Frontiers of Information Technology & Electronic Engineering
>> 2020,
Volume 21,
Issue 7
doi:
10.1631/FITEE.1900185
A double-layered nonlinear model predictive control based control algorithm for local trajectory planning for automated trucks under uncertain road adhesion coefficient conditions
Affiliation(s): College of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; College of Automobile and Transportation, Guangxi University of Science and Technology, Liuzhou 545006, China; less
Received: 2019-04-08
Accepted: 2020-07-10
Available online: 2020-07-10
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Abstract
We present a double-layered control algorithm to plan the local trajectory for s equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer (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 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.
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