Disturbance Observer Based Control for Four Wheel Steering Vehicles With Model Reference

This paper presents a disturbance observer based control strategy for four wheel steering systems in order to improve vehicle handling stability. By combination of feedforward control and feedback control, the front and rear wheel steering angles are controlled simultaneously to follow both the desired sideslip angle and the yaw rate of the reference vehicle model.A nonlinear three degree-of-freedom four wheel steering vehicle model containing lateral, yaw and roll motions is built up, which also takes the dynamic effects of crosswind into consideration.The disturbance observer based control method is provided to cope with ignored nonlinear dynamics and to handle exogenous disturbances. Finally, a simulation experiment is carried out,which shows that the proposed four wheel steering vehicle can guarantee handling stability and present strong robustness against external disturbances.

Study on control mechanism for turning of tracked vehicles with twin driving

Turning mechanism is important assemblies for tracked vehicles. Turning performance is important evaluating indicator. The performance of the turning mechanism directly affect the mobility and productivity of the crawler. However, there are still some problems crying out for solutions in superior turning mechanism for vehicle engineering area. Composition and performance of turning system in agricultural tracked vehicles matched with twin driving differential turning mechanism was introduced, which adopted quiet hydraulic double pumps and double motors, took advantage of flexibility greatly for track vehicle turning and benefit for handling used steering wheel.

Decoupling Control of Lateral Dynamics for 4WS Vehicle

An four wheel steering (4WS) feedback control system that simultaneously achieves both body sideslip angle and yaw rate responses always desirable regardless of changes in vehicle dynamics. Quantitative feedback theory (QFT) is offered as the main tool for designing the control law. Inverted decoupling is also employed to make multivariable quantitative feedback design easier. Various nonlinear analyses are carried out and show that the proposed control system is a robust decoupling controller which not only makes body sideslip angle and yaw rate of the vehicle track the desired reference input signals respectively, but also satisfies the requirement of robustness for the control system. The results also indicate that the control system can make it available to realize ideal lateral steering dynamics tracking for vehicles.

Optimization of the key position parameters for tractor steering wheel based on a driver’s arm muscle load analysis

Steering wheel is the most frequently used manual device in tractors,whose position directly affects the handling comfort of the driver and fatigue degree of the arm muscles.In this study,the biomechanical modelling software AnyBody was used for an inverse kinetics analysis of the rotation process of tractor steering wheel,calculate the muscle activation degree of the driver’s arm and compare it with the calculated results of surface EMG tests to verify the reliability of the biomechanical model.Based on the biomechanical model,the effects of three position parameters(steering wheel inclination,front-back distance,and upper-lower height)on the activation degree of the driver’s arm muscles were evaluated.The results demonstrated that steering wheel inclination has the most significant effect on the degree of muscle activation,followed by the upper-lower height and then front-back distance.Considering the interaction among factors,a regression orthogonal test was designed,and the test results revealed that the minimum muscle activation(1.2887)can be obtained with the steering wheel inclination of 31°,front-back distance of 431 mm and upper-lower height of 375 mm.The findings can provide a reference for optimizing the structure and position parameters of tractor steering wheels.

Method for measuring the steering wheel angle of paddy field agricultural machinery by integrating RTK-GNSS and dual-MEMS gyroscope

Aiming at the application environment of paddy agricultural machinery with bumpy and undulating changes,the problems affecting the method for steering wheel angle measurement by MEMS gyroscope were analyzed,and a wheel angle measurement method combining Dual-MEMS gyroscope(dual MEMS gyroscope)and RTK-GNSS was designed.The adaptive weighting method was used to fuse the heading angle differentiation of RTK-GNSS,the MEMS gyroscope angle rate,and velocity data,and the rod-arm compensation was performed to accurately obtain the angle rates of the body and steering wheels of agricultural machinery;the difference between the combined angular rate of the steering wheel of the agricultural machinery and the angular rate of the agricultural machinery body was obtained,and the integrator is used to integrate the difference to get the wheel steering angle value,and the Kalman filter was designed to make feedback correction for the integration process of angle calculation to eliminate the errors caused by the gyroscope zero bias,random drift,and gyroscope rod arm effect,and to obtain the accurate value of wheel steering angle.A comparative test with the connecting rod wheel angle sensor was designed,and the results show that the maximum deviation is 4.99°,the average absolute average value is 1.61°,and the average standard deviation is 0.98°.The method in this study and the connecting rod wheel angle sensor were used on paddy farm machinery.The wheel angle measurement deviation of the proposed method and the connecting rod wheel angle sensor was not more than 1°,which is relatively small.It has good stability,speed adaptability,and dynamic responsiveness that meets the accuracy requirements of steering wheel angle measurement for paddy field agricultural machinery unmanned driving and can be used instead of connecting rod angle sensors for unmanned agricultural machinery.

Comfort evaluation and position parameter optimization of the steering wheel in agricultural machinery based on a three-level evaluation index system

The aim of this study is to evaluate the comfort and optimize the position parameters of steering wheel.Taking the H point of driver as the reference point,three position parameters of steering wheel were determined,which were used as experimental factors.A comprehensive evaluation index system of the comfort was established.The comfort range and optimal levels of three parameters were determined by a single factor test,based on which a response surface optimization and validation test was carried out.The optimization and validation test results show that the expected comprehensive score of the comfort is 0.864,and the average relative error between the predicted and the measured value is 4.18%,indicating that the optimization results are reliable.The findings can provide reference for the comfort optimization design of steering wheel in agricultural devices.

Usability Assessment of Steering Wheel Control Interfaces in Motorsport

In order to assess the usability of steering wheel control interfaces in motorsport,it is necessary to employ a set of appropriate methods.The method selection process first involves identifying the relevant motorsport-based usability criteria.The unique factors associated with context of use in motorsport are then defined.These are employed to synthesize a set of key performance indicators(KPIs)that require specific analysis.The first KPI relates to error rates;these should be minimized,particularly under high cognitive load on the primary.The second KPI involves task times;these should be minimized not just to reduce distraction,but also to improve competitive performance.The third KPI states that usability should be optimized to minimize visual distraction.The fourth KPI advocates the minimizing of interface task load to reduce the effect on the primary task.The final KPI states that interface functionality should be easy to learn and recall.The KPIs provide clear goals to guide the identification of the most appropriate methods for each aspect of usability.Methods categories are devised and sets of appropriate methods selected based on the KPIs.These then combine into a toolset with an associated application process with the overall goal of providing a means by which motorsport interfaces may be both analysed and improved.

Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30%of their flight time,and it always exceeds 50%for short-haul aircraft when the ground traffic is congested.Aircraft also contribute significantly to emissions,fuel burn,and noise when taxiing on the ground at airports.There is an urgent need to reduce aircraft taxiing time on the ground.However,it is too expensive for airports and aircraft carriers to build and maintain more runways,and it is space-limited to tow the aircraft fast using tractors.Autonomous drive capability is currently the best solution for aircraft,which can save the maneuver time for aircraft.An idea is proposed that the wheels are driven by APU-powered(auxiliary power unit)motors,APU is working on its efficient point;consequently,the emissions,fuel burn,and noise will be reduced significantly.For Front-wheel drive aircraft,the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn.Forward traction effects the aircraft’s maximum turning ability,which is difficult to be modeled to guide the controller design.Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models;however,the models of related works are always simplified,fixed,or not easily modified,but that is what we care about most.Only with complex models can the trained controller be intelligent.High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design.This paper focuses on the maneuvering problem of front-wheel drive aircraft,a high-fidelity aircraft taxiing dynamic model is established,including the 6-DOF airframe,landing gears,and nonlinear tire force model.A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft.It is proved that in some conditions,the DRL based controller outperformed conventional look-ahead controllers.