A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions

We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MP...

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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering 2020-07, Vol.21 (7), p.1059-1073
Main Authors: Wang, Hong-chao, Zhang, Wei-wei, Wu, Xun-cheng, Cao, Hao-tian, Gao, Qiao-ming, Luo, Su-yun
Format: Article
Language:English
Subjects:
Algorithms
Automation
Closed loops
Collision avoidance
Collision dynamics
Communications Engineering
Computer Hardware
Computer Science
Computer Systems Organization and Communication Networks
Control algorithms
Control methods
Control systems design
Control theory
Controllers
Electrical Engineering
Electronics and Microelectronics
Feedback control
Instrumentation
Networks
Nonlinear control
Predictive control
Proportional integral derivative
Roads
Surface stability
Trajectory control
Trajectory planning
Trucks
Vehicle safety
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Summary:We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.
ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.1900185