Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy road...Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.展开更多
The performance of the vehicle dynamics stability control system（DSC） is dominated by the accurate estimation of tire forces in real-time.The characteristics of tire forces are determined by tire dynamic states and ...The performance of the vehicle dynamics stability control system（DSC） is dominated by the accurate estimation of tire forces in real-time.The characteristics of tire forces are determined by tire dynamic states and parameters,which vary in an obviously large scope along with different working conditions.Currently,there have been many methods based on the nonlinear observer to estimate the tire force and dynamic parameters,but they were only used in off-line analysis because of the computation complexity and the dynamics differences of four tires in the steering maneuver conditions were not considered properly.This paper develops a novel algorithm to observe tire parameters in real-time controller for DSC.The algorithm is based on the sensor-fusion technology with the signals of DSC sensors,and the tire parameters are estimated during a set of maneuver courses.The calibrated tire parameters in the control cycle are treated as the elementary states for vehicle dynamics observation,in which the errors between the calculated and the measured vehicle dynamics are used as the correcting factors for the tire parameter observing process.The test process with a given acceleration following a straight line is used to validate the estimation method of the longitudinal stiffness;while the test process with a given steering angle is used to validate the estimated value of the cornering stiffness.The ground test result shows that the proposed algorithm can estimate the tire stiffness accurately with an acceptable computation cost for real-time controller only using DSC sensor signal.The proposed algorithm can be an efficient algorithm for estimating the tire dynamic parameters in vehicle dynamics stability control system,and can be used to improve the robustness of the DSC controller.展开更多
This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, ha...This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, has been implemented in the MATLAB environment and was developed based on a John Deere tractor. The simulation results from this MATLAB model was validated through field navigation tests. The accuracy of the trajectory estimation is strongly affected by the determination of the cornering stiffness of the tractor. In this simulation, the tractor cornering stiffness analysis was identified during simulation analysis using the MATLAB model based on the recorded trajectory data. The obtained data was used in simulation analyses for various navigation operations in the field of interest. The analysis on field validation test results indicated that the developed tractor system could accurately estimate wheel trajectories of a tractor system while operating in agricultural fields at various speeds. The results also indicated that the developed system could accurately determine tractor velocity and steering angle while the tractor operates in curved fields.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52405112,U24A20199)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20240973).
文摘Four-Wheel Independent Steering(4WIS)Vehicles can independently control the angle of each wheel,demonstrating superior trajectory tracking performance under normal conditions.However,on intermittent icy and snowy roads,the presence of time-varying adhesion coefficients,time-varying cornering stiffness,and the irregularities due to ice and snow accumulation introduce multiple uncertainties into the steering system,significantly degrading the trajectory tracking performance of 4WIS vehicles.In response,this paper proposes a robust Tube Model Predictive Control(Tube-MPC)trajectory tracking control method for 4WIS.In this method,a Bi-directional Long Short-Term Memory neural network is established for online estimation of tire cornering stiffness under different road adhesion coefficients,providing accurate estimation of time-varying cornering stiffness for each wheel to mitigate the uncertainties of time-varying adhesion coefficients and cornering stiffness.Additionally,considering the road irregularities caused by snow accumulation on intermittent icy and snowy roads,a trajectory tracking controller that integrates Tube-MPC and robust Sliding Mode Control is proposed.The nominal MPC model,developed from the estimated tire cornering stiffness,utilizes the sliding surface and the optimal auxiliary control unit law for the tube is derived from the reaching law in Tube-MPC,aiming to minimize the trajectory tracking error while enhancing the controller’s robustness against road uncertainties.The experiments show that the proposed method outperforms the Tube-MPC algorithm in terms of trajectory accuracy and robustness.This method demonstrates excellent trajectory tracking accuracy under intermittent icy and snowy road conditions,and it lays a theoretical foundation for future studies on vehicle stability and trajectory tracking under such road conditions.
基金supported by National Natural Science Foundation of China (Grant No.50905092)
文摘The performance of the vehicle dynamics stability control system（DSC） is dominated by the accurate estimation of tire forces in real-time.The characteristics of tire forces are determined by tire dynamic states and parameters,which vary in an obviously large scope along with different working conditions.Currently,there have been many methods based on the nonlinear observer to estimate the tire force and dynamic parameters,but they were only used in off-line analysis because of the computation complexity and the dynamics differences of four tires in the steering maneuver conditions were not considered properly.This paper develops a novel algorithm to observe tire parameters in real-time controller for DSC.The algorithm is based on the sensor-fusion technology with the signals of DSC sensors,and the tire parameters are estimated during a set of maneuver courses.The calibrated tire parameters in the control cycle are treated as the elementary states for vehicle dynamics observation,in which the errors between the calculated and the measured vehicle dynamics are used as the correcting factors for the tire parameter observing process.The test process with a given acceleration following a straight line is used to validate the estimation method of the longitudinal stiffness;while the test process with a given steering angle is used to validate the estimated value of the cornering stiffness.The ground test result shows that the proposed algorithm can estimate the tire stiffness accurately with an acceptable computation cost for real-time controller only using DSC sensor signal.The proposed algorithm can be an efficient algorithm for estimating the tire dynamic parameters in vehicle dynamics stability control system,and can be used to improve the robustness of the DSC controller.
基金Project supported by the National Natural Science Foundation of China (No. 30270773)the Teaching and Research Award Program forOutstanding Young Teachers in Higher Education Institutions of MOE,Chinaand the Natural Science Foundation of Zhejia
文摘This research aims at using a dynamic model of tractor system to support navigation system design for an automati- cally guided agricultural tractor. This model, consisting of a bicycle model of the tractor system, has been implemented in the MATLAB environment and was developed based on a John Deere tractor. The simulation results from this MATLAB model was validated through field navigation tests. The accuracy of the trajectory estimation is strongly affected by the determination of the cornering stiffness of the tractor. In this simulation, the tractor cornering stiffness analysis was identified during simulation analysis using the MATLAB model based on the recorded trajectory data. The obtained data was used in simulation analyses for various navigation operations in the field of interest. The analysis on field validation test results indicated that the developed tractor system could accurately estimate wheel trajectories of a tractor system while operating in agricultural fields at various speeds. The results also indicated that the developed system could accurately determine tractor velocity and steering angle while the tractor operates in curved fields.
