1662 results about "Yaw" patented technology

The invention discloses a forklift operation control optimization method and system based on a disturbance observation technology, and relates to the technical field of electric forklift control, and the method comprises the steps: collecting the operation data of a forklift operation process in real time, sensing and fusing through multi-source sensing information, analyzing the state variable of forklift movement, and building a dynamic parameter mapping relation; taking the measured value and the predicted value of the forklift all-condition dynamic model as input, constructing a high-order sliding-mode observer, evaluating disturbance source interference, and generating a motion compensation correction amount; a feed-forward-feedback fused composite controller is constructed, a yawing moment pre-compensation instruction is generated based on a disturbance source disturbance evaluation result, and a forklift trajectory tracking error is dynamically corrected; and monitoring the distance between the operation point of the forklift and the instability boundary in real time, and triggering a torque redistribution strategy when the distance is close to the instability boundary. Through deep fusion of real-time sensing data, dynamic modeling and an intelligent control algorithm, the working performance of the forklift under complex working conditions is remarkably improved.

A method of controlling drift of a vehicle in which the vehicle more easily enters, maintains, releases, and transitions drift behavior, includes determining, by a controller, whether a current driving situation of the vehicle is a drift entry situation or a release situation based on vehicle driving information, and generating a drift index configured to indicate a current degree of drift from a determination result, determining, by the controller, a target yaw moment for left and right speed control depending on a driving situation or a braking situation of the vehicle and target yaw moments for drift assistance control depending on a driver input and a vehicle state in the drift entry situation, based on the vehicle driving information, and generating and outputting, by the controller, a target torque of a motor configured to drive each wheel of the vehicle based on the determined target yaw moments.

The invention discloses a vehicle state estimation method based on adaptive strong tracking extended Kalman filtering. The method comprises the following steps: constructing a nonlinear three-degree-of-freedom dynamic model containing a state equation and an observation equation based on an extended Kalman filtering algorithm to describe longitudinal, lateral and yaw states of a vehicle; state variables of the state equation are a side slip angle, a yaw velocity and a longitudinal vehicle speed; constructing a time-varying measurement noise statistical estimator based on a Sage-Husa algorithm to adaptively correct a measurement noise covariance matrix in the extended Kalman filtering algorithm; state prediction is carried out on the vehicle through the state equation, and a state prediction covariance matrix is calculated; and calculating the ratio of the sum of quadratic terms of the information sequence to the trace of the variance matrix of the information sequence based on an extended Kalman filtering algorithm, and judging whether filtering is really divergent or not. According to the method, the state quantity which is difficult to measure in the vehicle driving process is estimated in real time by establishing an adaptive strong tracking extended Kalman filter, and the vehicle state is accurately estimated.

The invention belongs to the technical field of wind tunnel tests, and particularly relates to a precession simulation device for a spiral flutter wind tunnel test. According to the invention, the design problem of the rotor craft spiral flutter wind tunnel test device is solved, and the key influence parameters and influence rules of the spiral flutter of the aircraft are researched by designing the precession motion simulation device of the rotor craft. Rotation of the rotor wings can be realized in the wind tunnel, and the rotating speed of the rotor wings is adjusted through the motor. Meanwhile, pitching and deflection modal decoupling of the rotating shaft of the rotating system is achieved, and pitching and deflection modal frequencies are independently adjustable; meanwhile, the axis of the pitch axis and the axis of the yaw axis coincide, so that pitching and yaw freedom degree coupling precession motion can be achieved under the effect of gyroscopic moment. Meanwhile, nacelle rotating mass rotational inertia, nacelle weight and mass center position adjustment can be achieved; and finally, the rotating speed of the rotating mass such as the rotor wing and the propeller hub can be adjusted through a speed adjusting motor, matching of the rotating speed and the incoming flow speed is guaranteed, and the spiral flutter state is achieved.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably improved.

The invention discloses an online efficient load shedding control method based on a forecast wind field. The method comprises the following steps: firstly, acquiring a load attack angle and a sideslip angle which are calculated in real time on a rocket; amplitude limiting processing is carried out on the load attack angle and the sideslip angle, an attack angle control instruction is generated after gain control network calculation is carried out, and an attitude control system carries out direct attack angle control in a load sensitive area in combination with the load shedding requirement and the guidance deviation correction capacity; and pitching and yaw feedback control instructions are generated, active load shedding control is implemented, and errors generated by attack angle control are compensated. The on-line active and passive load shedding mode covers the real deviation or interference influence during flight, the generated load shedding instruction is better matched with the actual flight, the universality is good, the implementation is easy, and the load shedding effect is better.

The invention discloses an unmanned aerial vehicle automatic test method, which comprises the steps of building a high-fidelity semi-physical simulation platform which comprises an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator, a flight control module and a ground control station, and loading a complete machine fault model and a dynamic environment model; designing an intelligent multi-fault injection scheme, and defining a single fault scene, a combined fault scene and a fault evolution scheme; configuring a dynamic environment simulation scene including static environment parameters, dynamic environment elements and environment, and a fault interaction scene; executing a self-adaptive test process, automatically generating a test case, intelligently scheduling a test sequence and dynamically adjusting test parameters; and performing multi-source data intelligent analysis and evaluation, collecting test data, performing fusion analysis on fault influence, and generating an evaluation report. By constructing a whole machine fault and dynamic environment model, the scene authenticity is improved by 40%, and for example, the motor fault yaw deviation under strong wind is closer to a real machine compared with a traditional method.

The invention relates to a course axis drift suppression method and device, a holder and a medium, and the method comprises the steps: receiving original data of a current course, the original data comprising an absolute angle outputted by a course encoder, and an acceleration and an angular velocity outputted by an IMU module; the original data are converted into a unified coordinate system through a rotation matrix, and mechanical installation deviation is compensated; performing data fusion on the compensated original data through a preset fusion algorithm; and outputting the fused absolute angle to a servo mechanism of the holder, and by adopting the method, through real-time sensor data alignment, a dynamic compensation algorithm and a fusion algorithm, a course drift suppression mechanism which is resistant to dynamic interference and low in cost is constructed, and static drift lt can be realized; the dynamic tracking error is 1t, 0.01 degree / h and 1t; and the technical index is 0.05 degree.

The invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, and belongs to the technical field of artificial intelligence risk prediction. The vehicle is provided with an upper-layer torque decision module, a lower-layer torque cooperation module and a vehicle-mounted sensor; selecting a pivot steering auxiliary strategy according to an actual driving scene, and pressing a function key to enter a working condition; vehicle dynamic information is obtained through a vehicle-mounted sensor, whether a vehicle is in an in-situ steering state or a small-radius steering state is judged by combining vehicle body static data, and when a selected function key is in an in-situ steering state or a small-radius steering state, the vehicle dynamic information is obtained in real time, and fuzzy reasoning and a neural network algorithm are combined. The ground attachment coefficient and the target yaw angle speed are intelligently judged, and therefore accurate control over the steering torque is achieved; or a double-controller structure is adopted, the torque of each wheel can be effectively distributed and adjusted under complex working conditions, and flexible control and stable running of the vehicle in a narrow space are ensured.

The invention discloses a distributed driving automobile control method. The method comprises the steps that 1, dynamic parameters of an automobile are collected in real time, and the equivalent adhesion coefficient of each wheel is calculated; 2, dividing types and optimizing torque distribution according to pavement adhesion coefficients: respectively adopting priority, uniform and dynamic weight distribution strategies for low, high and asymmetric pavements; 3, establishing a multi-objective optimization model, and solving an optimal torque distribution scheme considering the tire utilization rate and the yaw stability by using a quadratic programming algorithm; 4, compensating a torque error and motor response delay by adopting sliding mode control and a feedforward-feedback mechanism; and 5, the control parameters are dynamically corrected based on errors of the longitudinal acceleration and the yaw velocity, and torque limiting or safety mode switching is triggered. Through multi-strategy collaborative optimization, the stability and driving efficiency of the vehicle under different attachment road surfaces are improved, the tire slipping risk is reduced, and driving safety under extreme working conditions is guaranteed.

The invention belongs to the technical field of unmanned aerial vehicles and flight control thereof, and discloses a variable-pitch multi-rotor unmanned aerial vehicle and a flight attitude control method thereof. Wherein the variable-pitch multi-rotor unmanned aerial vehicle is provided with an active-disturbance-rejection attitude control system, the active-disturbance-rejection attitude control system is provided with a rolling control loop, a pitching control loop and a yawing control loop, and each control loop comprises an extended state observer, a differential tracker and a nonlinear state error feedback. And when the control parameters are optimized, the control parameters are optimized by using an improved cumberfish optimization algorithm, and a dynamic updating strategy of the improved cumberfish optimization algorithm adopts a cumberfish position updating rule based on a variable spiral sine-cosine strategy. According to the technical scheme, the technical problem that control parameters of the unmanned aerial vehicle are difficult to adjust due to factors such as model uncertainty, external interference and nonlinear coupling characteristics caused by variable-pitch rotors in a complex dynamic environment is solved, and the flight performance of the unmanned aerial vehicle can be improved.

The invention belongs to the technical field of electronic information, and discloses an unmanned aerial vehicle control method for non-cooperative target tracking, which comprises the following steps of: 1, acquiring a real-time video stream of a target by a pan-tilt camera, and extracting a target feature vector; step 2, defining and calculating a normalized visual error vector, and adjusting the attitude of the holder based on the target feature vector obtained in the step 1, so that the target is kept in the center of the image; outputting a pan-tilt yaw angle, a pan-tilt pitch angle and a normalized visual error vector; 3, designing a tracking quality factor and a pitch angle scale factor according to a result output in the step 2, and dynamically adjusting the maximum speed of the unmanned aerial vehicle to calculate an expected speed vector; and obtaining the speed and position of the unmanned aerial vehicle at the next moment. According to the invention, decoupling control is carried out on the motion of the unmanned aerial vehicle and the rotation of the holder, and the motion trajectory of the unmanned aerial vehicle is controlled on the premise of ensuring that the target is always in the view field range of the unmanned aerial vehicle, thereby realizing accurate and stable tracking of the non-cooperative target.

A vehicle, a vehicle stability control method and apparatus, and a medium. The method comprises: establishing a lateral stability linear model of a vehicle, wherein in the lateral stability linear model, an additional yaw moment is used as a control quantity, both a mass-center sideslip angle and a yaw rate are used as state quantities correlated with a tire lateral force, and the tire lateral force is linearly correlated with a tire sideslip angle within a preset sideslip angle range; using, as a first optimization objective, the objective of the additional yaw moment meeting a target mass-center sideslip angle and a target yaw rate, and constructing a constraint condition of the first optimization objective on the basis of an additional torque capability range of each motor of the vehicle; and using, as a second optimization objective, the objective of enabling the output energy of each motor to be minimum, and decomposing the additional yaw moment into the additional torque of each motor on the basis of the second optimization objective and the first optimization objective having the constraint condition. The vehicle stability control can be realized with the minimum motor energy output, thereby improving the energy-saving effect.

The invention provides a quad-rotor unmanned aerial vehicle trajectory tracking control method and device, equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: determining a translation system state variable and an attitude system state variable of an unmanned aerial vehicle based on the real-time trajectory data of the quad-rotor unmanned aerial vehicle; calculating a translation disturbance estimated value based on the translation system state variable, the disturbance observer constant and the expected speed; thrust data is determined based on the translation system state variable, the translation disturbance estimated value, the expected speed, the expected position and outer ring controller parameters of the unmanned aerial vehicle; determining a desired attitude angle based on the thrust data and the desired yaw angle; calculating a control moment based on the expected attitude angle, the attitude system state variable and an inner ring controller parameter of the unmanned aerial vehicle; and based on the thrust data, the control torque and the physical parameters of the unmanned aerial vehicle, calculating to obtain a rotor expected rotating speed, and controlling the unmanned aerial vehicle based on the rotor expected rotating speed. The accuracy of flight control of the unmanned aerial vehicle can be improved.

The utility model relates to a cross-medium aircraft convenient for adjusting flight attitude, which comprises a water-drop-shaped centrosymmetric aircraft body with an aircraft head and an aircraft body, and the aircraft head of the aircraft body is positioned on the front side of the aircraft body. The aircraft body is further provided with an underwater power mechanism used for providing underwater flight power and an air power mechanism used for providing air flight power, the side portion of the aircraft body is connected with four or six fixed wings, slip-flow rudders controlled to rotate are arranged on the rear edges of the fixed wings, and the rotating axes of the slip-flow rudders are parallel to the wing faces of the fixed wings and perpendicular to the long axis of the aircraft body. Each fixed wing is provided with a slipstream rudder, so that the cross-medium aircraft can be conveniently controlled to pitch, roll, yaw and other actions, and the cross-medium aircraft can be conveniently switched between a hovering flight attitude and a level flight attitude.

The invention relates to a vehicle trajectory tracking control method considering dynamic motion requirements. Comprising the following steps: 1, establishing a nonlinear vehicle dynamics model by comprehensively considering longitudinal motion, lateral motion and yawing motion of a vehicle; 2, searching saddle node bifurcation points with qualitative change of the stability state of the vehicle through a particle swarm optimization algorithm and a Lyapunov criterion, and constructing a dynamic stability domain boundary of the vehicle under the change of the longitudinal vehicle speed and the road adhesion coefficient; and 3, designing a vehicle multi-target trajectory tracking controller based on a nonlinear model predictive control theory, quantitatively evaluating the stability state of the vehicle by adopting a stability index, and meanwhile, designing a controller built-in parameter adjustment strategy to match the dynamic motion requirements of the distributed driving electric vehicle in different stability states. According to the method, the driving safety and the movement comfort of the distributed driving electric automobile are effectively ensured, and a set of solution is provided for realizing the optimal cooperative control of the chassis subsystem of the automatic driving automobile.

The invention discloses a vehicle stability control method, system and device, a medium and a program product, and relates to the technical field of vehicle dynamic control, and the method comprises the steps: calculating a vehicle speed, a pavement friction coefficient, a slip rate, a load transfer rate and a preview path curvature; the vehicle speed, the road friction coefficient, the load transfer rate and the preview path curvature serve as input, the yaw velocity deviation and the slip rate serve as reward signals, and a yaw velocity dynamic threshold value is generated; according to the actual yaw velocity, the expected yaw velocity and the yaw velocity dynamic threshold, obtaining a judgment result that the current vehicle is under-steering or over-steering, and generating a control strategy in combination with a vehicle driving form; and the optimal braking torque adjustment amount is calculated with the minimum yaw velocity deviation and the slip rate as the optimization target, and the front and rear shaft driving torque is obtained according to the deviation between the actual side slip angle and the expected side slip angle. The self-adaptive dynamic environment and the differentiated adaptive driving form are realized, and the safety and the controllability under the complex working condition are improved.

The invention is suitable for the technical field of automatic driving vehicle control, and provides a full-state control method for dynamic drifting of a distributed driving vehicle, which comprises the following steps of: tracking a reference path to obtain an expected state quantity; establishing a dynamic error of a full state to obtain expected state derivatives of the velocity, the side slip angle and the yaw velocity; calculating a rear tire force weight, and implementing rear tire force constraint of the distributed driving vehicle; inverting the vehicle dynamics model to obtain expected longitudinal force and lateral force of each wheel; performing tire model inversion to obtain an expected front wheel slip angle and wheel speed of each wheel; and according to the expected front wheel slip angle and the wheel speed of each wheel, an actuator is adjusted, and control instructions of the steering wheel angle and the torque of each wheel are generated. The state of the vehicle can be accurately controlled, the maneuverability of the vehicle is good during conventional turning and limit drifting, and the vehicle can flexibly cope with complex working conditions; the stability and safety of the vehicle are enhanced, and the full-state dynamic drift control of the distributed driving vehicle is realized.

The invention discloses a multi-signal fusion yaw velocity credibility calculation method and electronic equipment. The method includes calculating a first yaw velocity based on a vehicle model; calculating a second yaw velocity based on the lateral acceleration; calculating a third yaw velocity based on the geometric constraint of the wheel speed difference; determining whether a credibility updating condition is satisfied; and if yes, determining the credibility of the actually measured yaw velocity of the sensor according to the first yaw velocity, the second yaw velocity, the third yaw velocity and the actually measured yaw velocity of the sensor. Multi-dimensional signals such as the wheel speed, the steering angle and the acceleration are fused, and fault tolerance is improved. And through threshold screening and weighted fusion, noise and sudden change interference are suppressed. And front wheel calculation can be switched when a rear wheel breaks down, so that the system continuity is guaranteed.

The invention relates to the technical field of vehicle engineering, and discloses a vehicle transverse stability coordination control system based on sensing fusion, which comprises a main controller, a sensing fusion coprocessor and a transverse control coprocessor. The main controller calls and sends a stability control instruction according to the driving state; the sensing fusion coprocessor schedules a transverse control instruction in multi-source information fusion; the transverse control coprocessor analyzes the parameters to obtain basic control signals, dynamically monitors transverse acceleration, yaw velocity and tire slip angles, generates and outputs compensation signals through mode adjustment such as dynamic coordination function bypass selection and global or local parameter dynamic coordination, and controls an actuator after being fused with the basic signals. The system integrates a data fusion module, a state evaluation module and a multi-strategy dynamic adjustment module, multi-source information accurate fusion and multi-parameter cooperative control are achieved, the transverse stability and driving safety of the vehicle under the complex working condition are improved, and the method is suitable for the field of vehicle engineering control.

The invention relates to the technical field of unmanned aerial vehicle vision, and discloses a vision system of an unmanned aerial vehicle and the unmanned aerial vehicle. The system comprises a visual data acquisition module, a dynamic feature extraction module, an environment modeling module and a decision control module. The visual data acquisition module captures a synchronous frame sequence containing infrared wave bands, visible light wave bands and depth information in a target area through a multispectral sensor array; the dynamic feature extraction module performs cross-modal fusion processing on the original visual data stream to generate a space-time correlation feature tensor containing a target contour geometric invariance descriptor and a motion trail differential topological structure; the environment modeling module constructs a three-dimensional semantic grid map according to the feature tensor, wherein each voxel unit codes the material reflectivity, the dynamic obstacle occurrence frequency and the illumination attenuation coefficient; the decision control module generates a flight path control instruction containing a pitch angle adjustment amount, a yaw angle compensation value and a speed change gradient based on the map, and assists the unmanned aerial vehicle to better cope with a complex environment.

The invention provides a vehicle control method and system, a vehicle, a storage medium and a program product, and relates to the technical field of vehicles. According to the vehicle control method, vehicle state information and the current yaw velocity of a target vehicle are obtained, the predicted yaw velocity and the target yaw velocity are determined according to the vehicle state information, the feed-forward yaw moment and the feedback yaw moment are further determined according to the current yaw velocity, the predicted yaw velocity and the target yaw velocity, and the feedback yaw moment is determined according to the feedback yaw moment and the feed-forward yaw moment. And then the target vehicle is controlled according to the feedforward yaw moment and the feedback yaw moment. According to the vehicle control method and device, the vehicle is controlled by combining the feedforward yaw moment and the feedback yaw moment, feedforward control and feedback control are combined, the vehicle control precision is improved, and then the vehicle control stability is improved.

The invention discloses an LSTM (Long Short Term Memory)-based fan blade unmanned aerial vehicle routing inspection path optimization method. The method comprises the following steps: 1) constructing a map model of a fan blade routing inspection area by adopting a grid method; 2) measuring yaw data of the fan in real time; 3) dynamically updating the map model; 4) taking the real-time yaw angle and the yaw speed of the fan and the historical position of the fan blade as the input of the LSTM model, and predicting the trajectory of the fan blade in the future n time steps through the LSTM; and 5) dynamically planning the inspection path of the unmanned aerial vehicle according to a D * algorithm. Compared with a traditional static path planning method, the method has the advantages that higher inspection precision and higher dynamic adaptability can be achieved, dynamic path optimization is achieved, unnecessary repeated flight is avoided, energy consumption is greatly reduced, and meanwhile the coverage rate and efficiency of inspection are improved.

The invention discloses a commercial vehicle driving control method considering a load non-uniform distribution characteristic. The method comprises the following steps: acquiring and preprocessing sensor data of a commercial vehicle; estimating yaw disturbance torque caused by uneven load distribution in real time; a robust model prediction controller based on yaw disturbance torque compensation is constructed, and multi-axis coordinated yaw torque meeting the driving stability requirement under the non-uniform load working condition is output; driving force optimization distribution is carried out, and target driving force of each electric wheel is determined; and designing a driving force synchronous tracking controller based on the tire relaxation characteristic model, and outputting the torque of each electric wheel to complete the driving stability control of the commercial vehicle. According to the method, the yaw disturbance torque observer and robust model predictive control are fused, and the disturbance yaw torque is estimated in real time and compensated in a feedforward mode, so that pertinence and fineness are better in the aspect of disturbance suppression; the auxiliary sliding mode control can further improve the robustness of the system in an unknown or rapidly changing external environment.

The invention discloses a motor torque control method applied to a bilateral independent electrically-driven tracked vehicle. The method comprises the following steps: determining an instantaneous center position of a tracked vehicle during steering based on a kinematic model of the tracked vehicle independently driven by motors on two sides; determining a tracking position by searching a target point on a path, and calculating a current transverse deviation and a current course deviation in combination with the current position and course information of the tracked vehicle; the transverse deviation and the course deviation obtained through calculation serve as input, the yaw velocity of the vehicle during steering serves as output, and then the yaw velocity of the vehicle is obtained; then, the linear speeds of the two sides of the tracked vehicle are obtained through the established kinematics model of the tracked vehicle, and then the expected rotating speeds of motors on the two sides during steering are calculated; and finally, optimizing by adopting a fractional order PI lambda D mu controller and an improved particle swarm optimization algorithm, and calculating the optimal output torques of the motors on the left side and the right side of the tracked vehicle.

A VTOL UAV comprising a chassis, a propulsion system mounted on the chassis, electronics including a flight control system, and a 3D LIDAR sensor mounted on the chassis. The 3D LIDAR sensor has a laser beam 360° rotation axis (R) and an azimuthal scanning angle (α) in a range of 70° to 110°. The propulsion system comprises a plurality of propellers with motors configured for flight without wings, including static hovering flight, the VTOL UAV comprising a yaw axis (Z) about which it can rotate upon itself. The 3D LIDAR sensor is statically mounted on the chassis such that the LIDAR laser beam 360° rotation axis (R) is inclined at a LIDAR inclination angle (β) with respect to the yaw axis (Z) in a range of 35° to 50°.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle accurate attitude control method fusing multiple sensors and a flight controller thereof. The method comprises the following steps: acquiring the position and attribute characteristic data of an unmanned aerial vehicle through multiple sensors; determining the offset degree of the statistical period and the offset direction of the unmanned aerial vehicle according to the position difference between the unmanned aerial vehicle position and the planned route in the previous statistical period, the unmanned aerial vehicle direction and the planned route angle; attitude angles are obtained through attitude calculation, the attitude angles form a fluctuation curve, the fluctuation degree is analyzed, and the attitude stability degree of the unmanned aerial vehicle is obtained; the deviation of the unmanned aerial vehicle relative to the initial position is obtained according to the fluctuation curve slope of the yaw angle; comparing the direction with the offset direction to determine a correction angle; and calculating a three-axis error signal through the correction angle, and inputting the three-axis error signal into a PID (Proportion Integration Differentiation) controller for control. A more accurate attitude angle is obtained, so that the control precision is improved.

The invention relates to a robust cooperative control method for yaw stability of a distributed angle module electrically-driven vehicle. The method comprises the following steps: acquiring driving state data of a corner module vehicle; according to the transverse stability constraint condition and the transverse dynamical model, a nominal control model is constructed, the nominal control model is a part related to the determined parameters in the transverse dynamical model, and the target of the transverse stability constraint condition is that the error of the yaw velocity and the error of the lateral velocity tend to zero; constructing an uncertainty control model, wherein the uncertainty control model is a part related to the uncertain parameters in the transverse dynamic model; driving state data are input into the nominal control model and the uncertainty control model, and yaw moment control parameters are determined; and based on the yaw moment control parameter and the rear wheel steering control parameter, performing stability cooperative control on the angle module vehicle. By adopting the method, the yaw stability of the angle module vehicle can be improved.

The invention belongs to the field of vehicle stability control, and particularly relates to a control method suitable for integration of four-wheel independent drive and active rear wheel steering. Comprising the steps of establishing a whole vehicle dynamics model suitable for controller design, performing stable region division by combining front and rear wheel side slip angle phase planes, setting an ideal yaw velocity and an ideal side slip angle, designing a feedforward and PID feedback integrated controller, performing torque distribution and the like. According to steering wheel rotation angle input, corresponding parameters of the vehicle are combined, the vehicle stability coefficient, the ideal yaw velocity and the side slip angle are calculated, the feedforward and PID feedback integrated controller inputs the calculated rear wheel rotation angle into the controlled vehicle, the calculated additional yaw moment obtains the basic driving moment based on pedal information, and the vehicle stability coefficient, the ideal yaw velocity and the side slip angle are calculated. Torques corresponding to four wheels are input to a controlled vehicle through torque distribution, so that the purpose of integrated control is achieved, and the vehicle handling stability is improved.

The invention relates to a multi-mode interactive indoor unmanned aerial vehicle target detection and tracking method and system. The system comprises a target detection and tracking module, a laser radar inertial odometer module, a video display and interaction module and a flight control module. The method comprises the steps that an unmanned aerial vehicle end receives and processes RGB images, laser radar point cloud and IMU data; and optimizing a YOLOv8 target detection model and an OSTrack target tracking model through a TensorRT inference engine. By fusing LiDAR and IMU data, accurate state estimation and map construction are provided. And the ground station end displays a video stream and generates a control command through the video display and interaction module. And calculating a flight speed and a yaw angle rate by estimating an error between the target image position and the current position of the unmanned aerial vehicle, and generating a flight control instruction. The problems that an indoor unmanned aerial vehicle system often faces low target detection precision, poor tracking stability and difficult user interaction experience are solved, and the method and the system are more efficient, more accurate and higher in interactivity.