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

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.

scholarly journals Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities

2020 ◽  
pp. 095440622094546
Author(s):  
Husain Kanchwala ◽  
Icaro Bezerra Viana ◽  
Nabil Aouf
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Mixed Integer ◽  
Distributed Model ◽  
Programming Approach ◽  
High Fidelity ◽  
Vehicle Model ◽  
Integer Quadratic Programming ◽  
Tracking Controller

This paper discusses cooperative path-planning and tracking controller for autonomous vehicles using a distributed model predictive control approach. Mixed-integer quadratic programming approach is used for optimal trajectory generation using a linear model predictive control for path-tracking. Cooperative behaviour is introduced by broadcasting the planned trajectories of two connected automated vehicles. The controller generates steering and torque inputs. The steering and drive motor actuator constraints are incorporated in the control law. Computational simulations are performed to evaluate the controller for vehicle models of varying complexities. A 12-degrees-of-freedom vehicle model is developed and is subsequently linearised to be used as the plant model for the linearised model predictive control-based tracking controller. The model behaviour is compared against the kinematic, bicycle and the sophisticated high-fidelity multi-body dynamics CarSim model of the vehicle. Vehicle trajectories used for tracking are longitudinal and lateral positions, velocities and yaw rate. A cooperative obstacle avoidance manoeuvre is performed at different speeds using a co-simulation between the controller model in Simulink and the high-fidelity vehicle model in CarSim. The simulation results demonstrate the effectiveness of the proposed method.

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