131 results about "Wind disturbance" patented technology

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention relates to the technical field of engineering control, in particular to a hoisting control method of a safe and portable hoisting cage, which comprises a hoisting pre-stage, data pre-acquisition and analysis before hoisting, a hoisting control stage and hoisting landing control. In the prior art, position closed-loop control mainly depends on a stroke encoder and a position sensor, dynamic swing deviation caused by wind disturbance is difficult to accurately pre-judge and counteract in real time, and the defects of control lag and insufficient response exist. According to the scheme, the motion trail and posture of the cage are predicted in advance based on simple pendulum dynamic model numerical solution and the Runge-Kutta method, high-precision deviation detection is carried out in combination with a virtual-real vision calibration technology and AR enhanced display, a cage motion state control model based on feedforward prediction is established, trail prediction errors are greatly compressed, and the accuracy of the motion state of the cage is improved. The forward-looking inhibition of the movement of the cage, especially wind-induced swinging, is realized, and the accuracy and the response speed of an active swinging inhibition measure are obviously improved.

The invention relates to the technical field of flight control, in particular to a fixed-wing unmanned aerial vehicle attitude correction and precise flight control method fused with visual navigation, which comprises the following steps: extracting ORB feature points and constructing a dynamic feature point cloud; simultaneously acquiring angular velocity and acceleration data of the inertial measurement unit; performing tight coupling fusion processing on the dynamic feature point cloud and the inertial measurement data to generate real-time pose data; inputting the real-time pose data into an attitude correction parameter calculation module, and generating an attitude correction parameter through a dynamic weight distribution algorithm in combination with a preset flight path planning model and GPS positioning data; and a control surface deflection instruction and an accelerator control instruction are generated through the multi-constraint optimization model, and unmanned aerial vehicle flight attitude closed-loop correction is completed. According to the method, aerodynamic parameters such as the air speed, the wind field lateral disturbance and the attack angle are introduced to serve as coupling input, the control surface deflection amount and limiting conditions are dynamically adjusted, and self-adaptive modeling and control over the nonlinear flight characteristics are achieved.

The invention discloses an unmanned aerial vehicle wind resistance control method and system based on wind field sensing, and relates to the technical field of aircraft wind resistance control and wind field sensing. The method comprises the following steps: acquiring local wind field information around an aircraft in real time through wind speed and direction sensors arranged in a distributed manner, and performing fusion processing to form three-dimensional wind field distribution; generating a feedforward control quantity based on the wind field disturbance identification and short-term prediction model; feedforward control and a feedback control law of a flight control system are fused to form a composite control strategy, and flight attitude stabilization and control response optimization of the unmanned aerial vehicle under complex wind disturbance are realized. Correspondingly, the system comprises a flow field sensing and data processing module, a disturbance identification and feedforward control module and a flight control execution module, and has the functions of wind field real-time sensing, disturbance prediction, control preposition deployment and the like. According to the invention, the wind resistance and autonomous flight safety of the unmanned aerial vehicle in a low-altitude complex wind field environment can be remarkably improved.

The invention discloses a high-rise building operation robot, and belongs to the technical field of engineering construction, the high-rise building operation robot comprises a base, the base is provided with a mechanical arm, a driving mechanism for driving the mechanical arm to move horizontally and a lifting mechanism for vertically moving along a wall surface, the end part of the mechanical arm is provided with a sand blasting gun, two sides of the sand blasting gun are provided with distance measuring sensors, and the base is provided with a wind direction and wind speed sensor; the modeling module is used for fusing sensor data to generate an environment model containing three-dimensional geometric and semantic information and a dynamic wind field distribution model; the cooperation module generates a cooperation control instruction sequence for coordinating each component based on prediction optimization control; the adjusting module corrects parameters of the sand blasting gun in real time through autonomous learning; the execution module generates a driving instruction in combination with feedforward and compliance control; and the decision module monitors and outputs an operation adjustment instruction or a re-planning instruction. The device can sense the condition of the outer wall in real time and dynamically adjust the operation strategy, the adaptability to the complex wall surface is improved, meanwhile, wind disturbance is effectively resisted, and the sand blasting quality is guaranteed.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention relates to an intelligent photovoltaic cleaning method for air-ground cooperation of an unmanned aerial vehicle and a cleaning robot, and belongs to the technical field of photovoltaic cleaning. The method comprises the steps that the real-time pose of the unmanned aerial vehicle is determined in a visual positioning and laser positioning fusion mode; establishing a wind speed estimation model by taking the wind speed as a predictable state quantity; solving an optimal control sequence of the unmanned aerial vehicle in a prediction time domain based on a wind speed estimation model so as to compensate wind disturbance in advance; the track of the unmanned aerial vehicle is corrected to achieve accurate butt joint of the unmanned aerial vehicle and the cleaning robot; and an intelligent decision-making method is used for realizing the timely transfer of the cleaning robot by the unmanned aerial vehicle and the cleaning work of the photovoltaic panel of the cleaning robot. Through centralized control of the central platform, a single unmanned aerial vehicle assists multiple cleaning robots, dynamic task allocation and path planning are achieved, and the problems that desert photovoltaic cleaning efficiency is low and the coverage range is limited are solved.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.

The invention discloses a mobile intelligent control spraying and dust-settling system, and particularly relates to the technical field of compensation or utilization of an external environment of a spraying and dust-settling spraying vehicle under a wind field disturbance condition, which comprises a plurality of wind speed and wind direction sensing nodes mounted on a lifting rod in the advancing process of the spraying and dust-settling spraying vehicle, acquiring a plurality of local wind field parameters in different heights and directions, and constructing a wind speed gradient data set based on the local wind field parameters; and performing normalization and disturbance feature extraction processing on the wind speed gradient data set to generate a wind disturbance time sequence and a spatial disturbance distribution map. By constructing a time-space coupling control mechanism fusing wind disturbance feature extraction, trend prediction and path calibration, advanced sensing of non-steady-state wind field disturbance and dynamic correction of spray response parameters are achieved, and therefore the problems of sensing-control coupling lag and spray offset errors are solved.

The invention relates to an unmanned aerial vehicle control system and method for formation flight in a wind environment. The method comprises the following steps: generating a wind disturbance environment with a specified map scale and containing random obstacles; acquiring external image data and inertial measurement data of the unmanned aerial vehicle, and generating a grid map through point cloud extraction and conversion operation; the method comprises the following steps: modeling a spatial topological structure of an unmanned aerial vehicle cluster by adopting an undirected graph mode, and solving and generating a collision-free initial trajectory by adopting an unconstrained discrete optimization mode; optimizing the initial trajectory by adopting a task redistribution strategy, and re-planning a global target and a local target to obtain a re-planned optimized trajectory; and based on the optimized trajectory, solving by adopting a model prediction control mode to obtain an optimal control quantity for unmanned aerial vehicle bottom layer control so as to correspondingly control the motion state of each unmanned aerial vehicle and realize formation flight. Compared with the prior art, the unmanned aerial vehicle cluster can efficiently and accurately realize formation flight in an obstacle environment with wind influence.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an aerial work six-degree-of-freedom mechanical arm and a self-adaptive anti-interference control method and system.The self-adaptive anti-interference control method comprises the steps that a mechanical arm structure is manufactured through an arm body designed in a carbon fiber topological optimization mode and a joint shell provided with a flow guide groove; constructing a dynamic disturbance observer based on a DDPG algorithm, outputting a dynamic compensation amount through the dynamic disturbance observer, calculating a wind power disturbance value in combination with a disturbance compensation algorithm, and integrating the wind power disturbance value to a control law of the unmanned aerial vehicle; adjusting impedance parameters on line based on a TD3 algorithm; and generating a real-time obstacle avoidance path based on an optimized multi-target PPO path planning algorithm, and executing a path planning task of the corresponding real-time obstacle avoidance path. The unmanned aerial vehicle has the advantages that the wind disturbance resistance is remarkably enhanced, the load sudden change stability is remarkably optimized, the task energy consumption is remarkably reduced, the narrow space adaptability is remarkably improved, and the light weight is achieved.

The invention provides a four-rotor unmanned aerial vehicle anti-wind-disturbance robust flight control method and system, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting the real-time state data of an unmanned aerial vehicle, the real-time state information comprises the position, speed, height, three-axis attitude angle and three-axis angular speed; acquiring an expected inspection track instruction, generating an expected attitude angle instruction and a total lift instruction in combination with the real-time state data, performing wind disturbance estimation by adopting a neural network, and compensating the expected attitude angle instruction and the total lift instruction; calculating a control torque increment by adopting an increment backstepping method based on the expected attitude angle instruction and the real-time state data, and obtaining a control torque instruction by combining the control torque at the previous moment; and the control torque instruction and the total lift instruction are distributed to the rotating speeds of four motors of the four-rotor unmanned aerial vehicle, and the motors execute the rotating speed instructions to drive the unmanned aerial vehicle to complete a wind disturbance resisting flight task. The method can actively adapt to wind field changes, and it is guaranteed that the unmanned aerial vehicle can still fly accurately, stably and safely under disturbance.

The invention relates to the technical field of trajectory analysis, in particular to a trajectory aiming trajectory analysis method which comprises the following steps: acquiring wind speed time-varying data and centroid parameters, calculating wind disturbance stress and screening a maximum value, judging a rotating direction trend, executing pitch angle consistency judgment, and calculating an angle adjustment rate to generate boundary parameters. The method comprises the steps of analyzing a target speed trend to judge delay, calculating a deviation trend difference value, extracting wind disturbance sudden change to mark a high-sensitivity zone, judging that deviation coincides with the high-sensitivity zone, and generating a dynamic correction instruction set. Modeling analysis is carried out on a target delay trend by combining a direction included angle and mode difference change in a target speed trend sequence, and a trajectory correction mapping relation is constructed by combining flight parameters and timestamps, so that the adjustability and striking precision of an overall hit path are effectively improved.

The invention provides an unmanned aerial vehicle two-dimensional plane anti-wind disturbance trajectory correction method, device and equipment and a medium, and the method comprises the steps: obtaining the wind field collection information of an unmanned aerial vehicle corresponding to the current moment, the wind field collection information comprising wind speed data and wind direction data; determining main wind field mode data of the unmanned aerial vehicle corresponding to the current moment based on the wind field acquisition information of the unmanned aerial vehicle corresponding to the current moment; performing wind field change prediction on the main wind field mode data of the unmanned aerial vehicle corresponding to the current moment to obtain wind field change data in a preset time period; and determining the optimal thrust compensation amount of the unmanned aerial vehicle corresponding to the current moment based on the wind field change data in the preset time period, and generating a correction control instruction based on the optimal thrust compensation amount, so as to perform wind disturbance resistance track correction on the unmanned aerial vehicle by adopting the correction control instruction. Therefore, the accuracy of wind disturbance resistance track correction of the unmanned aerial vehicle is effectively improved, and the flight safety is ensured.

The invention provides an intelligent pod control method and system for an unmanned aerial vehicle. According to the method, multiple frames of thermal radiation images are obtained through an infrared imaging module of an unmanned aerial vehicle pod, a dynamic temperature field atlas is generated through heat source decoupling, and spatial relevance between heat sources is analyzed through an atlas neural network. Damping torque matched with wind disturbance is generated based on the temperature gradient vector, filtering parameters of the magnetofluid holder are optimized, and meanwhile the interference intensity between energy transmission paths is calibrated. A threat propagation path is generated by combining thermal diffusion characteristics, and the pod deflection angle is dynamically adjusted, so that the center of an optical view field is synchronized with the threat centroid, and accurate tracking and anti-interference control are realized. The method is used for intelligent pod control of the unmanned aerial vehicle. Through intelligent heat source analysis and adaptive adjustment, accurate and stable tracking of a threat target by the unmanned aerial vehicle pod is realized.

The invention discloses a double-machine cooperative hoisting operation guiding system and method based on Beidou positioning, and the system comprises a coordinate data implantation unit which is used for pre-storing a standardized coordinate template with environment compensation parameters; the sensor network is used for acquiring crane position, load and environment data in real time; the crane positioning and guiding unit is used for generating a multi-dimensional environment correction factor, dynamically comparing coordinates, weighting and calculating deviation, and planning a synchronous walking path through a multi-objective optimization algorithm; and the hoisting control unit is used for constructing a synchronous control model containing moment balance and kinematics constraint and adjusting hoisting parameters by adopting a closed-loop algorithm. According to the method, millimeter-level positioning guiding and double-machine load self-adaptive balancing can be achieved, segment inclination and wind disturbance influences are effectively restrained, hoisting synchronization precision and operation safety are remarkably improved, high-steady-state anti-inclination control can be completed without newly adding hardware, and the method has high engineering practicability and popularization value.

The invention provides a non-linear flight control method and system for an anti-wind disturbance unmanned aerial vehicle. The method comprises the following steps: acquiring wind field disturbance characteristics in real time by using a preset meteorological radar; inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic torque fluctuation prediction value; preset dynamic filtering processing is applied to the amplitude characteristic of the aerodynamic torque fluctuation predicted value so as to generate a control instruction correction signal; and the control instruction correction signal is coupled with a preset nonlinear control law of an unmanned aerial vehicle flight control system, so that unmanned aerial vehicle attitude offset caused by wind disturbance is inhibited in real time, and unmanned aerial vehicle nonlinear flight control is carried out. According to the method, the composite defect of hysteresis and filtering mismatch of traditional linear control in a strong wind sudden change scene is overcome, stable attitude control of the unmanned aerial vehicle in low-altitude wind shear and gust sudden attack is realized, the anti-interference response speed of the unmanned aerial vehicle is remarkably improved, and the out-of-control risk of the unmanned aerial vehicle is greatly reduced.

The invention discloses an unmanned aerial vehicle steering effect analysis method based on wind disturbance, and belongs to the technical field of unmanned aerial vehicle overall design and flight control. Comprising the following steps: 1, constructing a coupling simulation model; step 1.1, constructing a six-degree-of-freedom rigid body dynamic model; step 1.2, integrating control laws; step 2, dynamically injecting a wind disturbance scene; step 3, dynamically evaluating the steering effect and extracting parameters; step 4, carrying out multidisciplinary collaborative optimization; and 5, carrying out closed-loop iteration verification. Dynamic closed-loop test: breaking through traditional static analysis, realizing real-time interactive simulation of wind disturbance-control-steering effect, and accurately capturing nonlinear transient response; multi-source disturbance fusion: discrete gust and continuous turbulence are compositely injected, so that challenges of complex meteorological conditions on steerage are reflected more truly; and cross-domain parameter linkage: through dynamic iteration of pneumatic, control and structural parameters, system performance loss caused by excessive design of a single specialty is avoided.

The invention relates to the technical field of aerostat control, and discloses an aerostat control method and system, and the method comprises the steps: S1, collecting the internal state data, external environment data and environment parameters of an aerostat through a multi-source sensor; s2, processing the data acquired in the S1, and constructing a state and disturbance observer to obtain an aerostat state estimation value and an environmental wind disturbance estimation value; s3, based on the state estimation value and the wind disturbance estimation value of the aerostat, a self-adaptive control algorithm is adopted, a control instruction used for controlling the height and the horizontal position of the aerostat is generated, and the control instruction comprises a feedforward component used for actively compensating wind disturbance; and S4, distributing the control instruction to a channel power system of the aerostat and executing the control instruction. An integrated control architecture with state estimation, disturbance observation and adaptive control capabilities is constructed, three technical bottlenecks of weak control robustness, poor anti-interference capability and low intelligent level of a traditional aerostat in outdoor application are effectively solved, and the positioning precision and stability are improved.

The invention discloses an unmanned aerial vehicle anti-wind disturbance controller and a control method, and belongs to the technical field of unmanned aerial vehicle flight control. The method runs on an independent processing module which is connected in series between a main flight controller and an electronic speed regulator of the unmanned aerial vehicle, a dynamic reference is constructed by collecting motor current in real time, deviation is calculated, and wind disturbance equivalent disturbance torque is estimated in a prospective mode through a third-order linear extended state observer (LESO) with bandwidth parameterization design; in combination with a'user operation state + flight mode 'two-dimensional intelligent decision compensation strategy, a'look-ahead wind resistance + attitude backup' dual-redundancy channel is matched, so that the wind disturbance is compensated in advance, and the flight stability in a complex scene is ensured. A corresponding controller adopts a standardized independent hardware design, the adaptability is high, the integration cost is low, and the method can be widely applied to high-precision operation scenes of various multi-rotor unmanned aerial vehicles.

The invention relates to the technical field of artificial intelligence, and discloses an unmanned aerial vehicle flight control-oriented intelligent obstacle recognition method, which comprises the following steps of constructing an adaptive wind disturbance fuzzy kernel based on unmanned aerial vehicle attitude data, and synthesizing a degraded image for training; a specific obstacle recognition model integrating a frequency domain and space domain joint feature decomposition module, a turbulence invariant feature enhancement module guided by physical prior information and a double-branch anti-fuzzy feature extraction network is adopted, and a fuzzy robustness contrast loss function is combined to carry out progressive training so as to learn feature representation insensitive to fuzziness; after the trained model is deployed, the wind disturbance fuzzy intensity of a real-time input image is estimated on line, the internal workflow of the model is dynamically adjusted according to the wind disturbance fuzzy intensity, the attention weight is adaptively adjusted, and the output of a trunk or an anti-fuzzy branch is selected. According to the invention, through coupling of the physical model and deep learning, the recognition robustness, accuracy and stability of the model in a dynamic wind disturbance environment are improved.

The invention relates to an unmanned aerial vehicle attitude control method based on motor state disturbance compensation, belongs to the technical field of unmanned aerial vehicle control, and solves the problems of hysteresis and limited control effect of an existing unmanned aerial vehicle attitude control method. The position controller calculates to obtain expected tension, a roll angle and a pitch angle; the attitude feedback controller calculates a feedback torque according to the expected attitude, the actual Euler angle and the body angular velocity; the load identification module calculates an estimated disturbance torque based on the target rotating speed, the actual rotating speed and the current of each motor; the feedforward neural network module adopts a trained RBF neural network to calculate torque residual error estimation, and torque synthesis is carried out to obtain a torque feedforward compensation amount; adding the feedback torque and the feed-forward compensation amount to obtain a total control torque; and the mixing controller calculates the target rotating speed of each motor according to the expected tension and the total control torque, and finally the control distributor drives the motors to execute. The attitude stability and the control response speed of the unmanned aerial vehicle in the wind disturbance environment are remarkably improved.

The present application relates to the field of unmanned aerial vehicle flight control, more particularly, the present application relates to unmanned aerial vehicle flight control method and system based on unmanned aerial vehicle search and rescue platform, the method comprises: based on the preset time window calculates the current attitude stability and wind resistance consumption of unmanned aerial vehicle, and then the excess proportion is obtained and the comprehensive wind resistance index is calculated, according to which the weak machine is judged; Then calculate the next moment of weak machine traction (current traction minus release amount) and strong machine next moment of traction (original traction plus weak machine release amount total sum of increments), complete the reassignment of unmanned aerial vehicle cluster traction. The present application can intelligently evaluate the wind resistance of unmanned aerial vehicle and dynamically adjust the traction distribution, combined with accurate prediction and environmental adaptation, significantly improve the task execution stability and reliability of unmanned aerial vehicle cluster in wind disturbance environment, ensure that the task allocation is more scientific and reasonable.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle multi-load synchronous control system, and the system comprises a linkage control unit which is used for detecting an image shot by an unmanned aerial vehicle, obtaining a camera imaging plane deviation according to a detection result, and combining with a kinematic model to solve an expected instruction of the unmanned aerial vehicle and a holder; the multi-load configuration unit is used for determining control logic according to the load type and the expected instruction and issuing an operation instruction to the load according to the control logic so as to realize linkage control of the load and the unmanned aerial vehicle controller; the control instruction adjusting unit is used for acquiring the flight state of the unmanned aerial vehicle when the expected instruction is executed, and calculating a disturbance value of the unmanned aerial vehicle to determine a final control instruction of the unmanned aerial vehicle by taking the flight state as input; the equipment is an airborne computing power unit. The active-disturbance-rejection pose correction technology is introduced, real-time compensation of disturbance such as wind disturbance, load sudden change and hanging point deviation is achieved, and the working efficiency and the complex scene adaptability are improved.

The application discloses a quad-rotor unmanned aerial vehicle attitude fault-tolerant control method considering unknown wind disturbance, and relates to the field of unmanned aerial vehicle attitude control design; the method can effectively cope with unknown wind disturbance, actuator partial failure and deviation faults. In the application, unknown wind disturbance and deviation faults are regarded as total disturbance, an improved disturbance compensator is designed by using an adaptive mechanism for the total disturbance, and the size of switching gain is adjusted online according to the control accuracy requirement, so that the waste of controller output energy caused by excessively large switching gain is avoided. For actuator partial failure, a failure factor compensator designed based on an adaptive method enables the unmanned aerial vehicle to timely process faults and recover normal trajectory. An attitude controller is designed by using a non-singular fast terminal sliding mode control technology, so that the robustness and fast response characteristics are ensured, the unmanned aerial vehicle can timely process faults, the fault tolerance of the unmanned aerial vehicle is enhanced, and the flight safety of the unmanned aerial vehicle is improved.

The invention discloses a mooring unmanned aerial vehicle take-up and pay-off anti-interference control method based on image recognition, and relates to the technical field of unmanned aerial vehicle cable anti-interference control systems. Comprising the following steps: acquiring a wind speed, a wind direction, a cable direction, an actually measured tension, a winch speed and a cable image with aligned timestamps, and projecting the wind speed along the cable direction to obtain a wind disturbance component along the cable; inputting the actually measured tension, the winch speed and the wind disturbance component into a tension prediction model to output predicted tension at the next moment; comparing the predicted tension with a safety interval to adjust the take-up and pay-off speed of the winch; and in combination with the image, identifying the cable form, performing visual correction on the winding and unwinding speed of the winch through loosening eliminating logic and vibration suppression logic, and issuing the corrected winding and unwinding speed of the winch to execute and synchronize to the unmanned aerial vehicle so as to adaptively cooperate with the winding and unwinding action of the winch. Through tension prediction and multi-link evaluation adjustment, the problem that the unmanned aerial vehicle shakes and drifts in the air due to the fact that the tension is abnormal and the take-up and pay-off speed is not matched is effectively solved.

The invention relates to the field of remote control, and discloses an intelligent bucket throwing operation method and system for remotely controlling a grab ship unloader, which are used for realizing intelligent bucket throwing operation for remotely controlling the grab ship unloader. The intelligent bucket throwing operation method for remotely controlling the grab ship unloader comprises the steps of generating a full-state expected trajectory containing position, speed and acceleration information, ensuring that the trajectory is smooth and accurate, constructing a nonlinear model prediction controller, integrating a nonlinear disturbance observer, effectively estimating and compensating wind disturbance and unmodeled dynamics, and enhancing the anti-jamming capability of the system. A discrete element method is utilized to simulate interaction of material particles, and in combination with bilge topographic data, spatial distribution after material throwing is accurately predicted, system stability is monitored in real time, and a control instruction is dynamically adjusted. The precision, the stability and the efficiency of bucket throwing operation of the grab ship unloader are remarkably improved, the operation difficulty and the labor cost are reduced, and the grab ship unloader can well adapt to complex working conditions.

The invention relates to the field of unmanned aerial vehicle automatic landing control, and discloses an unmanned aerial vehicle wind-resistant landing control system which comprises an external wind-resistant structure module arranged on the periphery of an unmanned aerial vehicle landing area, and the external wind-resistant structure module adopts a fixed conical cover body design; the external windproof structure module guides external airflow into the landing area, and the size and the height of the top opening of the conical cover body are shown in the specification. A local low-wind-speed environment is constructed through the external windproof structure module, the wind speed of a landing area is effectively reduced, and the wind speed of the landing area is effectively reduced in combination with real-time estimation and accurate control of the environment perception and decision control module on the external wind speed and the tail-section attitude correction capability of the attitude correction and re-landing strategy module. The landing point alignment accuracy of the unmanned aerial vehicle under the condition of crosswind, gust or sudden wind direction change is remarkably improved, and horizontal drifting and landing failure caused by wind disturbance are effectively avoided.

The invention discloses an unmanned aerial vehicle flight control method and system based on a Mangbar and a dual-channel attention mechanism under wind field disturbance, and particularly relates to the technical field of unmanned aerial vehicle autonomous navigation and intelligent control. The method comprises the following steps: constructing a wind field disturbance model based on computational fluid mechanics, and simulating an unsteady wind field environment among urban building groups; designing a deep reinforcement learning network fusing a Mama framework and a double-channel attention mechanism, wherein the deep reinforcement learning network is used for realizing dynamic modeling of long-sequence wind field features and attention weighting of key spatial-temporal features; a dynamic road sign point guiding mechanism and a multi-stage attenuation greedy strategy are put forward, the sparse reward problem is relieved, and the local path optimization capability is improved; and a multi-target composite reward function including energy constraint, safety distance and path efficiency is constructed, and global path planning of energy consumption perception is realized. The method can improve the flight stability, path smoothness and energy efficiency of the unmanned aerial vehicle in a strong wind disturbance environment, and is suitable for unmanned aerial vehicle autonomous navigation tasks in an urban complex environment.

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