Sensors · 2017 · 154 citations · 24 references
EngineeringAutomotive EngineeringMechatronics EngineeringArtificial IntelligenceAutonomous VehiclesVehicle DynamicsAdaptive ControlControl TheoryIntelligent Transportation SystemsLateral Vehicle DynamicsVehicle ControlVehicle Control SystemsAutonomous Vehicle NavigationControl System ModelingIntelligent ControlLateral ControlNeural Network ControlNonlinear System ModelingNonlinear Vehicle DynamicsPath Tracking Control
Intelligent vehicles are complex nonlinear systems, making path‑tracking controller design a key research area. The study aims to design a lateral control dynamic model for intelligent vehicle lateral tracking. The authors build a vehicle dynamics transfer function, construct a second‑order control system using a CARMA model, identify parameters with forgetting‑factor recursive least squares, and implement a neural‑network PID controller for lateral path tracking. Simulation results demonstrate that the proposed model and algorithm achieve high real‑time performance and robustness in path‑tracking control, providing a theoretical basis for autonomous navigation and laying groundwork for vertical‑lateral coupling control.
The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.
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