Article
Automation & Control Systems
Heng Wang, Wenwen Song, Yongyu Liang, Qing Li, Deyu Liang
Summary: This paper investigates the problem of path tracking for Autonomous Ground Vehicles (AGVs) in the presence of sideslip angles. An observer is designed to estimate both the sideslip angle and the vehicle yaw rate, and an observer-based controller is established to ensure stability and accurate path following. The nonlinear vehicle dynamics model is reformulated as a Linear Parameter Varying (LPV) system, and a finite frequency H-infinity criteria is satisfied to effectively attenuate disturbances. Parameter-dependent gain matrices are calculated by solving a convex optimization problem. Simulation results demonstrate the effectiveness of the proposed method.
Article
Automation & Control Systems
Heng Wang, Tengfei Zhang, Xiaoyu Zhang, Qing Li
Summary: This paper investigates the problem of path tracking control for Autonomous Ground Vehicles (AGVs), considering input saturation, system nonlinearities, and uncertainties. Firstly, a linear parameter varying (LPV) model is formulated for the nonlinear path tracking system, taking into account the variation of vehicle velocity. Secondly, an observer-based control strategy is proposed to mitigate the effects of noise on lateral offset and heading angle measurements, using a finite frequency H-infinity index to tackle the derivative of desired heading angle's impact on path tracking error. Thirdly, sufficient conditions are derived to guarantee robust H-infinity performance of the path tracking system, with the calculation of observer and controller gains converted into a convex optimization problem. Finally, simulation examples verify the advantages of the proposed control method in this study.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Engineering, Electrical & Electronic
Yuhang Zhang, Weida Wang, Wei Wang, Chao Yang, Yuanbo Zhang
Summary: This paper proposes an adaptive constrained path following control scheme considering the influence of parametric uncertainty for autonomous electric vehicles. By utilizing adaptive feedback control law, designing a constraint function, and proving the closed-loop stability, the proposed scheme achieves effective driving safety and accurate path following.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Kunwu Zhang, Qi Sun, Yang Shi
Summary: An adaptive learning model predictive control (ALMPC) scheme is proposed for trajectory tracking of autonomous ground vehicles (AGVs), with a focus on estimating unknown system parameters and introducing robustness constraints for handling uncertainties. The proposed method shows improved prediction accuracy and reduced conservatism compared to robust MPC methods, with theoretical analysis confirming recursive feasibility and input-to-state stability of the closed-loop system.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Mechanical
Ying Tian, Qiangqiang Yao, Peng Hang, Shengyuan Wang
Summary: An adaptive path tracking control strategy is proposed in this study to improve the path tracking accuracy under high-speed and large-curvature conditions by coordinating active front wheel steering and direct yaw moment. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the prediction model. The adaptive strategy based on fuzzy rules is applied to adjust the weight coefficients in the cost function to adaptively adjust the priorities of path tracking accuracy and vehicle stability.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Engineering, Marine
Qiang Guo, Xianku Zhang, Yao Meng, Daocheng Ma
Summary: This paper proposes a fixed-time adaptive path-following control scheme for autonomous surface vehicles to address accuracy and safety considerations in actual navigation practice. The proposed scheme effectively follows a reference path while satisfying output performance and feasibility constraints.
Article
Computer Science, Artificial Intelligence
Xingyu Zhou, Zejiang Wang, Heran Shen, Junmin Wang
Summary: This paper proposes a novel control architecture to tackle the backlash issue in ground vehicle path-tracking. The dynamics of the steering system's backlash are compensated using an adaptive inverse controller and robustified with sigma modification. Hardware experiments demonstrate the superiority of the proposed solution.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2022)
Article
Engineering, Civil
Chuan Hu, Yimin Chen, Junmin Wang
Summary: This study proposes a fuzzy-observer-based control method using a Takagi-Sugeno vehicle dynamic model to address path-tracking control issues of autonomous vehicles when GPS is temporarily unavailable, demonstrating its effectiveness through high-fidelity simulations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Chemistry, Analytical
Xianzhi Tang, Longfei Shi, Bo Wang, Anqi Cheng
Summary: This paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature. The system consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. The control strategy was verified on a simulation platform and an autonomous vehicle test platform, showing better tracking adaptation capability and improved tracking accuracy.
Article
Engineering, Mechanical
Kai Jiang, Chuan Hu, Fengjun Yan
Summary: This paper investigates the path-following problems in autonomous ground vehicles (AGVs) using predictive control and neural network modeling. A data-driven approach based on deep neural networks is proposed to handle system identification tasks where the AGVs model is difficult to construct accurately. To balance control tractability and model accuracy, input convex neural networks (ICNNs) are developed to describe AGVs dynamics. A periodically online learning algorithm is also designed to adapt to different road conditions and disturbances. Two driving simulations are conducted to demonstrate the effectiveness of the proposed techniques.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2022)
Article
Engineering, Marine
Yufei Xu, Lei Wan, Ziyang Zhang, Guofang Chen
Summary: This article investigates the path-following control problem for autonomous underwater vehicles (AUVs) subject to external disturbances and unknown model parameters. It proposes an event-triggered model-free adaptive control (ETMFAC) method with a practical experimental platform, employing a line-of-sight (LOS) guidance law and an improved model-free adaptive control method. The stability of the control system is proved by theoretical analysis, and the superiority of the proposed method is verified through numerical simulations and practical experiments.
Article
Engineering, Electrical & Electronic
Ahmet Canberk Manav, Ismail Lazoglu, Eren Aydemir
Summary: This article presents an enhanced path-following control framework for autonomous semi-trailer docking. The proposed system introduces adaptive controllers and gain scheduling to improve the robustness and performance of path-following tasks in docking. The evaluation results, obtained through simulation and physical testing, show improvements in terms of control problem formulation and acceptable path-following errors.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Civil
Naman Patel, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami
Summary: This study introduces an automated, physically-realizable, dynamic adversarial attack aimed at compromising an end-to-end trained DNN controlled autonomous vehicle. The attack, initiated by a billboard displaying videos, leads the vehicle to track an adversary customized trajectory, showing high effectiveness.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Environmental Sciences
Heba Kurdi, Shaden Almuhalhel, Hebah Elgibreen, Hajar Qahmash, Bayan Albatati, Lubna Al-Salem, Ghada Almoaiqel
Summary: With the rise of autonomous vehicles and artificial intelligence, path planning has become a key research focus area. A new algorithm called Tide Path Planning (TPP) inspired by natural tide phenomenon was introduced in this paper, showing superior performance compared to other existing path planning algorithms.
Article
Engineering, Civil
Yixiao Liang, Yinong Li, Amir Khajepour, Yanjun Huang, Yechen Qin, Ling Zheng
Summary: In this research, a novel scheme is proposed to integrate local motion planning and control for autonomous vehicles. The local motion planning is transformed into the longitudinal control problem and a lateral MPC controller is designed to track the global path and execute the local motion commands. Comprehensive case studies demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
