A robust nonlinear skid-steering control design applied to the MULE (6x6) unmanned ground vehicle

The Fuzzy Steering Control Using a Slope Direction Estimation Method for Small Unmanned Ground Vehicle

Journal of the Korea Institute of Military Science and Technology ◽

10.9766/kimst.2012.15.6.721 ◽

2012 ◽

Vol 15 (6) ◽

pp. 721-728

Author(s):

Sang Hoon Lee ◽

Jin Wook Huh ◽

Sincheon Kang ◽

Myung Chun Lee

Keyword(s):

Estimation Method ◽

Unmanned Ground Vehicle ◽

Steering Control ◽

Ground Vehicle ◽

Direction Estimation ◽

Slope Direction

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Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

Measurement and Control ◽

10.1177/0020294019878871 ◽

2020 ◽

Vol 53 (3-4) ◽

pp. 501-518

Author(s):

Chaofang Hu ◽

Lingxue Zhao ◽

Lei Cao ◽

Patrick Tjan ◽

Na Wang

Keyword(s):

Path Planning ◽

Model Predictive Control ◽

Obstacle Avoidance ◽

Predictive Control ◽

Path Tracking ◽

Unmanned Ground Vehicle ◽

Steering Control ◽

Steering Angle ◽

Ground Vehicle ◽

Tracking Controller

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

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