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Sequential Distributed Model Predictive Control for Automated Mining Fleet
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DOI：10.1109/ACCESS.2023.3311520.png)
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Abstract
En 中文
This work mainly focuses on fleet collaborative control for autonomous mining fleets. In this paper, we first developed a double-layer coupling model including the intersection platoon (IP) in the upper layer and the following platoon (FP) in the lower layer and unified the headway calculation and the structure of the control problem of the two types of platoons. Then, the constant time headway (CTH) spacing policy is implemented to ensure safety during fleet operation. Next, a controller based on the sequential distributed model predictive control (SEQ_DMPC) algorithm is proposed to solve the platoon control problem. To verify the proposed controller, we built a second-order electric truck longitudinal dynamics model as the actuators and verified the accuracy of the model. Next, we assume that all trucks are permitted to implement optimization simultaneously at each time step in the simulation and verify the platoon following stability based on the electric truck dynamic model. Finally, we simulated the fleet production process, and the results show that the proposed controller performs well in all aspects.
Keywords:
Autonomous vehicles
Power distribution control
Traffic control
distributed control
intelligent transportation systems
predictive models
traffic control
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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