16523 results about "Vehicle control" patented technology

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention relates to the field of intelligent driving, in particular to a driver dangerous behavior intervention system and method based on a space-time diagram neural network, and the method comprises the steps: obtaining a physiological signal, a driving posture and control behavior data through a multi-modal data collection module; constructing a dynamic graph structure and evaluating a driver risk state by using a space-time diagram neural network risk perception engine; the intelligent intervention execution module adopts a tactile, visual or vehicle control intervention strategy according to the evaluation result; the closed-loop optimization module monitors an intervention effect and updates a risk model and a strategy; according to the system, millimeter wave radar and multi-channel visual analysis are fused, the recognition accuracy is improved to 98.6%, dangerous events are predicted 15-30 seconds in advance through physiological signals and micro-expression analysis, and sufficient response time is provided for a driver.

The invention discloses a control method and system based on multi-unmanned aerial vehicle cooperative game confrontation, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: S1, game environment modeling and simulation, S2, game strategy generation and optimization, S3, dynamic task allocation and cooperative control, S4, communication and information interaction optimization, and S5, efficiency evaluation and adaptive adjustment. On the basis of realizing cooperative game confrontation of multiple unmanned aerial vehicles, task allocation can be efficiently carried out on the unmanned aerial vehicles, and the autonomous decision-making capability of the unmanned aerial vehicles can be improved.

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention relates to the technical field of unmanned aerial vehicle track control, in particular to an unmanned aerial vehicle track control method and system. The method comprises the following steps: firstly, acquiring flight braking parameters and distribution task data of an unmanned aerial vehicle, and extracting low-altitude distribution meteorological condition feature data; then, flight state simulation is carried out based on flight braking parameters and task data, simulation quantification is carried out on flight attitude instability in combination with meteorological conditions, and then trajectory avoidance behavior control learning is carried out; and finally, constructing a trajectory control model by applying a strategy gradient algorithm, and sending the trajectory control model to an unmanned aerial vehicle control center to execute trajectory control. According to the invention, the unmanned aerial vehicle trajectory control technology is optimized, so that the unmanned aerial vehicle trajectory control technology is more perfect.

A diagnostic system and method for identifying and resolving vehicle Controller Area Network (CAN) bus faults is disclosed. The system connects to a vehicle's data link connector (DLC) and automatically retrieves diagnostic trouble codes (DTCs) from multiple electronic control units (ECUs). It filters the DTCs to identify those related to CAN communication errors, groups repeated DTCs across ECUs to highlight systemic faults, and assigns repair priority based on status, scope, and criticality. A user interface displays prioritized DTCs with definitions, affected systems, possible causes, and repair guidance. Upon technician input, the system re-scans the network to confirm resolution and updates the display accordingly. The invention supports semantic and conceptual recognition of communication faults, enabling robust, language-agnostic analysis. This system reduces diagnostic time, improves repair accuracy, and supports multilingual or manufacturer-specific DTC definitions without requiring external infrastructure or advanced user expertise.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

The invention discloses a vehicle stopping electrical safety monitoring system, and relates to the technical field of new energy vehicle control, the vehicle stopping electrical safety monitoring system comprises an intelligent sensing layer, a digital twin decision-making layer, a hierarchical execution layer and a safety escape layer, and the intelligent sensing layer comprises a multi-mode sensor array and is used for collecting physical parameters, chemical parameters and environmental parameters of a vehicle electrical system in real time. According to the method, a high-precision three-dimensional model, an embedded electrolyte leakage model and a wire harness aging model are constructed, the high-precision three-dimensional model comprises a battery compartment geometric structure and a wire harness layout, the former simulates a diffusion path and predicts a short-circuit risk based on fluid dynamics, and the latter realizes fault evolution prediction based on a physical mechanism by combining vibration fatigue and insulation material aging analysis. The hysteresis of traditional threshold value alarm is broken through, a dynamic threshold value table is generated in combination with environment temperature, the charge state and historical fault data, the problem that a traditional fixed threshold value cannot adapt to complex working conditions is solved, and the false alarm rate and the missing alarm rate are reduced.

The present invention provides an unmanned aerial vehicle-based electric power patrol inspection control method and system. The method comprises: creating a flight path on the basis of an electric power patrol inspection area, and collecting images and position information of electric power devices by means of various types of devices carried by an unmanned aerial vehicle, the images and the position information being transmitted to a receiving terminal at a ground station in a wireless transmission mode; the ground station receiving the images and the position information that are collected in real time, processing and analyzing the images to obtain analytic status, such as defects, damage and corrosion, of the electric power devices; and, on the basis of the analytic status from the ground station, using the position information as distinguishing features to generate an electronic patrol inspection report, providing service lives, maintenance costs and maintenance measures of the electric power devices on the basis of the electronic patrol inspection report, and notifying maintenance staff of the position information of the electric power devices. The system comprises an unmanned aerial vehicle control module, an image processing and analysis module and an electronic patrol inspection report generation module. The present invention is of great significance to maintenance and management of electric power devices.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a task allocation and conflict resolution system and method for multi-unmanned aerial vehicle collaborative operation, and provides the following scheme: dividing an initial operation area and generating a response weight by constructing a crop growth state map and a three-dimensional plot model; based on path planning and resource adaptation, a flight route is dynamically generated, and the crop state and the unmanned aerial vehicle state are monitored in real time; when adjustment conditions are met, a multi-dimensional dynamic task evaluation model is constructed, and task migration and conflict decoupling are completed in combination with particle swarm optimization and an autonomous negotiation mechanism. The method is suitable for a precision agriculture scene, the unmanned aerial vehicle path dynamic scheduling in the operation area and the high-priority area precision coverage are realized, and the operation efficiency and the resource cooperation capability are improved.

Provided are a vehicle control method and apparatus, and a vehicle-mounted device, a vehicle and a storage medium. The vehicle control method comprises: on the basis of environmental perception data, position data of a vehicle, and map data, determining behavior trajectory information of a target object in an environment where the vehicle is located; sending the behavior trajectory information of the target object and ego-vehicle trajectory information of the vehicle to a server, so that the server processes the behavior trajectory information of the target object and the ego-vehicle trajectory information on the basis of a target model to generate planning and decision-making information, wherein the target model is a generative pretrained transformer and is obtained by means of performing training on the basis of a plurality of types of training sample data; receiving the planning and decision-making information fed back by the server, and on the basis of the behavior trajectory information of the target object and the planning and decision-making information, generating planning trajectory information and driving strategy information for the vehicle; and on the basis of the planning trajectory information and the driving strategy information, controlling the vehicle to travel. The safety of autonomous driving and the driving and riding experience of users can be improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a low-altitude operation unmanned aerial vehicle cooperative control method, which comprises the steps of acquiring a broadband electromagnetic signal of a surrounding environment of a communication iron tower; identifying electromagnetic interference components of the broadband electromagnetic signals based on spectrum characteristic analysis, and decomposing characteristic parameters of the electromagnetic interference components; positioning the spatial orientation of an interference source according to the frequency spectrum energy difference of electromagnetic interference, and constructing an electromagnetic risk topological graph in combination with a three-dimensional point cloud model of the communication iron tower; generating a dynamic compensation amount to correct the trajectory of the unmanned aerial vehicle by using the electromagnetic interference component characteristic parameters, and reconstructing the unmanned aerial vehicle cooperative formation topology based on the interference source spatial orientation; and generating an anti-interference obstacle avoidance constraint according to the electromagnetic risk topological graph and the reconstructed cooperative formation topology, and cooperatively controlling the unmanned aerial vehicle to complete scanning in a preset trajectory interval. The technical problems of unmanned aerial vehicle flight attitude instability and high collision risk caused by communication iron tower electromagnetic environment interference are solved, and rapid and accurate cooperative control of communication iron tower unmanned aerial vehicle low-altitude operation patrol inspection is realized.

The invention relates to an audio data processing method and device, electronic equipment and a vehicle, and is applied to the technical field of computers. The audio data processing method comprises the following steps: acquiring target voice data of a user; obtaining at least one grammar element corresponding to the target voice data, and determining a grammar structure integrity score corresponding to the target voice data based on the at least one grammar element; determining an environment interference score corresponding to the target voice data; determining a confidence score corresponding to the target voice data based on the grammatical structure integrity score and the environmental interference score; and under the condition that the confidence score is greater than or equal to the target confidence threshold, determining to execute a target control instruction corresponding to the target voice data, thereby determining whether the target voice data is an instruction for vehicle control by combining the grammatical structure integrity of the voice data and the environmental interference degree. The accuracy of vehicle control and the interaction experience of the user and the vehicle are improved.

Various electric energy supply vehicles and electric energy supply vehicle control system; a to-be-replaced electric vehicle and an electric vehicle battery replacement control system; a robot, a robot driving system, a carrying robot and a carrying robot control system; a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of charging and replacing cabinets, a charging and replacing cabinet control system, a plurality of programmable controllers, a plurality of wireless programmable controllers, a plurality of to-be-replaced electric vehicles with different structures, a constructed electric vehicle finding mode, a plurality of electric vehicles to be subjected to battery replacement, a construction battery swapping station link is omitted, one electric energy supply vehicle can provide replenishment for a plurality of electric vehicles, an electric energy supply vehicle is put according to the amount of electric vehicles, invalid investment is avoided, and a vehicle owner does not need to center the battery again.

A control system for controlling a second unmanned aerial vehicle that flies while holding a first cable connected to a first unmanned aerial vehicle that performs work and a second cable extending from a cable reeling machine, includes a sensor to sense a surrounding environment and output sensor data, and a controller configured or programmed to control operation of the unmanned aerial vehicle and, during flight of the second unmanned aerial vehicle, detect the first cable and the second cable based on the sensor data, and upon predicting that at least one of the first cable and the second cable will contact the ground or an obstacle on the ground, change a trajectory of the second unmanned aerial vehicle to avoid the contact.

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

The invention provides a range extending type tractor power system and a control method, and solves the problems that in the prior art, a tractor power system is low in energy conversion rate, a stepless speed change structure is complex to achieve, walking drive PTO drive cannot be coupled, and multiple controllers are difficult to cooperate. The range extender is electrically connected with the power battery and can provide electric energy for the power battery; the motor assembly comprises a driving motor and a PTO motor; the control assembly comprises a vehicle control unit, a range extender controller and an all-in-one controller, the range extender controller and the all-in-one controller are connected with the vehicle control unit, the range extender controller is electrically connected with the range extender, the all-in-one controller is electrically connected with the power battery, the range extender and the motor assembly, and the vehicle control unit controls the range extender controller and the all-in-one controller according to specific working conditions. The range extender is controlled by the range extender controller to supply power to the power battery or / and the all-in-one controller, and then the all-in-one controller is used for carrying out power distribution on the driving motor and the PTO motor.

Methods and systems for optimizing an action policy of an autonomous vehicle machine learning model. Images are generated corresponding to an environment about a vehicle. These images are passed through an image encoder to generate image-based embeddings of the current state of the vehicle. A text prompt representing a goal of the autonomous vehicle is passed through a text encoder to generate text-based embeddings of the goal. A similarity score is determined, representing a similarity between the image-based embeddings of the current state and the text-based embeddings of the goal. A reinforcement learning model for a closed-loop autonomous driving task is executed, with the similarity score used as the reward function. An action policy corresponding to a control of the vehicle is optimized based on the reward function.

The invention discloses a cloud control self-driving automobile control method considering automobile cloud communication time delay. By constructing a time delay prediction model based on historical communication delay data, the back-and-forth communication time delay at the current moment is accurately predicted, and forward compensation is carried out on the vehicle state by adopting an iterative prediction formula in combination with a vehicle transverse dynamic model. According to the method, a process disturbance correction item is introduced, system modeling errors caused by environment disturbance are effectively corrected, and the state prediction precision is improved. And deducing a future vehicle state through the predicted time delay step number in combination with a historical vehicle state and control input so as to realize dynamic compensation of the time delay influence. The method effectively solves the problems of control lag and state information lag caused by communication delay in vehicle-cloud cooperative control, improves the control real-time performance and precision of a cloud control automatic driving system, enhances the robustness and safety of the system in a complex environment, and is suitable for automatic driving scenes with high dynamic and high safety requirements.

The invention discloses an unmanned aerial vehicle inspection method and system applied to foundation pit accumulated water monitoring, and the method comprises the steps: determining an optimal route node sequence, and generating an unmanned aerial vehicle control instruction set; acquiring internal and external parameters of a multi-source sensor, and performing time alignment compensation on image frames and sensor data; acquiring a ponding area image coordinate set and attribute data; a three-dimensional projection point set of the ponding area is obtained, and the real coverage area of the ponding area is calculated; generating a three-dimensional overview map of the foundation pit by using the multi-view image of the foundation pit area; determining a three-dimensional coordinate point set and an area estimation result of the ponding area; and projecting the three-dimensional coordinate point set of the ponding area to the three-dimensional overview map of the foundation pit to generate a hidden danger monitoring report. By integrating the unmanned aerial vehicle, the sensor data and the advanced image processing technology, the accuracy, stability and visualization effect of foundation pit accumulated water monitoring are greatly improved, more reliable and comprehensive technical support is provided for safety management of foundation pit engineering, and the safety risk in the construction process is remarkably reduced.

An autonomous vehicle control system includes one or more sensors configured for coupling with an agricultural vehicle, the one or more sensors configured to determine kinematics of the agricultural vehicle relative to a crop row. The system includes a guidance control module configured to coordinate steering of one or more steering mechanisms of the agricultural vehicle. The guidance control module includes a sensor input configured to receive kinematics of the agricultural vehicle, a vehicle kinematics comparator configured to determine one or more error values using the received vehicle kinematics, a crop curvature generator configured to determine crop row curvature using the one or more error values, and a steering interface configured to provide instructions to a vehicle steering controller to guide the agricultural vehicle using the crop row curvature.

The invention relates to a vehicle queue cooperative control method fusing deep reinforcement learning communication time delay compensation, and belongs to the technical field of intelligent vehicle control. For the problems of control error accumulation and queue instability caused by communication time delay in the vehicle-road cloud cooperative system, a solution integrating deep reinforcement learning prediction and preset performance control is provided. According to the technical scheme, the method comprises the following steps: constructing a vehicle state prediction model based on a DDPG algorithm, and generating a time delay compensation amount in real time; establishing a vehicle dynamics model containing communication time delay, and designing a sliding mode controller based on a barrier function; and dynamically adjusting the control constraint in combination with an adaptive error boundary mechanism. According to the method, through closed-loop fusion of prediction compensation and dynamic constraint, under the scene of burst communication delay (100-400 ms), the queue spacing error is reduced by 62%, the speed tracking error is still kept to be smaller than 0.3 m / s, and the queue stability and the anti-jamming capability are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention discloses a vehicle stability adaptive control method under low attachment road surface multi-source interference, and belongs to the field of vehicle control, and the method comprises the following steps: S1, multi-source interference perception and attachment coefficient prediction; s2, dynamic correction of tire dynamic parameters: correcting tire cornering stiffness and slippage stiffness on line; s3, multi-source interference collaborative observation and estimation: dynamically estimating an interference source by using an enhanced extended state observer; s4, phase plane stability envelope analysis and weight adjustment: dynamically allocating control priorities; and S5, on the premise of considering an interference source, performing dynamic torque distribution and actuator cooperative control: optimizing four-wheel torque by using a quadratic programming algorithm to obtain a torque distribution result. By adopting the vehicle stability adaptive control method under the low-attached road surface multi-source interference, the problems of insufficient modeling precision, weak interference suppression, target conflict and redundancy deficiency in low-attached road surface vehicle stability control are solved, and the trajectory tracking precision and the yaw stability are remarkably improved.

The invention relates to an expressway shunting area forced lane change decision-making method for an intelligent network connection environment, and the method comprises the steps: 1, extracting a forced lane change track based on a MAGIC data set, and expanding a sample through a TimeGAN model; 2, environment parameters such as time urgency and spatial constraint are extracted, a K-Means clustering method is adopted to classify lane changing scenes, various scene samples are scored in combination with an empirical optimal evaluation system, and optimal behavior samples are screened; and step 3, constructing a lane changing decision model based on a behavior cloning method, and learning an expert driving strategy. According to the method, a fusion modeling framework of'sub-scene + anthropomorphic imitation learning 'is innovatively proposed, and robust behavior generation of the automatic driving vehicle in various typical scenes is realized for a special decision-making task of forced lane changing. The method has the characteristics of data driving, self-adaption and the like, can be embedded into an intelligent network connection vehicle control system, and provides technical support for optimization of a lane changing strategy of the intelligent network connection vehicle control system.

The invention provides a vehicle-mounted oxygen production control method and device, a vehicle-mounted oxygen production system and electronic equipment, and relates to the technical field of vehicle control. The method comprises the steps that the oxygen partial pressure in a cockpit, the altitude of the environment where the cockpit is located and the oxyhemoglobin saturation of passengers in the cockpit are obtained; determining an oxygen supply demand value based on the oxygen partial pressure, the altitude and the oxyhemoglobin saturation; and based on the oxygen supply demand value, an oxygen generation module is controlled to start oxygen generation or stop oxygen generation. The starting and stopping of the oxygen generation module are controlled based on the oxygen supply demand value, waste caused by starting of the oxygen generation module when oxygen supply is not needed can be avoided, and therefore the energy consumption of vehicle-mounted oxygen generation can be greatly reduced.

The invention relates to a vehicle control method and device, computer equipment and a storage medium. The method comprises the steps of obtaining target crosswind information of crosswind acting on a vehicle according to driving information of the vehicle, weather information of a scene where the vehicle is located and scene image information; determining a target yaw velocity of the vehicle according to the vehicle state information of the vehicle; determining a first angular velocity difference value between the target yaw velocity and an actual yaw velocity obtained through a sensor of the vehicle, and determining driving condition information of the vehicle according to the first angular velocity difference value; and target control information of the vehicle is obtained by using the first angular velocity difference value and / or the target crosswind information according to the driving working condition information of the vehicle. By adopting the method, the driving stability of the vehicle in the crosswind environment can be improved.

The invention provides a multi-rotor unmanned aerial vehicle adaptive attitude stability control algorithm and device, and relates to the technical field of unmanned aerial vehicle control. The control algorithm comprises a geometric phase driving control process, a fractional order chaos game control process, a manifold resonance self-adaptive control process, a topology manifold self-adaptive control process and a multi-scale disturbance prediction control process, and all the processes of the control algorithm are executed through the main control processing unit. In order to solve the problems that a traditional PID control attitude error is 0.02-0.05 degree and is difficult to adapt to a dynamic environment, a geometric phase drive control flow is combined with a plasma pneumatic fine tuning array and an inertial measurement unit, a high-precision 3 * 3 rotation matrix is generated by adopting Lie algebraic transformation and a Newton-Rafson iteration method, the rotation speed of a rotor wing is optimized, and the precision of the rotor wing is improved. The attitude error is controlled to be 0.006 degrees, and the effect is improved compared with a traditional PID control effect.