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Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately.
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An Intermittent Learning Algorithm for High-Speed Autonomous Driving in Unknown Environments
An intermittent, model-free optimal control algorithm that enables an autonomous vehicle to track a nonpredetermined trajectory at high speed is presented. The approachisbandwidthandenergyefficientinthatcommunication between actuators is limited to instances when it is needed rather than performing unnecessary periodic updates. We formulate the problem by properly augmenting the system and reference (trajectory) data, and then designing a triggering mechanism for the controller to work with a sampled version of the augmented states at some triggering instants. In order to obtain a model-free solution, we leverage a Q-learning framework with a zero-order hold actor network and a critic network to approximate the optimal intermittent controller and the optimal cost, respectively, resulting in appropriate tuning laws. Finally, we provide a numerical example of an ground vehicle driving autonomously at high-speed on a race track.
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- PAR ID:
- 10121585
- Date Published:
- Journal Name:
- 2019 American Control Conference (ACC)
- Page Range / eLocation ID:
- 4286-4292
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
