An intelligent-optimal predictive controller for path tracking of Vision-based Automated Guided Vehicle

Author(s):  
Xing Wu ◽  
Peihuang Lou ◽  
Dunbing Tang ◽  
Jun Yu
Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 671
Author(s):  
Jialing Yao ◽  
Meng Wang ◽  
Zhihong Li ◽  
Yunyi Jia

To improve the handling stability of automobiles and reduce the odds of rollover, active or semi-active suspension systems are usually used to control the roll of a vehicle. However, these kinds of control systems often take a zero-roll-angle as the control target and have a limited effect on improving the performance of the vehicle when turning. Tilt control, which actively controls the vehicle to tilt inward during a curve, greatly benefits the comprehensive performance of a vehicle when it is cornering. After analyzing the advantages and disadvantages of the tilt control strategies for narrow commuter vehicles by combining the structure and dynamic characteristics of automobiles, a direct tilt control (DTC) strategy was determined to be more suitable for automobiles. A model predictive controller for the DTC strategy was designed based on an active suspension. This allowed the reverse tilt to cause the moment generated by gravity to offset that generated by the centrifugal force, thereby significantly improving the handling stability, ride comfort, vehicle speed, and rollover prevention. The model predictive controller simultaneously tracked the desired tilt angle and yaw rate, achieving path tracking while improving the anti-rollover capability of the vehicle. Simulations of step-steering input and double-lane change maneuvers were performed. The results showed that, compared with traditional zero-roll-angle control, the proposed tilt control greatly reduced the occupant’s perceived lateral acceleration and the lateral load transfer ratio when the vehicle turned and exhibited a good path-tracking performance.


2021 ◽  
pp. 1-20
Author(s):  
Ying Tian ◽  
Qiangqiang Yao ◽  
Chengqiang Wang ◽  
Shengyuan Wang ◽  
Jiaqi Liu ◽  
...  

1998 ◽  
Vol 31 (3) ◽  
pp. 153-158
Author(s):  
J.E. Normey-Rico ◽  
J. Gómez-Ortega ◽  
E.F. Camacho

2019 ◽  
Vol 9 (7) ◽  
pp. 1372 ◽  
Author(s):  
Guoxing Bai ◽  
Li Liu ◽  
Yu Meng ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  

Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.


1999 ◽  
Vol 7 (6) ◽  
pp. 729-740 ◽  
Author(s):  
Julio E. Normey-Rico ◽  
Juan Gómez-Ortega ◽  
Eduardo F. Camacho

Sign in / Sign up

Export Citation Format

Share Document