Research on Model Predictive Control-based Trajectory Tracking for Unmanned Vehicles

Yuan, Shoutong; Zhao, Pengchao; Hu, Xin

doi:10.1109/iccre.2019.8724158

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…Since the center of mass slip angle has a greater impact on vehicle stability, a reasonable design of the center of mass slip angle constraint can improve the safety of MPC tracking control. In this paper, according to the enterprise standard, the center of mass slip angle β of the smart vehicle is designed as 18 :…”

Section: Improved Mpc Trajectory Tracking Control Methodsmentioning

confidence: 99%

Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Han¹,

Huang³

et al. 2022

Advances in Mechanical Engineering

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In order to improve the accuracy of trajectory tracking of in-wheel motor electric vehicles, a preview time adaptive trajectory tracking method based on iterative algorithm and fuzzy control is proposed. Firstly, based on the vehicle’s three-degree-of-freedom model, the vehicle is controlled to track trajectory based on model predictive control (MPC). The preview step size and sampling period of MPC are adjusted by iterative function and fuzzy controller, respectively. Then, In order to optimize MPC active steering control, a differential torque controller is established to realize the trajectory tracking control of differential torque steering. Finally, Carsim/Simulink co-simulation analysis and real vehicle verification are done. The simulation results show that the controller can complete the trajectory tracking control of the in-wheel motor intelligent vehicle, and the stability and steering performance are good. The controller has good robustness and adaptability according to road adhesion conditions and vehicle speed changes. At the same time, the trajectory tracking accuracy of the MPC controller is better than sliding mode variable structure control (SMC). The real vehicle verification results show that when the real vehicle tracking under different speeds, the adaptive preview time controller designed in this paper has good trajectory tracking performance and stability.

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Section: Improved Mpc Trajectory Tracking Control Methodsmentioning

confidence: 99%

Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Han¹,

Huang³

et al. 2022

Advances in Mechanical Engineering

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“…Efficient sensor development relying on ultrasonic technology [27], Radio Detection and Ranging (RADARs) [28], Light Detection and Ranging (LIDARs) [29]- [30] and complex image processing algorithms [4] as well as control systems and algorithms [31]- [32], have been developed. This enables the vehicles within the near field IAV, to announce and plan their trajectory in coordination with other cars [33]- [34] and coordinate their collective motion [28], [35]- [36] with other cars while traveling the form of platoons [37]- [38] with efficient self-driving algorithms; such as Robust Cruise Control [39]- [40].…”

Section: B Control Strategies and Sensor Development For The Iavsmentioning

confidence: 99%

Why is Internet of Autonomous Vehicles not as Plug and Play as We Think ? Lessons to Be Learnt From Present Internet and Future Directions

et al. 2020

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The recent race for autonomous or 'driverless' vehicles, has spawned a lot of research in the area of Internet of Autonomous Vehicles (IAVs). With the advent of the latest technology fueled by Artificial Intelligence and Machine Learning, Autonomous Vehicles (AVs) can now determine the best possible route to a destination based on the current traffic situation and take dynamic driving decisions accordingly, while preventing accidents. Field trials for single autonomous vehicles have been largely successful. However, as more autonomous vehicles will be added to the intelligent transport networks, current research is now centered around their synergistic coexistence in the offering of network-centric and user-centric services. This development is governed by borrowing several concepts from the legacy Internet to address the problems of IAVs. In this paper, we present an extensive overview of the research challenges in the IAVs. Moreover, our contributions in this paper are that (i) We show how the network-oriented cooperative client-server model will give way to a more unorthodox and 'selfish' decentralized and peer-to-peer (P2P) model, for example in the offering of navigation services on the IAV. (ii) We discuss how centralized architecture will give way to more distributed architectures for real-time information propagation over the IAV. (iii) We discuss how network-centric policies will begin to shift to user-centric under more beneficial revenue models by offering network-assisted quality of service (QoS) provisioning. (iv) We discuss in detail how vehicle traffic grooming in the IAV would present as much of a challenge as in the legacy Internet. (v) We discuss the disruptive role of value-added services on the IAV, and (iv) Finally, we discuss the problem of cyber threats in the IAV just as in the legacy Internet.

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“…When a reference state is introduced, the changing trend of the reference path can be added to MPC. The robust [16] and stochastic [17] model predictive controls are the main methods of dealing with uncertain systems [18]. The intelligent control achieves a better control based on self-learning, self-adaptation, and self-organization.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.

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Research on Model Predictive Control-based Trajectory Tracking for Unmanned Vehicles

Cited by 17 publications

References 5 publications

Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

Why is Internet of Autonomous Vehicles not as Plug and Play as We Think ? Lessons to Be Learnt From Present Internet and Future Directions

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

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