Tube-Based Robust Model Predictive Control for Tracking Control of Autonomous Articulated Vehicles

Jeong, Dasol; Choi, Seibum B.

doi:10.1109/tiv.2023.3320795

Cited by 1 publication

(1 citation statement)

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“…Nevertheless, application to diverse driving situations is challenging, due to the utilization of a fuzzy controller with a complex activation function to determine the appropriate amount of rear wheel steering based on driving conditions. A path tracking controller for articulated vehicles with a front steering axle was proposed using a tube-based MPC [20]. This controller is based on error dynamics using a bicycle model and a planned path to determine the plant model for MPC.…”

Section: Introductionmentioning

confidence: 99%

A Tube-Based Model Predictive Control for Path Tracking of Autonomous Articulated Vehicle

Lee,

Jeong

2024

Actuators

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This paper presents tube-based Model Predictive Control (MPC) for the path and velocity tracking of an autonomous articulated vehicle. The target platform of this study is an autonomous articulated vehicle with a non-steerable axle. Consequently, the articulation angle and wheel torque input are determined by the tube-based MPC. The proposed MPC aims to achieve two objectives: minimizing path tracking error and enhancing robustness to disturbances. Furthermore, the lateral stability of the autonomous articulated vehicle is considered to reflect its dynamic characteristics. The vehicle model for the MPC is formulated using local linearization to minimize modeling errors. The reference state is determined using a virtual controller based on the linear quadratic regulator to provide the optimal reference for the MPC solver. The proposed algorithm was evaluated through a simulation study with base algorithms under noise injection into the sensor signal. Simulation results demonstrate that the proposed algorithm achieved the smallest path tracking error, compared to the base algorithms. Additionally, the proposed algorithm demonstrated robustness to external noise for multiple signals.

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Section: Introductionmentioning

confidence: 99%