scholarly journals Yaw stability control of automated guided vehicle under the condition of centroid variation

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Wei Liu ◽  
Qingjie Zhang ◽  
Yidong Wan ◽  
Ping Liu ◽  
Yue Yu ◽  
...  
Keyword(s):  
Stability Control ◽  
Automated Guided Vehicle ◽  
Yaw Stability

scholarly journals Yaw stability control of automated guided vehicle under the condition of centroid variation

2021 ◽  
Author(s):  
Wei Liu ◽  
Qingjie Zhang ◽  
Yidong Wan ◽  
Ping Liu ◽  
Yue Yu ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Stability Control ◽  
Theory Model ◽  
Vehicle Body ◽  
Maximum Deviation ◽  
Automated Guided Vehicle ◽  
Steering Control ◽  
Sideslip Angle ◽  
Low Speed ◽  
Yaw Stability

Abstract The centroid of an automated guided vehicle changes due to the irregular position and unbalanced weight of the merchandises on the load platform, which affects the completion of the handling task between stations in intelligent factories. This paper presents a hierarchical control strategy to improve yaw stability considering centroid variation. Firstly, the vehicle body and hub motor models are established based on dynamics. Secondly a hierarchical controller is designed by using the method of extension theory, model predictive control (MPC) and sliding mode control. Then based on CarSim and Simulink, the step co-simulation of the low-speed condition of the automated guided vehicle is carried out. Compared with the uncontrolled condition, the maximum deviation of the yaw rate is reduced from 0.58 rad/s to 0.52 rad/s, and the error with the theoretical value is reduced from 16% to 4%; the maximum deviation of the centroid sideslip angle is reduced from -0.84 rad to -0.77 rad, and the error with the theoretical value is reduced from 12% to 3%. Finally, a four-wheel drive and four-wheel steering automated guided vehicle is manufactured to carry out inter station steering experiments in simulated factory environment. The error between simulation and experiment is less than 5%. The results show that the designed controller is effective, and the research can provide theoretical and experimental basis for the low-speed steering control stability of automated guided vehicle.

scholarly journals Yaw stability control of automated guided vehicle under the condition of centroid variation

2021 ◽  
Author(s):  
Wei Liu ◽  
Qingjie Zhang ◽  
Yidong Wan ◽  
Ping Liu ◽  
Yue Yu ◽  
...  
Keyword(s):  
Sliding Mode ◽  
Stability Control ◽  
Theory Model ◽  
Vehicle Body ◽  
Maximum Deviation ◽  
Automated Guided Vehicle ◽  
Steering Control ◽  
Sideslip Angle ◽  
Low Speed ◽  
Yaw Stability

Abstract The centroid of an automated guided vehicle changes due to the irregular position and unbalanced weight of the merchandises on the load platform, which affects the completion of the handling task between stations in intelligent factories. This paper presents a hierarchical control strategy to improve yaw stability considering centroid variation. Firstly, the vehicle body and hub motor models are established based on dynamics. Secondly a hierarchical controller is designed by using the method of extension theory, model predictive control (MPC) and sliding mode control. Then based on CarSim and Simulink, the step co-simulation of the low-speed condition of the automated guided vehicle is carried out. Compared with the uncontrolled condition, the maximum deviation of the yaw rate is reduced from 0.58 rad/s to 0.52 rad/s, and the error with the theoretical value is reduced from 16% to 4%; the maximum deviation of the centroid sideslip angle is reduced from -0.84 rad to -0.77 rad, and the error with the theoretical value is reduced from 12% to 3%. Finally, a four-wheel drive and four-wheel steering automated guided vehicle is manufactured to carry out inter station steering experiments in simulated factory environment. The error between simulation and experiment is less than 5%. The results show that the designed controller is effective, and the research can provide theoretical and experimental basis for the low-speed steering control stability of automated guided vehicle.

Composite Nonlinear Feedback for Vehicle Active Front Steering

2014 ◽  
Vol 663 ◽  
pp. 127-134 ◽  
Author(s):  
M.H. Che Hasan ◽  
Y.M. Sam ◽  
Ke Mao Peng ◽  
Muhamad Khairi Aripin ◽  
Muhamad Fahezal Ismail
Keyword(s):  
Control Method ◽  
Actuator Saturation ◽  
External Disturbance ◽  
Reference Signal ◽  
Stability Control ◽  
Nonlinear Feedback ◽  
Composite Nonlinear Feedback ◽  
Active Front Steering ◽  
Yaw Stability ◽  
Fast Settling

In this paper, Composite Nonlinear Feedback (CNF) is applied on Active Front Steering (AFS) system for vehicle yaw stability control in order to have an excellent transient response performance. The control method, which has linear and nonlinear parts that work concurrently capable to track reference signal very fast with minimum overshoot, fast settling time, and without exceed nature of actuator saturation limit. Beside, modelling of 7 degree of freedom for typical passenger car with magic formula to represent tyre nonlinearity behaviour is also presented to simulate controlled vehicle as close as possible with a real situation. An extensive computer simulation is performed with considering a various profile of cornering manoeuvres with external disturbance to evaluate its performance in different scenarios. The performance of the proposed controller is compared to conventional Proportional Integration and Derivative (PID) for effectiveness analysis.

scholarly journals A Fault Tolerant Vehicle Stability Control Using Adaptive Control Allocation

2018 ◽  
Author(s):  
Ozan Temiz ◽  
Melih Cakmakci ◽  
Yildiray Yildiz
Keyword(s):  
Adaptive Control ◽  
Fault Tolerant ◽  
Stability Control ◽  
Steering Wheel ◽  
Slip Angle ◽  
Control Allocation ◽  
Control Input ◽  
Allocation Strategy ◽  
Virtual Control ◽  
Yaw Stability

This paper presents an integrated fault-tolerant adaptive control allocation strategy for four wheel frive - four wheel steering ground vehicles to increase yaw stability. Conventionally, control of brakes, motors and steering angles are handled separately. In this study, these actuators are controlled simultaneously using an adaptive control allocation strategy. The overall structure consists of two steps: At the first level, virtual control input consisting of the desired traction force, the desired moment correction and the required lateral force correction to maintain driver’s intention are calculated based on the driver’s steering and throttle input and vehicle’s side slip angle. Then, the allocation module determines the traction forces at each wheel, front steering angle correction and rear steering wheel angle, based on the virtual control input. Proposed strategy is validated using a non-linear three degree of freedom reduced two-track vehicle model and results demonstrate that the vehicle can successfully follow the reference motion while protecting yaw stability, even in the cases of device failure and changed road conditions.

Research on Yaw Stability Control of Active Front Steering Based on BP Neural Network PID

2013 ◽  
Vol 765-767 ◽  
pp. 1903-1907
Author(s):  
Jie Wei ◽  
Guo Biao Shi ◽  
Yi Lin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Steering System ◽  
Stability Control ◽  
Steering Angle ◽  
System A ◽  
Active Front Steering ◽  
Yaw Stability ◽  
Neural Network Pid

This paper proposes using BP neural network PID to improve the yaw stability of the vehicle with active front steering system. A dynamic model of vehicle with active front steering is built firstly, and then the BP neural network PID controller is designed in detail. The controller generates the suitable steering angle so that the vehicle follows the target value of the yaw rate. The simulation at different conditions is carried out based on the fore established model. The simulation results show the BP neural network PID controller can improve the vehicles yaw stability effectively.

DLMPCS-based improved yaw stability control strategy for DDEV

2019 ◽  
Vol 13 (9) ◽  
pp. 1329-1339 ◽  
Author(s):  
Ke Shi ◽  
Xiaofang Yuan ◽  
Guoming Huang ◽  
Xizheng Zhang ◽  
Yongpeng Shen
Keyword(s):  
Control Strategy ◽  
Stability Control ◽  
Yaw Stability
Sign in / Sign up

Export Citation Format

Share Document