2104 results about "Yaw" patented technology

The invention relates to a multi-machine cooperative yaw control method and device considering a dynamic wind direction and a medium. The multi-machine cooperative yaw control method comprises the steps that S1, the dynamic relation between the wind direction and a wake flow area is established based on a Gaussian-curl hybrid model; s2, constructing a single-machine yaw optimization model based on model predictive control, optimizing single-machine yaw by taking maximum generating capacity of a wind power plant and reduction of fatigue load as optimization objectives, and obtaining a single-machine optimal yaw angle through rolling optimization and feedback correction; and S3, a serial optimization method is adopted to carry out multi-round iterative optimization on one fan by one fan, and an optimal multi-fan cooperative yaw control result is obtained. Compared with the prior art, the method has the advantages that higher total generating capacity and more balanced fatigue load can be realized, so that the whole wind power plant reaches the optimal state of power and load in a large range.

The invention discloses an unmanned aerial vehicle flight control method and system based on a target image, and relates to the field of unmanned aerial vehicle flight control, and the method comprises the steps: obtaining the relative position of an unmanned aerial vehicle in a flight region based on the target image and a position calculation algorithm, and judging the yaw state of the unmanned aerial vehicle through a position comparison method; analyzing the target image by using an identification processing technology based on yaw state judgment to determine a flight yaw angle of the unmanned aerial vehicle, and generating a yaw control quantity according to the flight yaw angle to control the flight state of the unmanned aerial vehicle; collecting a real-time target image of the unmanned aerial vehicle after the flight state is adjusted, analyzing the flight height of the unmanned aerial vehicle according to the real-time target image, and planning a landing track of the unmanned aerial vehicle based on the flight height; and generating an autonomous landing control instruction according to the unmanned aerial vehicle landing trajectory and a preset flight demand to complete autonomous landing flight control of the unmanned aerial vehicle. By collecting the target image in real time and analyzing the flight height, the landing track of the unmanned aerial vehicle can be accurately planned.

The invention belongs to the technical field of wind turbine yaw control, and relates to a wind turbine yaw cooperative control method and system based on a yaw wake flow model. The method comprises the following steps: selecting three wind turbines as target units; calculating a power loss rate to obtain a wake flow loss evaluation index; determining the wind turbine influenced by the wake flow according to the wake flow loss evaluation index; establishing a target function taking the total output power of the wind turbines as an optimization target, establishing a function relational expression between the yaw angle and the incoming flow wind speed of the downstream fan, and determining a relational expression between the incoming flow wind speed and the output power by utilizing a static power curve of the adjacent wind turbines; and the incoming flow wind speed is set, and the yaw angle of the wind turbine under the maximum output power is determined. On the basis of a three-dimensional yaw wake flow model of the wind turbine, a target function with the total output power of the three wind turbines as an optimization target is established, an optimization strategy of the three wind turbines is obtained through yaw power calculation, and active yaw cooperative control over the wind turbine units at different wind speeds is achieved.

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.

The invention relates to the technical field of aircrafts, and discloses an aircraft attitude control method, device and equipment and a storage medium, and the method comprises the steps: collecting flight state parameters of an aircraft; the pitch angle data in the flight state parameters are compared with a preset angle threshold value, corresponding flight mode switching is executed, and the attitude deviation value is calculated; power distribution calculation is conducted on the three ducts, and the target rotating speed of each duct paddle is obtained; performing position optimization calculation on the guide plates below the duct to obtain the position control quantity of each guide plate; the three flow guide plates are configured as triangular control points, the position compensation amount of an aileron, a yaw rudder and an elevator is calculated and determined through vector synthesis, and the angle control amount of each control surface is obtained; according to the method, the attitude of the aircraft is converted into an execution signal, execution feedback data is acquired to correct a control instruction in real time, closed-loop attitude control of the aircraft is realized, the attitude control precision of the aircraft is improved, and the stability of the flight process is ensured.

The invention discloses a fan operation real-time dynamic adjustment control method and system based on environmental data. The method comprises the steps that multi-dimensional dynamic parameters of the environment where a fan is located are collected in real time based on a distributed sensor network; constructing a dynamic coupling prediction model, inputting the dynamic coupling prediction model into a bidirectional verification module based on multi-dimensional dynamic parameters, and obtaining an environment state prediction matrix in a future time window; based on the environment state prediction matrix, a combined adjustment instruction set of the fan pitch angle, the yaw angle and the generator torque is obtained, and the instruction set comprises main strategy parameters and an emergency tolerance interval; based on an incremental learning mechanism, model parameters are dynamically updated according to the deviation between actual environment parameters and predicted values, and the updating period is adaptively adjusted along with the change of turbulence intensity. The method has the advantages that accurate prediction and optimal control of the fan in a complex environment are achieved, the limitation that a traditional data driving model lacks physical interpretability is broken through, and the running stability and efficiency of the fan are improved.

A dynamic safety filtering control method and domain control architecture for extreme driving functions. The method comprises: S10, obtaining environment state information and function state information; S20, determining whether a vehicle is approaching a real-time physical constraint boundary; if yes, entering step S50; and if not, entering step S30; S30, determining whether the vehicle has exceeded a dynamic safety boundary; if yes, entering step S50; and if not, entering step S40; S40, completely trusting in an extreme driving control output; S50, calculating a target yaw angle and a target vehicle speed of the vehicle; S60, in light of a real-time physical constraint boundary finite time, intervening in the target yaw angle and / or the target vehicle speed, wherein intervening in the target yaw angle ensures that the vehicle is at a safe distance from the physical constraint boundary, and intervening in the vehicle speed ensures the dynamic stability of the vehicle; and S70, according to the target vehicle speed and the target yaw angle, calculating a safety control quantity.

The invention discloses a large-scale wind power plant yaw control method and system based on FLORIS, belongs to the technical field of wind power generation, and solves the problems that a wind power plant yaw optimization scheme is low in accuracy, wind power plant related data processing is large in data size and long in time, and a multi-wind-direction and multi-wind-speed yaw scheme adapts to the wind power plant yaw optimization scheme. The method comprises the steps that wind power plant real-time data and wind turbine real-time data are collected; the wake flow loss model, the wake flow deflection model, the wake flow turbulence model and the wake flow combination model are improved, the wind power plant flow field and the output power attenuation influenced by the wake flow are calculated, and the wind turbines influenced by the wake flow are screened out to serve as target wind turbines; main upstream wind turbines are determined according to the flow field parameters of the target wind turbine; establishing a rectangular coordinate system; the wind turbines are arranged in the rectangular coordinate system, the yaw angles of all the wind turbines are adjusted, the optimal yaw angle is obtained, the target wind turbine is controlled according to the optimal yaw angle, and the method is completed. The method is suitable for various complex wind fields and fits the actual operation condition of the wind power plant.

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 wind power plant yaw control optimization method based on a sequential optimization algorithm, and the method comprises the steps: obtaining the coordinates of all fans at a current inflow wind direction angle, queuing all fans after the coordinates are converted in sequence, and calculating the yaw angle range of each cycle according to the actual demands; (n-1) times of outer layer circulation are set for the first (n-1) fans in the preliminary optimization process, in each outer layer circulation, yaw angle traversal value taking circulation is conducted on a certain fan according to the obtained yaw range and the uniform step length, and then the optimal yaw combination and the corresponding power of the wind power plant are obtained; and then optimization is carried out to obtain the optimal yaw angle of each fan in the whole field. According to the method, the wake flow secondary deflection effect in the wind power plant and the influence effect of the inflow turbulence intensity on the wake flow are considered, the improved Gaussian wake flow model is adopted to comprehensively consider calculation of wake flow loss in the three-dimensional direction, the calculation result is more accurate, and the final control optimization effect can also conform to the actual engineering situation.

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 embodiment of the invention provides an unmanned aerial vehicle flight path detection method and equipment, and the method comprises the steps: firstly determining a flight plan of an unmanned aerial vehicle, then generating a flight path line and a first space-time envelope corresponding to the flight plan according to the flight plan, monitoring the flight attitude data of the unmanned aerial vehicle after the unmanned aerial vehicle takes off, and carrying out the detection of the flight path of the unmanned aerial vehicle. A second space-time envelope body can be obtained according to a first time point, a first distance between space position information of the unmanned aerial vehicle at the first time point and a flight track point in the second space-time envelope body is calculated, and whether the unmanned aerial vehicle deviates from the first space-time envelope body or not is judged according to the first distance. According to the scheme, by introducing the flight plan, the flight track line and the space-time envelope of flight, the flight path of the unmanned aerial vehicle in specific time is predicted, the full view of the flight process in the whole flight plan is mastered, meanwhile, the distance relation between the real-time position of the unmanned aerial vehicle and the corresponding space-time envelope is calculated, and the possible yaw risk of the unmanned aerial vehicle is accurately recognized.

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 discloses a tire burst control method and device and a vehicle, and the method comprises the steps: obtaining the state information of the vehicle when a tire burst signal is received; the state information comprises an actual vehicle speed and an actual yaw velocity; according to the actual vehicle speed, actual basic correction torque is calculated; calculating an actual feedback correction torque according to the actual yaw velocity; and according to the actual basic correction torque and the actual feedback correction torque, performing weighted calculation on a target correction torque, and according to the target correction torque, controlling the vehicle. The phenomenon that deviation of the vehicle cannot be completely compensated or compensation is excessive can be avoided, so that deviation of the vehicle after tire burst is prevented, and the stability of the vehicle after tire burst is guaranteed.

The invention discloses a heavy commercial vehicle yaw control system with delay compensation and slip suppression. A state sensing unit collects signals and sends data to a yaw control unit and a braking force control unit. The yaw control unit acquires a desired yaw moment and a braking moment. A yaw velocity tracking module calculates a desired yaw velocity. And the wheel slip suppression module obtains a brake torque for avoiding slip through control. The braking force control unit addresses braking force distribution under yaw moment control and wheel slip suppression, and compensates for the delay of the pneumatic brake system. And the braking force cooperative distribution module calculates the expected braking chamber pressure of each wheel according to the required yawing moment and the braking moment. The braking force delay compensation module calculates and predicts the pressure of a braking chamber through a prediction model, and obtains control input of a braking actuator through a PID controller so as to compensate delay. According to the system, the driving safety of the heavy commercial vehicle under the complex road condition is improved through cooperative control.

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 system for reverting an actual heading of a mobile construction equipment subject to load from one or more work implements to a desired heading, the mobile construction equipment having a front frame articulatable with respect to a rear frame, the front frame including a steering apparatus, the system including: a rear inertial motion unit disposed on the rear frame, the rear inertial motion unit for providing an actual rear frame yaw rate; a steering sensor for providing an actual steering angle of the steering apparatus; an articulation sensor for providing an actual articulation angle between the front frame and the rear frame; and a controller for: receiving the actual rear frame yaw rate, the actual steering angle, and the actual articulation angle to determine the actual heading, receiving a steering control indicating a desired steering angle of the steering apparatus and an articulation control indicating a desired articulation angle.

The invention discloses an improved grey wolf optimization algorithm-based solution for multi-unmanned aerial vehicle cooperative flight path planning, and belongs to the field of unmanned aerial vehicle flight path planning. According to the method, a multi-aircraft cooperative flight path planning model is established in a three-dimensional space, and the provided algorithm not only needs to consider self-factor limitations such as a pitch angle, a yaw angle, a navigation height and a navigation distance of an aircraft course, but also needs to consider areas where radars, artillery fire coverage areas and meteorological equipment in avoidance space are located, and external factors such as inter-aircraft cooperation. An original grey wolf optimization algorithm is improved by using a method of combining global search and local search, a fitness function loss value is quickly reduced by adopting multiple strategies, and a local search range can be expanded to a certain extent. The improved grey wolf optimization algorithm adopted by the invention is relatively high in parameter adjustment effectiveness and high in convergence speed, and can plan a track with relatively small loss according to different types of three-dimensional space constraints, so that a collaborative track optimization scheme of the unmanned aerial vehicle group is given.

The invention discloses an automobile stability coordination control method based on reinforcement learning, and belongs to the technical field of electric automobile control, the method comprises the following steps: using a linear two-degree-of-freedom model to obtain an expected yaw velocity and an expected side slip angle; additional yawing moment and a steering angle of a driving rear wheel are obtained through sliding mode control; acquiring the state type of the current vehicle; constructing an intelligent agent and a reward function of reinforcement learning, and continuously training the intelligent agent until the optimal cooperative control coefficients of the additional yaw moment and the steering angle of the active rear wheel are output, so as to realize the cooperative control of the electric vehicle; the reward function includes a steady state reward, a yaw velocity error and side slip angle error reward, a critical steady state reward, and an anti-jitter reward. According to the invention, the steering and yawing moment of the driving rear wheel can be coordinated and controlled, so that the stability and safety of the vehicle are ensured.

An aircraft has a boom, a propulsion assembly coupled to a first end of the boom, and a first wing coupled to a second end of the boom. The propulsion assembly is coupled to the boom by a rotating joint. A second wing is optionally coupled to the rotating joint. The first wing is coupled to the boom by a rotating joint. The first wing is coupled to the rotating joint by a hinge. A vehicle with roll, pitch, and yaw maneuverability able to mirror the aircraft movements may be coupled to the second end of the boom. The vehicle body may be picked up with a vehicle chassis disconnected from the vehicle body. The boom houses an energy source to power the propulsion assembly. A rudder is coupled to the second end of the boom. A paddle is disposed between the propulsion assembly and the boom.

The invention provides a multi-degree-of-freedom coupling execution mechanism for deflection of a water tunnel test model. The multi-degree-of-freedom coupling execution mechanism comprises a shell, an attack angle motion module, a yaw motion module and a rolling motion module, wherein the attack angle motion module, the yaw motion module and the rolling motion module are arranged in the shell; according to the mechanism, in the aspect of attack angle motion, a pitching motor drives a pitching lead screw through a mechanical transmission assembly, and control over the attack angle of a model balance component can be achieved by combining a translation sliding block and a pitching sliding block which slide in a matched mode in the height direction. The yaw movement module drives a yaw lead screw through a yaw motor, a pitching arc-shaped guide rail is stably pushed to move along the yaw arc-shaped guide rail through a yaw sliding block, and accurate adjustment of the yaw angle is achieved. The rolling motion module effectively realizes the rolling action of the model balance component through a rolling motor; the pitching, yawing and rolling motion modules can be independently controlled and can also be controlled in a coupling mode, the unique design greatly improves the flexibility and adaptability of mechanism motion control, and various complex working condition requirements can be accurately met.

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.