Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Bijo Sebastian ◽  
Pinhas Ben-Tzvi

This paper describes the use of an active disturbance rejection controller (ADRC) to estimate and compensate for the effect of slip in an online manner to improve the path tracking performance of autonomous ground vehicles (AGVs). AGVs with skid-steer locomotion mode are extensively used for robotic applications in the fields of agriculture, transportation, construction, warehouse maintenance, and mining. Majority of these applications such as performing reconnaissance and rescue operations in rough terrain or autonomous package delivery in urban scenarios, require the system to follow a path predetermined by a high-level planner or based on a predefined task. In the absence of effective slip estimation and compensation, the AGVs, especially tracked vehicles, can fail to follow the path as given out by the high-level planner. The proposed ADRC architecture uses a generic mathematical model that can account for the scaling and shift in the states of the system due to the effects of slip through augmented parameters. An extended Kalman filter (EKF) observer is used to estimate the varying slip parameters online. The estimated parameters are then used to compensate for the effects of slip at each iteration by modifying the control actions given by a low-level path tracking controller. The proposed approach is validated through experiments over flat and uneven terrain conditions including asphalt, vinyl flooring, artificial turf, grass, and gravel using a tracked skid-steer mobile robot. A detailed discussion on the results and directions for future research is also presented.

Author(s):  
Zhiqiang Zuo ◽  
Mengjia Yang ◽  
Haoyu Wang ◽  
Yijing Wang ◽  
Li Wang ◽  
...  

This paper presents a lateral control strategy with kinematic state error model-based predictive control and extended state observer for unmanned ground vehicles. Firstly, we propose a circular arc prediction technique to calculate the state of the reference system. Then, inspired by the idea of active disturbance rejection control, an extended state observer is utilized to estimate the value of the total disturbance caused by modeling uncertainties, external disturbance, and other factors in order to compensate model error. Finally, we propose a lateral controller that combines model-based prediction with extended state observer through state feedback to achieve precise trajectory tracking. The performance of the proposed control strategy is demonstrated by a co-simulation between CarSim and MATLAB/Simulink.


ROBOT ◽  
2011 ◽  
Vol 33 (4) ◽  
pp. 461-466 ◽  
Author(s):  
Hao LIU ◽  
Tao WANG ◽  
Wei FAN ◽  
Tong ZHAO ◽  
Junzheng WANG

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