Abstract

The centroid of an automated guided vehicle (AGV) changes due to the irregular position and uneven weight of the cargo 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 and sliding mode control. Then based on CarSim and Simulink, the low-speed step co-simulation condition of the AGV is carried out. Compared to the uncontrolled condition, the maximum deviation of the yaw rate is reduced from 0.58 to 0.52 rad/s, and the difference 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 difference with the theoretical value is reduced from 12 to 3%. Finally, a four-wheel drive and four-wheel steering AGV are manufactured to carry out inter station steering experiments in simulated factory environment on different road adhesion coefficients. The difference 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 AGV.

Highlights

  • A hierarchical controller is designed by using the method of extension theory, model predictive control (MPC) and sliding mode control

  • 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%

  • The upper controller is used to solve the additional yaw moment required by the yaw stability control, which is composed of yaw rate controller, sideslip angle of centroid controller, extension joint controller and sliding mode controller; the lower controller is the driving torque distribution controller, which distributes the additional yaw moment calculated by the upper controller to four driving wheels after constraint

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Summary

Dynamic model

The stability of the vehicle is mainly determined by the lateral motion and yaw motion, so the vehicle model only needs to consider the longitudinal motion, lateral motion and yaw motion. The following idealized assumptions should be made. O is the instantaneous center; CG is the centroid; L is the distance from the front axle to the rear axle; MZ is the yaw moment; β is the sideslip angle of centroid. According to the force balance and moment wheels respectively; δrl and δrr are the left and balance, the motion equation of three degrees of right steering angles of the rear wheels freedom is obtained as follows: respectively. Equation of yaw motion (i=fl, fr, rl, rr) are the longitudinal force and lateral force of the tire respectively; δfl and δfr are the left and right steering angles of the front. Iz is the moment of inertia around the z-axis; γ. is the yaw angular acceleration; Lf and Lr are the distances from the centroid to the front and rear axles respectively; d1 and d2 are the distances from the left wheel and the right wheel to the equivalent wheel respectively

Hub motor model
Design of yaw stability controller
Model predictive control
Design of upper controller
Design of lower controller
Simulation results and analysis
Development of prototype vehicle
Experimental results and analysis
Conclusion
Declaration
Full Text
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