88 results about "Nonlinear motion" patented technology

The invention discloses a visual prediction method for space-time manifold and implicit state deduction of a robot, and relates to the technical field of robot space visual perception. The motion state and the visual image of the robot are collected; calculating carrier pose offset and generating a reverse compensation matrix; modeling an image target into a visual cone probability manifold, and extracting an explicit manifold observation vector; monitoring the confidence coefficient in real time through an observation quality evaluation network, and activating an implicit deduction mode during observation degradation; reading historical time sequence characteristics by using an improved Transform model, and performing prediction and deduction on a future nonlinear motion state of the target; performing coordinate correction and probability field decoding on a prediction result in combination with the reverse compensation matrix, and outputting a three-dimensional prediction trajectory and a spatial covariance ellipsoid; according to the method, the problem that traditional visual tracking fails under the conditions of violent shaking of a carrier and target shielding is solved, and continuous and foresight physical scale positioning and safety decision making of a robot on a dynamic target are achieved.

The invention discloses a multi-modal data collaborative intelligent detection and three-dimensional positioning method for marine ships. The method comprises the following steps: performing refined preprocessing on collected multi-source heterogeneous data, and performing preliminary target detection; performing dynamic weighted fusion on the preprocessed multi-source heterogeneous data, and realizing accurate coordinate recovery of the ship target in a three-dimensional space by combining depth data of the laser radar; and predicting and updating the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. Through multi-modal data perception and preliminary processing, heterogeneous data deep fusion and three-dimensional positioning, and advanced multi-target continuous tracking and state estimation, abundant texture information of a visible light image, anti-interference capability of thermal imaging and accurate space depth data of a laser radar are fully utilized, and a multi-tracking mechanism cooperation strategy is integrated. The motion state of the recognized target is predicted and updated, and the problem of tracking interruption caused by nonlinear motion or transient shielding of the marine target is effectively solved.

The invention discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation, and belongs to the technical field of fan simulation calculation. In combination with calculation parameters used by three-party software, establishing a blade structured load model and a blade correction aerodynamic model, calculating an aerodynamic load and a structural load, and superposing: superposing the aerodynamic load, the gravity load and the centrifugal force load of the blade as an external load of a nonlinear structure control equation of the blade, and taking the external load as a calculation parameter; according to the method, the blade nonlinear structure model and the semi-coupling simulation process of the three-party wind turbine simulation software are constructed, and under the same simulation environment and load conditions, the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation, so that the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation calculation. The higher-precision non-linear motion response characteristics of the super-long flexible wind turbine blade are calculated, and a good effect can be achieved in the aspects of initial structure design and response evaluation of the large wind turbine blade.

The invention relates to the technical field of computer vision, in particular to a multi-target tracking method combining a time convolution network and a self-attention mechanism, which comprises a motion model based on a TCN, dynamic ReID feature updating based on track confidence, and a rematching strategy used for correcting wrong association caused by target interaction shielding. A TCN network is combined with an attention mechanism to be used for motion modeling, and nonlinear motion prediction of a track is achieved. In addition, a track confidence coefficient is provided to measure the robustness of the track, and the ReID features are dynamically updated based on the track confidence coefficient. Meanwhile, in order to make up for wrong association caused by target interaction shielding, a rematching module is designed, and the effectiveness of the rematching module is proved through experiments. In addition, each module of the system has a modular characteristic and a relatively strong generalization ability, so that the system is suitable for a wider real scene, and future research work is possibly stimulated.

The invention provides an attitude trajectory optimization method and system based on a self-adaptive sliding window. The method comprises the following steps: acquiring visual media data and decoding the visual media data into a time sequence image frame sequence; performing attitude estimation on each frame of image to obtain a coordinate set of each attitude feature point; caching a coordinate set of the historical attitude feature points to form a variable-length dynamic trajectory cache queue; constructing a sliding window according to the coordinate sets of the attitude feature points of the current frame and the previous frame; performing abnormal data elimination on each coordinate track; performing B spline fitting on each coordinate track; sequentially executing multi-stage optimization on the coordinate track; and updating the sliding window frame by frame to obtain a sequential sequence of the optimized attitude feature points. The method has the beneficial effects that the window radius is dynamically adjusted based on the motion intensity, the window is expanded at low speed to enhance noise suppression, and the window is reduced at high speed to reduce delay; and nonparametric regression fitting is carried out by adopting a cubic B spline, so that the modeling capability of a nonlinear motion track is enhanced, and the track smoothness is improved.

The invention discloses a method for calculating the posture of a berth airplane in rainy and snowy weather. The method comprises: acquiring an aircraft cleaning point cloud; performing coarse registration on the aircraft cleaning point cloud and the target point cloud, and outputting a registered aircraft pose; and carrying out Kalman pose prediction on the aircraft pose at the next moment by utilizing the aircraft pose and adding aircraft kinematics model constraint. According to the method, the posture of the aircraft at the next moment can be accurately predicted by establishing the nonlinear kinematics model and performing state estimation by adopting the extended Kalman filtering. By weighting key areas such as a machine head and an engine, accurate alignment of the important areas is guaranteed preferentially in the registration optimization process, and the precision and robustness of a registration algorithm under severe conditions are effectively improved. Kalman prediction provides a high-quality initial value for the ICP, and the accurate result of the ICP updates the state of Kalman filtering in turn. When the ICP fails, Kalman prediction takes over seamlessly, and a set of complete fault-tolerant mechanism is formed.

The invention provides a learning enhanced Kalman filtering method for forest emergency positioning and a related device. The method comprises the following steps: acquiring time sequence measurement data of an unmanned aerial vehicle and a ground target node; inputting time sequence measurement data into an error compensation network, and outputting a ranging error compensation amount and an observation noise covariance matrix; performing distance measurement compensation on the time sequence distance measurement data, feeding back the distance measurement data subjected to error compensation and a state error of the ground target node to a state prediction network, and outputting a state transition matrix and a process noise covariance matrix; state prediction is carried out based on a state transition model and a process noise covariance matrix, Kalman filtering fusion is carried out on a prediction state, compensated distance measurement data and an observation noise covariance matrix, three-dimensional position estimation of a ground target node is determined, combined improvement of measurement correction and prior prediction precision is realized, and the accuracy of measurement correction and prior prediction is improved. On the basis of keeping the optimality of traditional filtering, modeling is carried out on time-varying noise and non-linear motion, and the positioning precision in a forest complex environment is improved.

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an incremental Kd-Tree laser-inertial fusion positioning method based on improvement, the method provided by the invention adopts a practical filtering scheme, the operand is small, the real-time performance is high, and the requirement of a laser radar on an illumination condition is lower than that of a camera, so that the method is very practical. The noise is much lower than that of visual measurement, so that the precision of the method is higher than that of a visual algorithm; according to the method provided by the invention, two kinds of data are fused in a deep level, so that the method is not liable to fail during strenuous exercise, and a GPU is not needed during operation. The filtering algorithm adopts an iterative error state Kalman filtering algorithm, so that the precision of the system is not very low under nonlinear motion, and the map management efficiency is also improved by the improved incremental Kd-Tree algorithm in the aspect of map management. In conclusion, aiming at a special scene without a GNSS signal, the robot can efficiently position the robot and construct a surrounding environment map only by depending on the sensor of the robot.

The invention provides a vehicle-mounted three-dimensional scene rendering method and device, a storage medium and a program product, and the method comprises the steps: obtaining the perception data of a target object, and constructing a nonlinear motion state space representing the motion process of the target object; performing prediction updating on the target object based on the state transition model of the nonlinear motion state space to obtain prediction state information and a prediction covariance matrix at the current rendering moment; extracting variance information related to the position from the covariance matrix to calculate an uncertainty factor, introducing the uncertainty factor into a graphic rendering pipeline, and dynamically adjusting at least one type of visual attribute of the three-dimensional model to realize uncertainty prompt; and when new perception data of the target object is obtained, performing fusion updating on the prediction state information, and performing smooth transition on a rendering result based on the correction amount to suppress rendering jump. According to the method, high-frame-rate continuous rendering display is realized under the condition of low-frequency updating of the sensor, and the interpretability and the safety of a rendering result are improved.

The invention discloses a video multi-target tracking method and system based on intra-layer and inter-layer fusion, and relates to the technical field of video processing and computer vision. The method comprises the following steps: acquiring a video data set containing a multi-target bounding box label, and performing preprocessing to construct a track sequence data set; constructing a Kalman-transformer hybrid frame model, and training by using the trajectory sequence data set to obtain a trained Kalman-transformer hybrid frame model; collecting an input video sequence, and performing frame-by-frame target detection on the input video sequence to obtain a candidate detection set; and inputting the candidate detection set into the trained Kalman-transformer hybrid framework model, and carrying out data association and state estimation to obtain a multi-target trajectory set. According to the method, reasoning is carried out only based on the geometric trajectory on the premise of not extracting image appearance features, real-time performance and precision are both considered, and the problem that an existing Kalman filtering method is difficult to deal with nonlinear motion and long-time shielding is solved.

The invention relates to an eccentric head five-axis numerical control cutter shaft proper amount control method and device, and the method comprises the steps: determining an initial cutter location point position and a target cutter shaft vector in a workpiece coordinate system according to the geometrical characteristics and machining requirements of a to-be-machined workpiece; based on the physical structure parameters of the eccentric rotating workbench, determining the space offset of the rotating center and the workpiece reference through a pre-constructed space offset relation model; calculating the motion compensation amount of the spindle head and the rotating shaft according to the current angle of the rotating shaft of the workbench and the space offset; the motion compensation amount is converted into a motion control instruction of a numerical control system, a rotating shaft of the eccentric rotating workbench is driven to be linked with a spindle head, the direction of a cutter shaft is continuously controlled, and therefore the purposes that high-precision cutter shaft pointing control of the eccentric rotating workbench in five-axis linkage machining is achieved, and non-linear motion errors caused by rotation center deviation are eliminated are achieved.

The application discloses a rotor coupling dynamics calculation method of complex three-dimensional shape layout. A coordinate conversion relationship caused by a front protrusion / after-sweeping / downward-reversing configuration is established; nonlinear elastic deformation of elastic blades waving, oscillating and twisting and nonlinear coupling relationship among them are described; nonlinear motion caused by the complex three-dimensional shape layout is described based on the coordinate conversion relationship and the nonlinear coupling relationship; and the relationship among strain energy, kinetic energy and virtual work of external force of the rotor is established based on the Hamilton principle according to the description of the nonlinear motion, and a finite element method of a 15-degree-of-freedom beam element is used to establish a rotor dynamics equation. The application can effectively reflect the structural coupling characteristics and coupling degree of the waving-torsion direction caused by the front / rear sweep of the blade tip, effectively reflect the structural coupling characteristics and coupling degree of the oscillation-torsion direction caused by the upward / downward reversal of the blade tip, and effectively reflect the rotor dynamics characteristic behaviors caused by the three-dimensional shape of the blade.

The invention belongs to the technical field of rigid body posture estimation, and discloses a rigid body posture estimation method, system and device based on generalized correlation entropy geometric filtering, and the method comprises the steps: obtaining target motion information, and building a discrete nonlinear kinematics model; the discrete nonlinear kinematics model is initialized; sigma points of a posterior state covariance matrix and a process noise covariance matrix are generated respectively, and the sigma points are propagated and updated through manifold fast unscented transformation; obtaining a prediction state mean value, a priori state covariance matrix and a square root of the priori state covariance matrix; updating the central parameters based on the information of the historical moments; calculating a pseudo measurement matrix and updating the gain; correcting the state estimation by using the gain; judging whether convergence occurs or not, and if not, continuing iteration; otherwise, outputting a posterior state estimation value as a final rigid body posture estimation result at the moment. According to the method, the problem that the robustness and precision of rigid body posture estimation in a non-zero mean value and non-Gaussian noise environment are reduced is effectively solved.

This invention relates to an active disturbance rejection control method and system suitable for biomimetic chest cavity motion simulation. The method includes: acquiring and preprocessing a nonlinear motion trajectory signal to generate an original target position command; processing the original target position command using a tracking differentiator to generate a smoothly transitioning desired position signal and desired velocity signal; receiving the actual position feedback signal of the controlled object and the control input of the previous control cycle through an extended state observer, and estimating the actual position state observation value, actual velocity state observation value, and total disturbance state observation value of the controlled object in real time; calculating the position tracking error and velocity tracking error based on the desired position signal, desired velocity signal, and the actual position state observation value and actual velocity state observation value of the controlled object, and calculating the initial state feedback control quantity; generating an actual control command and sending it to the motor driver to drive the biomimetic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

The invention relates to a curtain driving method and device. The method comprises the steps that the curtain driving device obtains a starting instruction; the curtain driving device responds to the starting instruction and drives the curtain to execute sectional type nonlinear motion control according to a segmentation strategy, the segmentation strategy is determined according to target parameters obtained in advance, and the target parameters are changeable; wherein the sectional type nonlinear motion control at least comprises the following steps: in a first preset proportion interval of a target stroke determined according to the target parameter, gradually propelling a curtain to act by a curtain driving device according to an action interval determined by a sectional strategy; and in a second preset proportion interval of the target stroke, the curtain driving device is divided into at least one step of action, and the stroke of each step of action is larger than that of each step of action in the first preset proportion interval, so that different action rhythms are distributed in different stroke proportion intervals, and the target stroke can be adjusted according to the different action rhythms. And a sectional type nonlinear motion control mode in the whole target stroke is formed.

The application discloses a kind of unmanned aerial vehicle detection tracking methods based on multi-modal image, belong to target detection and tracking field.The present application detects aerial unmanned aerial vehicle by the joint action of multiple modes to realize the reliability of unmanned aerial vehicle detection under various environments, realize the identification and classification of unmanned aerial vehicle;In the tracking stage, the prediction structure of the fusion of LSTM and Kalman filter is introduced, the state modeling ability under the nonlinear motion of target and the shielding condition is enhanced, and the stability and continuity of tracking are improved;Adopting the multi-stage matching strategy effectively improves the matching accuracy and robustness in complex environment;At the same time, the confidence shunt strategy and trajectory management mechanism are combined, the response speed to new target and the fault tolerance to lost target are improved;Combined with global motion estimation and height information determination, the precise correction of unmanned aerial vehicle trajectory and target differentiation are realized, aliasing and misjudgment are avoided, and the detection and tracking ability of unmanned aerial vehicle is significantly improved.

The application discloses a kind of matching and mismatched interference under unmanned ship preset time control method and system, comprising the following steps: establishing the nonlinear motion model of unmanned ship under matching and mismatched interference;Design preset time adjustment function, the dynamic evolution process of system error is regulated;Kinematic disturbance observer and dynamics disturbance observer are designed to online estimate matching interference and mismatched interference;According to interference estimation information, construct kinematic controller, filter and dynamics controller, realize the robust preset time tracking control of reference trajectory of unmanned ship.The method proposed in the application can realize the online observation and compensation of multi-source disturbance, ensure that the system tracking error and disturbance observation error converge within the preset time, and the convergence time is adjustable, while having the advantages of continuous smooth control input, strong anti-interference ability and high engineering realizability, which can improve the control performance and reliability of unmanned ship in complex sea conditions and time-sensitive tasks.

The application discloses a preset time control method and system for a wheeled mobile robot under multiple disturbances, and comprises the following steps: establishing a nonlinear motion model of a wheeled mobile robot under matched and unmatched disturbances; designing a preset time adjustment function to regulate and control the dynamic evolution process of system errors; designing a kinematic disturbance observer and a dynamic disturbance observer to online estimate the matched and unmatched disturbances; and constructing a kinematic controller, a filter and a dynamic controller according to the disturbance estimation information, so as to realize robust preset time tracking control of the wheeled mobile robot on a reference trajectory. The method provided by the application can guarantee the convergence of system errors within a preset time without relying on the ideal no-slip assumption, has the advantages of adjustable convergence time, continuous and smooth control input, strong anti-interference ability and high engineering realizability, and improves the anti-interference performance of the robot in a complex dynamic environment and the reliability of a time-sensitive task.

This application discloses a multimodal data collaborative intelligent detection and 3D localization method for maritime vessels, comprising: fine preprocessing of collected multi-source heterogeneous data and preliminary target detection; dynamic weighted fusion of the preprocessed multi-source heterogeneous data, combined with depth data from lidar, to achieve accurate coordinate recovery of the vessel target in 3D space; and prediction and updating of the motion state of the identified target based on a multi-tracking mechanism collaborative strategy. By fully utilizing the rich texture information of visible light images, the anti-interference capability of thermal imaging, and the precise spatial depth data of lidar through multimodal data perception and preliminary processing, heterogeneous data deep fusion and 3D localization, and advanced multi-target continuous tracking and state estimation, this method integrates a multi-tracking mechanism collaborative strategy to predict and update the motion state of the identified target, effectively addressing the problem of tracking interruption caused by nonlinear motion or temporary occlusion of maritime targets.

Aiming at the problem of low filtering precision caused by noise time variation and outlier interference in ship navigation, the invention provides an unscented Kalman filtering ship integrated navigation method based on a variational Bayesian principle. According to the method, a 16-dimensional ship nonlinear motion model is constructed, student t distribution modeling is innovatively adopted to measure noise so as to enhance robustness, and noise covariance and auxiliary variables are modeled as inverse Gamma distribution and Gamma distribution respectively. By introducing variational Bayesian iterative optimization in UKF updating, state posteriori distribution and noise parameters are alternately updated until convergence, and real-time adaptive estimation of process and measurement noise covariance and outlier weight is realized. According to the method, filtering divergence can be effectively suppressed, and the estimation precision and robustness of the attitude, position and speed of the ship under the complex sea condition are remarkably improved.

The invention relates to the technical field of multi-agent driving control, in particular to an uncertain nonlinear agent driving control method based on linear quadratic adjustment, and the method comprises the steps: deducing a standard differential equation mathematical model according to an uncertain nonlinear kinematics model of a guider, thereby independently listing unmeasurable nonlinear items; a non-measurable nonlinear item is estimated through an observer, a dynamic tracking controller is designed based on the observer, and the input state stability of the system is ensured in combination with a multi-loop nonlinear small gain condition, so that a guider can track a time-varying reference signal. Then, a reference signal generating system which generates a variable reference signal at the moment is used as a new guider, a drive control controller is designed through linear quadratic adjustment, so that optimal input is solved through state estimation and error optimization, an uncertain nonlinear drive control problem is simplified to be a linear quadratic adjustment problem to be solved, and the drive control precision is improved. The integrated collaborative design of'tracking the reference signal by the guider 'and'arriving the target position by the rover' is realized.

This invention discloses a high-efficiency and precise time control method for unmanned underwater vehicles (UUVs), comprising the following steps: constructing a six-degree-of-freedom nonlinear motion mathematical model of the UUV; designing a precise time disturbance observer to estimate environmental disturbances online; establishing a dynamic model of the UUV's tracking error; and designing a precise time sliding mode controller. The method proposed in this invention ensures precise time convergence while avoiding excessive initial control input from the controller, significantly reducing system control energy consumption and ultimately achieving high-efficiency and precise time tracking control for the UUV.

The invention discloses a multi-target tracking method based on a self-adaptive iterative expansion intersection-to-union ratio and a related device, and belongs to the technical field of computer vision and graphics. The method follows the normal form of tracking after detection, firstly, independent target detection is carried out on each frame of image, and then a motion prediction module based on training during testing is used for predicting the position coordinates of a target in the next frame. And meanwhile, calculating a predicted position offset mean value, if the predicted position offset mean value is greater than a threshold value, constructing a cost matrix by adopting a self-adaptive iteration expansion intersection-union ratio method and utilizing an expansion intersection-union ratio between the predicted position and the detection frame and appearance characteristics, and solving the cost matrix by utilizing a Hungary algorithm so as to realize association of nonlinear motion targets between two adjacent frames. Experiments prove that the method has the best performance on two large-scale common nonlinear data sets, and irregular nonlinear motion of athletes in the tracking process can be effectively processed.

The invention belongs to the technical field of image processing, and relates to a vulcanizing machine mold closing state real-time monitoring method and system based on machine vision. The method comprises the following steps: collecting a continuous image sequence in the mold closing process of a vulcanizing machine, tracking mold edge feature points and mapping the mold edge feature points into a physical height time sequence; calculating a high-order difference based on the height data, and obtaining an instantaneous speed, an acceleration and a jerk; constructing a non-linear motion retardation index containing jerk and position weight for identifying foreign matter clamping stagnation; a double-side dynamic coupling unbalance degree based on the left and right side speed difference energy ratio is constructed and is used for evaluating the mechanical balance; and comparing the index calculated in real time with a preset threshold value, and executing sudden stop protection or maintenance early warning. According to the method, through process kinematics characteristic analysis, high-sensitivity detection of tiny foreign matter retardation is realized, and accurate data support is provided for equipment maintenance.

The application provides a vehicle-mounted three-dimensional scene rendering method, device, storage medium and program product. The method comprises: acquiring perception data of a target object, and constructing a nonlinear motion state space representing a motion process of the target object; performing prediction update on the target object based on a state transition model of the nonlinear motion state space, to obtain prediction state information and a prediction covariance matrix at a current rendering time; extracting variance information related to a position from the covariance matrix to calculate an uncertainty factor, and introducing the uncertainty factor into a graphics rendering pipeline to dynamically adjust at least one type of visual property of a three-dimensional model to realize uncertainty prompting; when new perception data of the target object is acquired, performing fusion update on the prediction state information, and performing smooth transition on a rendering result based on a correction amount to suppress rendering jump. The application realizes high-frame-rate continuous rendering display under the condition of low-frequency update of a sensor, and improves the explainability and safety of a rendering result.

The invention discloses an intelligent ship stranding early warning method which comprises the steps of obtaining and updating ship state data and environment water depth tide data, modeling a stranding risk area based on the water depth tide data, constructing a ship track prediction model and a stranding early warning detection area model, and performing early warning on the stranding risk area. And establishing a space-time two-dimensional weighted multi-level stranding risk assessment model, responding and feeding back an early warning result in real time, and establishing a continuous monitoring mechanism. Ship state data are collected through multiple sensors such as a GPS and an IMU, multi-source environment data such as an electronic chart and a tide station are fused, a nonlinear motion model and unscented Kalman filtering are adopted to accurately predict a trajectory, low-level early warning, middle-level early warning and high-level early warning are divided according to the risk degree, a differential response mechanism is matched, and early warning jitter is avoided by combining continuous monitoring and a risk relieving delay mechanism. According to the invention, accurate and real-time early warning of the stranding risk is realized, the dependence on the experience of sailors is reduced, and the ship navigation safety is remarkably improved.

This invention belongs to the field of robotic arm control and provides a robust control method and system for a lifting robotic arm. It includes determining five nonlinear motion equations of the robotic arm dynamics using the Euler-Lagrange equations and representing them in matrix form. The robotic arm dynamics matrix form is divided into driven and non-driven forms, and after reduction processing, a reduced-mode dynamic model of the robotic arm is obtained. A robust controller is constructed by building a second-order sliding mode controller and a fast terminal sliding mode controller. The robotic arm dynamics matrix form is transformed into a state-space form to obtain a state observer. The state observer is subtracted from the state-space form to obtain the estimation error dynamic equation. The dynamic state of the robotic arm is inferred using the state observer. The control mode of the robust controller is determined based on the estimation error dynamic equation corresponding to the dynamic state, and the corresponding control signal is generated based on the robust controller. This invention significantly improves the safety and efficiency of robotic arms in practical applications.

The invention discloses an unmanned aerial vehicle adaptive sliding-mode control method, system and device and a medium, and the method comprises the steps: building a nonlinear kinematics and dynamics model of a four-rotor unmanned aerial vehicle, carrying out the real-time estimation of the mass and disturbance force of the four-rotor unmanned aerial vehicle, adding the mass and disturbance force into a position adaptive sliding-mode controller, and carrying out the real-time estimation of the mass and disturbance force of the four-rotor unmanned aerial vehicle. An expected tension control law for outer ring position tracking is obtained through calculation, an expected attitude angle is generated through decoupling, generalized rotational inertia and disturbance torque of the quad-rotor unmanned aerial vehicle are estimated in real time and added into an attitude self-adaptive sliding mode controller, the expected attitude angle serves as an input instruction, and the position of the quad-rotor unmanned aerial vehicle is tracked. The expected torque manipulated variable used for driving the unmanned aerial vehicle is obtained through calculation, and therefore cooperative control over the position and attitude of the unmanned aerial vehicle is jointly completed through the expected torque manipulated variable and the expected tension control law. Therefore, system buffeting can be effectively suppressed without sacrificing sliding mode control robustness, and the control precision and the anti-interference capability of the quad-rotor unmanned aerial vehicle under the conditions of complex disturbance and parameter change are remarkably improved.

The invention provides an air-sea cross-domain communication gateway energy optimization management system and method in a coupling environment, a closed-loop control mechanism is constructed by fusing data noise reduction of extended Kalman filtering and particle filtering and an LSTM power prediction and reinforcement learning scheduling algorithm, and fine regulation and control of whole sea conditions are realized by combining a multi-mode power supply network. Compared with a traditional static strategy, the energy use time is effectively prolonged; the environmental state judgment accuracy is improved through a nonlinear motion model and a trend prediction mechanism, a transition scene regulation blank is effectively filled up through a medium sea condition mode, the mode switching time delay is lower, and the adaptive capacity of the system to a complex and changeable marine environment is remarkably enhanced; through a surge suppression circuit, redundant power supply design and a hybrid communication link scheduling strategy, the success rate of coping with power supply fluctuation and hardware faults, the communication link switching accuracy and the cross-domain data transmission success rate of the system are all improved, and meanwhile the system has wide applicability and can be expanded to various mobile platforms such as unmanned aerial vehicles and unmanned ships.