediction, thereby improving the tracking capability of the optoelectronic tracking system for highly maneuverable targets and meeting the actual needs of satellite-borne optoelectronic tracking systems.

The invention relates to the technical field of space target monitoring, and discloses an RCS information and Doppler information-assisted adaptive PHD filtering method, which comprises the following steps of: 1, establishing a Gaussian mixture logarithmic normal distribution model; 2, on the basis of the Gaussian mixture logarithmic normal distribution model, improving the Gaussian component weight of the Gaussian mixture logarithmic normal distribution model, and establishing an adaptive PHD filter assisted by RCS information and Doppler information; according to the method, target RCS information and Doppler information are introduced into a multi-target filtering updating process, and target feature information is fully utilized to perform space multi-target tracking in a space-based scene; meanwhile, according to the method, the difference between the target and the clutter is enlarged by using the target RCS information and the Doppler information; the problems that an existing filtering method cannot provide target amplitude and RCS information, the accuracy cannot be effectively guaranteed, and different target tracks cannot be effectively distinguished are solved.

The invention provides a photoelectric tracking control method based on inertial information assistance. The photoelectric tracking control method comprises the following steps: S1, obtaining a projection vector of a target in a target surface system and a projection vector of the target in a target geographical system; s2, performing time synchronization on the miss distance and the measured angle by taking inertial navigation as a reference, and predicting a projection vector under a geographical system to obtain an elevation angle and an azimuth angle of a carrier system at the current moment; s3, performing time alignment of the measurement data and space alignment of the coordinate system and the measurement data, and correcting the angle measurement data; s4, carrying out updating frequency improvement and prediction on the target vector position by using carrier operation information measured by the inertial measurement unit, providing target indication data to form control parameters and guide information, controlling the detection head to turn to the target position according to the guide information, and after guiding to turn in place, returning to the target position. A given target is guided, captured and tracked, miss distance and a tracking state are returned in real time, a position closed loop of target tracking can be realized, and the tracking performance and stability of a system are improved.

The invention provides a gradient determination method and a vehicle, and the method comprises the steps: calculating a prediction vector of a current moment through employing an extended Kalman filtering algorithm according to a first calibration vector corresponding to a previous moment of the current moment; the first calibration vector includes a first calibrated longitudinal vehicle speed, a first calibrated gradient, and a first calibrated gradient change rate. According to the prediction vector at the current moment and the observation vector at the current moment, calculating a calibration gradient at the current moment by adopting an extended Kalman filtering algorithm; wherein the observation vector comprises kinematically estimated longitudinal vehicle speed and pitch angle speed. In the process of calibrating the prediction vector, the pitch angular velocity is fused, a gradient can be obtained through pitch angular velocity integration, that is, the gradient obtained through the pitch angular velocity and the predicted gradient in the prediction vector are mutually verified, and the precision and robustness of gradient estimation can be effectively improved. And the calculation rate of the pitch angle speed to obtain the gradient is higher, so that the tracking effect of gradient estimation can be improved.

The present invention relates to the technical field of multi-target tracking, and discloses a 3D multi-target tracking method for improving Kalman filtering based on the number of scene targets, obtaining original point cloud data; processing the obtained original point cloud data to obtain a detection frame after processing the i-th frame point cloud data; adjusting the IOU threshold of the data association part by the number of the i-th frame target detection frame to obtain the IOU threshold in the current frame scene; obtaining the target association pairs between the tracking set and the detection set of the i-th frame; judging the obtained association pairs and distinguishing different target types; using the new tracking set as the input of the adaptive Kalman filter, and obtaining the optimal estimate of the target of the i+1-th frame through prediction, repeating the above steps until all point cloud data are traversed, thereby obtaining accurate tracking of multiple targets with target changes in different scenes; the method has high data association, can better balance the accuracy and stability of tracking in different scenes, and improves the overall tracking performance.

The invention discloses an instruction correction method considering the frequency domain characteristics of a servo control system. The method comprises the following steps: firstly, the instruction is decomposed into a combination of a Fourier fitting term and a remainder term; then, the remainder term is decomposed into a basic mode component term and a new remainder term by using empirical mode decomposition; then, the Fourier fitting term is corrected, and a frequency response curve of the servo control system from the instruction position to the motion output is established; the amplitude and phase of the sine and cosine signals of each frequency in the correction are based on the frequency characteristics of the servo control system, and the attenuation and lag of each frequency component in the servo control system are offset; then, the instantaneous amplitude and instantaneous phase of each basic mode component are solved by using Hilbert transform, and the attenuation and lag of the servo control system are offset; finally, the corrected Fourier series fitting term, the basic mode component term and the remainder term are used as a new instruction as input; the invention not only meets the higher tracking error control requirements, but also ensures the processing efficiency, and realizes the improvement of the tracking performance of the servo control system.

The application discloses a self-disturbance rejection control method and system based on an error compensation type extended state observer, and the method comprises the following steps: a linear self-disturbance rejection controller acquires input signals and each output state of an extended state observer, and outputs a first control signal; the linear self-disturbance rejection controller acquires input signals and each output state of the extended state observer, and outputs the first control signal; an estimation error model of the extended state observer for output displacement is constructed, a controlled object is converted into an integrator series type form, and an output displacement signal is obtained based on the controlled object; the output displacement signal and a control input signal are input into the extended state observer, and each output state is obtained; and then the linear self-disturbance rejection controller and the output displacement model are fed back. The estimation capability of the observer is greatly improved without increasing the bandwidth of the observer; the error compensation type disturbance observer is combined with the self-disturbance rejection control, and the tracking performance of the self-disturbance rejection control is further improved.