Event-Triggered Model Predictive Control for an Autonomous Vehicle Based on Tight Constraints

This paper develops an event-triggered model pre-dictive control strategy based on tight constraints for the control of autonomous vehicle. First, a discrete-time error model is established for a vehicle according to the magic formula-based tire model and the simplified bicycle model. Then, constrai...

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Bibliographic Details
Published inIEEE International Conference on Industrial Informatics (INDIN) pp. 1 - 6
Main Authors Lin, Xiurui, Chen, Hantuo, Qin, Dongdong, Liu, Andong, Ni, Hongjie, Wang, Ye
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.08.2024
Subjects
autonomous vehicle
Autonomous vehicles
Closed loop systems
constraint tightening
Event detection
event-triggered strategy
model predictive control
Optimization
Prediction algorithms
Predictive control
Simulation
Stability analysis
Sufficient conditions
Tires
trajectory tracking
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Summary:This paper develops an event-triggered model pre-dictive control strategy based on tight constraints for the control of autonomous vehicle. First, a discrete-time error model is established for a vehicle according to the magic formula-based tire model and the simplified bicycle model. Then, constraints with continuous contraction are constructed based on the tight constraint control mechanism to avoid excessive proximity to initial constraints and improve the vehicle comfort. Besides, an event-triggering condition is derived based on the input state stability (ISS) condition. Finally, the feasibility of the algorithm is proved, and the stability of the closed-loop system is derived. Simulation results show that this strategy can effectively reduce the waste of resources under the premise of ensuring a good vehicle tracking effect.
ISSN:2378-363X
DOI:10.1109/INDIN58382.2024.10774245