983 results about "Yaw" patented technology

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 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 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 wind power plant active wake flow control method and device based on machine-field cooperation. The method is characterized in that a field control layer and a machine control layer are cooperatively operated; calculating the real-time wind speed of each fan in the wind power plant based on a Gaussian rotation-energy conservation wake flow superposition model describing the asymmetric characteristics of the wake flow and the multi-wake flow interactive superposition of the whole wind power plant; in the field control layer control, the optimal yaw angle under different wind condition combinations is obtained by taking the maximization of the total output power of the wind power plant as a target; in the control of the machine control layer, yaw angle tracking errors and the fatigue load of a yaw bearing are considered, model prediction control is applied within a continuous time range, and the yaw angle of each fan is dynamically adjusted according to the optimal yaw angle provided by the field control layer. Compared with the prior art, the method has the advantages that the generating capacity is remarkably improved and the fatigue load is reduced in a randomly fluctuating wind field environment, and the method has remarkable applicability in a large offshore wind plant.

The invention relates to a near-earth spacecraft strong transverse maneuvering reentry trajectory planning method. The method comprises the following steps: determining basic parameters of return reentry trajectory planning; calculating a reentry trajectory of the maneuver without transverse offset; distributing a transverse maneuver amount, and establishing a transverse maneuver planning equation; calculating a braking yaw angle; calculating an initial value of the heeling angle offset; establishing a funnel guidance law under the condition of transverse offset maneuvering; according to the determined return and reentry trajectory planning basic parameters, the total transverse offset maneuver amount, the braking yaw angle and the funnel guidance law under the transverse offset maneuver condition, the initial value of the heeling angle offset amount, the time of the braking point relative to the return circle ascending node and the braking starting duration serve as initial values, reentry trajectory parameters are iteratively solved, and reentry trajectory planning is completed. According to the method, the problem of rapid planning of the reentry trajectory when the ground proximity device returns to the designated position under the condition of large initial transverse deviation is solved, and meanwhile, the problem of rapid planning of the reentry trajectory when the landing point needs to avoid the designated area can be solved.

A vehicle and a control method and apparatus therefor, a storage medium, and a computer program product, relating to the technical field of vehicles. The control method for a vehicle comprises: correcting an ideal yaw rate on the basis of desired steering characteristic information; obtaining an additional yaw moment on the basis of an ideal sideslip angle, the corrected ideal yaw rate, and an actual sideslip angle and an actual yaw rate of a vehicle; and performing torque distribution on the basis of the additional yaw moment. In the method, the ideal yaw rate is corrected on the basis of the desired steering characteristic information, so that, on the basis of the ideal sideslip angle, the corrected ideal yaw rate, and the actual sideslip angle and the actual yaw rate of the vehicle, variable steering characteristics of the vehicle are achieved by means of torque control, thereby enabling the vehicle to obtain higher steering sensitivity.

The invention relates to the technical field of autonomous navigation of unmanned cleaning trolleys, in particular to a robot autonomous navigation coordination control method and system based on an ST-TD3 strategy. The training action decision model generates actions based on the current state of the unmanned cleaning trolley, and the unmanned cleaning trolley executes the actions to adjust the speed and the yaw angle; the state comprises an environment depth characteristic and an obstacle density characteristic of an environment where the unmanned cleaning trolley is located, and a relative distance and a relative azimuth angle between the unmanned cleaning trolley and the target point; environment depth features and obstacle density features are extracted based on depth image features acquired by an unmanned cleaning trolley; the actions comprise linear velocity and angular velocity, the linear velocity and the angular velocity respectively meet the linear velocity maximum value constraint and the angular velocity maximum value constraint of the unmanned cleaning trolley, and the angular velocity represents the deflection direction through positive and negative values. The unmanned cleaning trolley can efficiently, safely and stably run in a complex cabin environment.

The invention provides a semi-trailer rollover early warning method and system based on dynamic threshold value prediction. The early warning method comprises the steps that the transverse acceleration, the yaw velocity, the vehicle speed, the suspension pressure and the front lane curvature of a semi-trailer are obtained in real time; according to the collected and calculated data and a linear trend extrapolation method, constructing a dynamic rollover risk threshold function and a comprehensive risk index function, and calculating a rollover risk threshold, a real-time risk value and a predicted risk value; the item with the higher value in the real-time risk value and the predicted risk value is selected and compared with a rollover risk threshold value, and whether early warning is triggered or not is judged, and a control instruction is issued; the early warning system comprises a semitrailer state monitoring unit, a lane curvature recognition unit, a rollover risk threshold calculation unit, a real-time risk value calculation unit, a predicted risk value calculation unit and an early warning control unit, and is used for realizing the early warning method, so that the response speed and the judgment accuracy of the semitrailer rollover early warning system under variable working conditions are improved.

The invention discloses a method and device for correcting the attitude of a tower drum of a floating type wind generating set, and the method comprises the steps: determining a variable pitch angle increment according to multi-source feature data and current control parameters when the attitude of the tower drum is judged to be abnormal based on the multi-source feature data of a target tower drum, and determining a yaw angle increment according to the multi-source feature data; according to the variable pitch angle increment and the yaw angle increment, performing attitude correction on the target tower drum, wherein the attitude correction is performed under the constraint of a safety boundary; when it is judged that the posture of the tower drum is still abnormal after correction, current control parameters are optimized based on the new multi-source feature data; and based on the optimized control parameters and the new multi-source characteristic data, determining a new variable pitch angle increment and carrying out attitude correction again until the attitude of the tower drum returns to normal. According to the method, the tower attitude of the floating type wind generating set can be corrected timely and accurately, the requirement for stable operation of the floating type wind generating set under the complex sea condition is met, and potential safety hazards are avoided.

The invention provides a vehicle drifting control method, device and equipment, a computer readable storage medium and a computer program product. The method comprises the steps that based on target state information and actual state information of a vehicle, a first slip rate component and a second slip rate component are obtained, the first slip rate component is used for slip angle stability control, and the second slip rate component is used for yaw velocity control; determining a left rear wheel slip rate target and a right rear wheel slip rate target based on a basic slip rate target, the first slip rate component and the second slip rate component; and rotating speed calculation is conducted based on the actual state information, the left rear wheel slip rate target and the right rear wheel slip rate target, and the vehicle is controlled based on the obtained left rear wheel rotating speed target and the obtained right rear wheel rotating speed target. According to the invention, a driver can maintain stable drifting of the vehicle only by operating the steering wheel and the accelerator.

The invention discloses a vehicle transverse anti-interference control method, device and equipment and a storage medium, and the method comprises the steps: employing an extended Kalman filtering algorithm to process the collected yaw velocity, longitudinal velocity and transverse acceleration of a vehicle, so as to estimate a road side slope angle value; and calculating a feed-forward compensation amount based on the road side slope angle value so as to carry out feed-forward compensation on vehicle transverse control and actively counteract interference of transverse displacement. According to the method, the transverse control precision can be remarkably improved, the control robustness is enhanced, the method adapts to complex road conditions, and the safety, comfort and driving stability of the vehicle are improved.

The invention relates to the technical field of vehicle dynamic performance testing, and particularly discloses a method and a system for testing vehicle tracking recovery capability based on transverse impact. The method comprises the following steps: acquiring position information of a tested vehicle in real time in a tracking driving process of the tested vehicle on a test road; if the tested vehicle arrives at the impact triggering area, controlling a road simulation plate laid on the test road to move transversely so as to simulate an impact working condition; when a tested vehicle arrives at an impact triggering area, rear wheels of the tested vehicle are located on the road simulation board. In the transverse movement process of the road simulation board, the instantaneous yaw angle of the tested vehicle is obtained; and responding to the situation that the instantaneous yaw angle is larger than a first set threshold value for the first time, and testing the tracking recovery capability of the vehicle according to the driving performance of the tested vehicle under the control of the electronic stability system. According to the method, the technical bottlenecks of uncontrollable interference and non-quantitative data of a traditional control test are overcome, and repeatable test, controllable parameters and quantitative evaluation are realized.

A vehicle control method for improving a driving stability of a vehicle includes: obtaining road information and determining a friction coefficient of road surface according to the road information; obtaining first vehicle parameters and a first yaw rate of the vehicle, the first vehicle parameters including a turning angle of steering wheel, and a longitudinal speed, and a lateral speed; determining a second yaw rate of the vehicle according to the first vehicle parameters, the first yaw rate, and the friction coefficient of road surface; determining a yaw moment of the vehicle according to a difference value between the first yaw rate and the second yaw rate; and distributing the yaw moment to a four-wheel driver, the four-wheel driver including four independent drivers for driving wheels. A computing device and a non-transitory storage medium are also provided.

The invention discloses a wind field yaw wake flow hybrid modeling method and device fusing simulation and deep learning. The method comprises the following steps: firstly, introducing a yaw-corrected actuating disc model, constructing a volume force source item under the yaw action of each fan, performing steady-state computational fluid mechanics simulation on the yaw wake flow of the wind field by combining a Reynolds average Navier Stokes method and a k-epsilon turbulence model, and constructing a yaw wake flow database of the wind field; and then, training the yaw wake flow database by using a deep neural network, and learning a nonlinear mapping relationship between a wind field input wind speed and a multi-fan yaw angle combination and an incoming flow wind speed of each fan, so that a wind field yaw wake flow data driving model is established, and real-time prediction of wake flow characteristics under non-consistent yaw configuration is realized. Compared with an existing yaw wake flow model, the method has the advantages that the technical difficulty of wake flow modeling of multi-fan non-consistent yaw is effectively solved, the prediction speed is remarkably increased while the modeling precision is improved, and the method has high engineering application value.

The invention relates to the technical field of wind power generation. The invention provides an emergency yaw and automatic crosswind method and system for a wind turbine generator. The method comprises the following steps: monitoring the on-off state of a safety chain in real time, and activating an emergency power supply switching module when detecting that the safety chain is disconnected; synchronously acquiring unit vibration spectrum, impeller rotating speed, environment wind speed and cabin load distribution data, and generating dynamic risk assessment parameters based on a multi-source signal fusion algorithm; inputting a hierarchical control instruction to a yaw driver according to a comparison result of the risk assessment parameter and a preset threshold value; and in the automatic crosswind control stage, the yaw rate and the angle offset are dynamically adjusted based on the blade root bending moment real-time feedback and the wind speed change rate, so that the unit maintains the aerodynamic load balance in the non-windward state. The problems that after a safety chain of an existing wind turbine generator is disconnected, yaw capacity is completely lost, emergency crosswind protection or fault recovery cannot be achieved, and a traditional emergency yaw scheme is high in cost, complex in maintenance, slow in response, difficult to adapt to complex working conditions and the like are solved.

The invention relates to the technical field of aircraft control, in particular to a control-surface-free aircraft control system based on synthetic jet and a momentum wheel, which comprises a lift force and attitude control subsystem, a steering control subsystem, a propulsion subsystem, a cooperative control unit and an attitude and navigation sensor. The lift force and attitude control subsystem independently controls different areas to generate local lift force difference through a synthetic jet generator matrix integrated on the surface, and rolling and pitching control is achieved. And the steering control subsystem changes the angular momentum direction of a flywheel through a control moment gyroscope arranged in the steering control subsystem, and generates reaction moment to control yawing. The propulsion subsystem provides a primary forward thrust. The cooperative control unit receives a sensor signal, generates an instruction according to a preset strategy, coordinates the subsystems to work cooperatively, and achieves whole-airspace flight control without an exposed control surface.

A vehicle control method, a vehicle control apparatus (100), a vehicle control system (200), and a computer-readable storage medium (300). The vehicle control method in the embodiments of the present application comprises: acquiring the current yaw rate and the current sideslip angle; acquiring a target yaw rate and a target sideslip angle; and, on the basis of the current yaw rate, the current sideslip angle, the target yaw rate and the target sideslip angle, individually controlling a plurality of front wheels and / or a plurality of rear wheels of a vehicle.

The invention provides a vehicle control method and device based on rear wheel steering. The method comprises the steps that whether a target vehicle is in an unstable state or not is judged based on a two-degree-of-freedom vehicle dynamics model of the target vehicle; when the target vehicle is in the unstable state, a rear wheel steering mode is determined according to the vehicle speed of the target vehicle; based on the deviation between the actual yaw velocity and the expected yaw velocity of the target vehicle, determining a rear wheel steering angle by adopting a proportional-integral-derivative controller; and controlling rear wheels of the target vehicle to steer according to the rear wheel steering mode and the rear wheel steering angle. By actively adjusting the steering angle of the rear wheel, the dynamic control capability of the vehicle is remarkably improved.

The invention relates to the technical field of new energy, and discloses an optimization method for an intelligent yaw system of a wind generating set, and the method comprises the steps: building the intelligent yaw system, enabling the system to be internally provided with seven modules, and enabling a multi-sensor data collection module to collect wind direction, wind speed and yaw angle data in real time, the data fusion module performs Kalman filtering fusion processing to obtain a high-confidence state estimation value, the intelligent decision output module generates an optimal yaw control instruction by adopting a multi-mode control strategy and a parameter self-adaptive mechanism based on fused data, an execution mechanism is driven to realize accurate wind alignment, and the system integrated hardware-in-the-loop test module is used for realizing accurate wind alignment. Omnibearing verification can be carried out before deployment, after actual operation, operation indexes are continuously monitored through the performance evaluation module, control parameters and fusion algorithm parameters are adjusted online through the optimization module on the basis of methods such as machine learning, closed-loop optimization is formed, finally, yaw precision, response speed and system stability are remarkably improved, and maintenance cost is reduced.

The invention relates to the technical field of wind power generation control, and discloses a yaw calibration method and system based on a fan sensor, and the method comprises the steps: carrying out the frequency domain and multi-resolution decomposition of a real-time wind direction signal, and obtaining a short-term disturbance level; if yes, recursive state estimation is executed to obtain a smooth wind direction trend; calculating a long-term drift value and carrying out probability state simulation and power loss model mapping to obtain nonlinear influence estimation; if high turbulence is judged, a dynamic compensation threshold value is calculated to generate an adaptive yaw instruction; the actuator is driven, and the alignment precision grade is determined according to the corrected feedback position; and iteratively updating the reference turbulence intensity index to match and optimize the compensation control parameter. According to the method, accurate identification of short-term disturbance and long-term change under complex turbulent flow can be realized, yaw maloperation is effectively reduced, and the alignment precision and the power generation efficiency are improved.

The invention relates to the technical field of mechanical arm vibration suppression, in particular to a mechanical arm tail end active vibration suppression method and system based on predictive compensation, and the method comprises the following steps: S1, collecting the three-axis acceleration, angular velocity and vibration spectrum data of a mechanical arm in real time through a high-frequency IMU sensor; monitoring a rolling angle, a pitch angle and a yaw angle of a base attitude through an auxiliary IMU sensor, and fusing double IMU data through a Kalman filtering algorithm; s2, generating a pre-compensation torque based on the fused data, optimizing a model gain coefficient in combination with a real-time deviation between a frequency spectrum actually measured by a high-frequency IMU sensor and a model prediction frequency spectrum, and correcting and outputting the pre-compensation torque in real time; and S3, the corrected compensation torque is injected into a feed-forward channel of a joint motor control loop, vibration suppression torque is output to a joint execution mechanism through a high-gain controller, and residual vibration at the tail end of the mechanical arm is counteracted in real time. According to the invention, an active vibration suppression system can be constructed to solve the problem of real-time vibration suppression under multi-source disturbance coupling.

The invention relates to the technical field of vehicles, in particular to a distributed driving system, a control method and device of the distributed driving system and a vehicle. The control method of the distributed driving system comprises the steps that operation parameters of the vehicle are obtained; determining a slipping state of the vehicle according to the operating parameters; when the slipping state is a four-wheel full-slipping state, a three-wheel slipping state or a split double-wheel slipping state, the vehicle body state of the vehicle is determined according to the actual yaw velocity of the vehicle; determining a control mode according to the slipping state of the vehicle, the vehicle body state and the road surface state of the vehicle driving road surface; a target drive torque for each drive wheel of the vehicle is determined according to the control mode. By comprehensively judging the slipping state, the vehicle body state and the road surface state of the vehicle, the complex driving scene is accurately recognized, and the problem that the working condition is misjudged due to single information of an existing system is solved.

A model predictive lane centering (MPLC) control system with reference switching and disturbance rejection includes: an MPLC application having control logic that utilizes model predictive control (MPC) for selectively tracking static and dynamic references, adapts MPC weights and constraints for trajectory tracking when the system switches between static and dynamic references. A Kalman filter estimates a lateral force disturbance, a yaw moment disturbance acting upon a vehicle, and a measurement bias corrupting a measured yaw rate. The system detects anomalies in MPC reference trajectory and adjusts actuator constraints in response to detected anomalies. The system actively and continuously adjusts actuator outputs, causing the vehicle to track and follow a current lane center, and on receiving a lane change command, smoothly changes lanes using vehicle actuators to alter position and alter a vehicle trajectory to enter an adjacent lane before returning to tracking and following a center of the adjacent lane.

The invention relates to the field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle autonomous fixed-point landing control method based on an ultrasonic array algorithm, and the method comprises the steps: dividing the landing process of an unmanned aerial vehicle into a normal flight stage, a deceleration approaching stage and a near-ground landing stage, starting an ultrasonic beacon array in the approaching flight stage, and confirming the availability of a landing point; a horizontal azimuth angle, a yaw angle and a relative distance are calculated in real time in a deceleration approach stage to perform landing guidance, direct waves and multi-path reflection signals are analyzed in a near-earth landing stage, characteristic parameters such as time delay inequality and phase offset are extracted, an environment reflection space model is reversely constructed, then positioning correction is calculated, a pose fine adjustment instruction is generated, and the attitude fine adjustment instruction is subjected to attitude fine adjustment. And precise fixed-point landing of the unmanned aerial vehicle is realized. According to the method, high-precision positioning is kept under complex environment disturbance, and the stability, accuracy and efficiency of fixed-point landing of the unmanned aerial vehicle in a complex environment are improved by converting the multipath effect from an interference source to available positioning information.

The invention relates to a Method of controlling a steer-by-wire steering system (1) of a road vehicle having at least two steerable road wheels, wherein two steerable road wheels are connected to each other via a rack (6) moved by a road wheel actuator (5), the method comprising the following steps a) Determining a target rack position of the rack (6) based on a steering input from a driver; b) Calculating a predicted yaw rate (10) of the road vehicle based on a predicted steering wheel angle; c) Measuring an actual yaw rate (11) and calculating a difference between the predicted yaw rate (10) and the actual yaw rate (11); d) Determining a first value for rack position control based on the difference of the actual yaw rate (11) and the predicted yaw rate (10) in a first controller (9); e) Calculating a steady state yaw rate (15) of the road vehicle based on the steering wheel angle, the velocity of the road vehicle and the lateral and longitudinal acceleration; f) Calculating a difference between the steady state yaw rate (15) and the actual yaw rate (11); g) Determining a second value for rack position control based on the difference of the actual yaw rate (11) and the predicted steady state yaw rate (15) in a second controller; h) Calculating a modified rack position request for the road wheel actuator (7) based on the sum (17) of the first and second values and the target rack position.

The invention relates to the technical field of vehicle chassis cooperative control, in particular to a distributed driving special vehicle severe road condition chassis cooperative control method, which comprises the following steps: estimating the whole vehicle mass through a recursive least square method, and estimating a road adhesion coefficient in combination with unscented Kalman filtering and a tire model; dividing into three types of no-load, half-load and full-load according to the mass of the whole vehicle, and dividing into three types of high-attachment, middle-attachment and low-attachment according to a road adhesion coefficient, so as to obtain nine types of driving working conditions; the yaw velocity, the side slip angle and the roll control threshold value based on the transverse load transfer rate are adjusted in a self-adaptive mode according to different working conditions; additional yawing moment is calculated through a PID controller; in combination with a four-wheel vertical force distribution result, a four-wheel independent torque distribution algorithm is used for determining driving torque or braking torque of four wheels, and a corresponding instruction is output through a drive-by-wire chassis interface; the problems of insufficient model precision, poor parameter adaptability and fixed control threshold value existing in the traditional control of the distributed driving special vehicle under the severe road condition are solved.

An aircraft having a plurality of independent yaw control mechanisms includes an airframe having a central fuselage sized to hold at least one operator or payload and a wing extending from the fuselage. The aircraft includes a distributed thrust array coupled to the airframe, the thrust array having at least a first, second, and third pair of propulsion assemblies. Each propulsion assembly includes a rotor and is operable for at least single-axis thrust vectoring. The aircraft further includes a flight control system operable to independently control and combine each yaw mechanism of the propulsion assemblies. The yaw mechanisms for inducing a yaw moment include: canting at least one pair of propulsion assemblies away from the fuselage, selectively tilting at least one pair of propulsion assemblies forwards and backwards, varying an aerodynamic control surface of at least one propulsion assembly, and varying a rotational speed of at least one propulsion assembly.

The invention discloses an electric vehicle stability control method based on steady-state lateral acceleration gain. The method comprises a steady-state yaw reference model, a steady-state lateral acceleration gain model, a feedback control module, a feedforward control module, a rear wheel differential yaw moment distribution module and a vehicle module. The steady-state yaw reference model and the steady-state lateral acceleration gain model are respectively used for calculating an expected yaw velocity and an ideal steering transmission ratio yaw velocity; the vehicle module is used for outputting actual state quantities of the vehicle, including longitudinal vehicle speed, yaw velocity and steering wheel angle; the feedback control module and the feed-forward control module determine feedback yawing moment and feed-forward yawing moment respectively, the feedback yawing moment and the feed-forward yawing moment are added to determine total yawing moment, and the rear wheel differential yawing moment distribution module distributes the total yawing moment according to steady-state characteristic categories of a vehicle and transmits the total yawing moment to the vehicle module for control. Therefore, the lateral stability and the maneuvering performance of the automobile are improved.

The invention belongs to the technical field of distributed electric drive modular transport vehicle planning and control, and particularly relates to a distributed electric drive modular transport vehicle obstacle avoidance planning control method and device. The specific process of the method comprises the steps that two front shafts and two rear shafts which are close to each other are equivalent to two equivalent shafts respectively, two e-SPMT units are used for equivalence, and a monorail dynamic model is established; determining a collision boundary and establishing a collision constraint condition design, establishing a cost function including a swept area, a distance to an obstacle and path smoothness, and optimizing the cost function to obtain an expected path meeting the collision constraint condition; on the basis of the expected path and kinematic constraints, an expected speed and a yaw velocity corresponding to each path point in the expected path are generated; taking the expected speed and the yaw velocity as state quantities, and generating a dynamics control quantity according to the monorail dynamics model; the dynamic control quantity is distributed to each wheel longitudinal force and turn angle.