Method for the Trajectory Tracking Control of Unmanned Ground Vehicles Based on Chaotic Particle Swarm Optimization and Model Predictive Control

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the m...

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Veröffentlicht in:Symmetry (Basel) Jg. 16; H. 6; S. 708
Hauptverfasser: Jin, Mengtao, Li, Junmin, Chen, Te
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2024
Schlagworte:
Accuracy
Algorithms
Computational efficiency
Control algorithms
Control methods
Controllers
Efficiency
Ground based control
Lane changing
Lateral stability
Local optimization
Mathematical optimization
Methods
Motion control
Multiple objective analysis
Optimization algorithms
Particle swarm optimization
Predictive control
Symmetry
Time lag
Tracking control
Tracking errors
Trajectory control
Unmanned ground vehicles
Vehicles
Yaw
ISSN:2073-8994, 2073-8994
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Zusammenfassung:The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym16060708