An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

Dongdong Yuan; Yankai Wang

doi:10.1109/DDCLS49620.2020.9275050

An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

Dongdong Yuan^*, Yankai Wang

^*Corresponding author for this work

School of Aerospace Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

In order to solve the dynamic modeling and parameter identification problems of unmanned vehicles trajectory tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the data-driven model-free adaptive control method, which does not depend on the precise dynamic model of the unmanned vehicle. The model-free adaptive control method is extended to the unmanned vehicle trajectory tracking control, and the model-free controller is designed and applied to the driverless vehicle trajectory tracking control. Aiming at the problem that the general compact form dynamic linearization model-free adaptive control (CFDL-MFAC) algorithm cannot converge in vehicle trajectory tracking control, combined with the dynamic characteristics of unmanned vehicles, an improved model-free adaptive control algorithm is proposed in this paper. The simulation results verify the effectiveness and feasibility of the algorithm. Mathematical simulation results show that the improved model-free adaptive algorithm of the designed unmanned vehicle is effective and can effectively implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the controller does not depend on the kinematics and dynamics models of the unmanned vehicle, and it has high control accuracy.

Original language	English
Title of host publication	Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020
Editors	Mingxuan Sun, Huaguang Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	996-1002
Number of pages	7
ISBN (Electronic)	9781728159225
DOIs	https://doi.org/10.1109/DDCLS49620.2020.9275050
Publication status	Published - 20 Nov 2020
Event	9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 - Liuzhou, China Duration: 20 Nov 2020 → 22 Nov 2020

Publication series

Name	Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020

Conference

Conference	9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020
Country/Territory	China
City	Liuzhou
Period	20/11/20 → 22/11/20

Keywords

Data-driven
Improved Model-free Adaptive Control Algorithm
Trajectory Tracking
Unmanned Vehicle

Access to Document

10.1109/DDCLS49620.2020.9275050

Cite this

Yuan, D., & Wang, Y. (2020). An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm. In M. Sun, & H. Zhang (Eds.), Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020 (pp. 996-1002). Article 9275050 (Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS49620.2020.9275050

Yuan, Dongdong ; Wang, Yankai. / An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm. Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020. editor / Mingxuan Sun ; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 996-1002 (Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020).

@inproceedings{8064d8d853c84e08ad1bbbc1a3f685bc,

title = "An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm",

abstract = "In order to solve the dynamic modeling and parameter identification problems of unmanned vehicles trajectory tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the data-driven model-free adaptive control method, which does not depend on the precise dynamic model of the unmanned vehicle. The model-free adaptive control method is extended to the unmanned vehicle trajectory tracking control, and the model-free controller is designed and applied to the driverless vehicle trajectory tracking control. Aiming at the problem that the general compact form dynamic linearization model-free adaptive control (CFDL-MFAC) algorithm cannot converge in vehicle trajectory tracking control, combined with the dynamic characteristics of unmanned vehicles, an improved model-free adaptive control algorithm is proposed in this paper. The simulation results verify the effectiveness and feasibility of the algorithm. Mathematical simulation results show that the improved model-free adaptive algorithm of the designed unmanned vehicle is effective and can effectively implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the controller does not depend on the kinematics and dynamics models of the unmanned vehicle, and it has high control accuracy.",

keywords = "Data-driven, Improved Model-free Adaptive Control Algorithm, Trajectory Tracking, Unmanned Vehicle",

author = "Dongdong Yuan and Yankai Wang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 ; Conference date: 20-11-2020 Through 22-11-2020",

year = "2020",

month = nov,

day = "20",

doi = "10.1109/DDCLS49620.2020.9275050",

language = "English",

series = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "996--1002",

editor = "Mingxuan Sun and Huaguang Zhang",

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address = "United States",

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Yuan, D & Wang, Y 2020, An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm. in M Sun & H Zhang (eds), Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020., 9275050, Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020, Institute of Electrical and Electronics Engineers Inc., pp. 996-1002, 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020, Liuzhou, China, 20/11/20. https://doi.org/10.1109/DDCLS49620.2020.9275050

An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm. / Yuan, Dongdong; Wang, Yankai.
Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020. ed. / Mingxuan Sun; Huaguang Zhang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 996-1002 9275050 (Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

AU - Yuan, Dongdong

AU - Wang, Yankai

PY - 2020/11/20

Y1 - 2020/11/20

N2 - In order to solve the dynamic modeling and parameter identification problems of unmanned vehicles trajectory tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the data-driven model-free adaptive control method, which does not depend on the precise dynamic model of the unmanned vehicle. The model-free adaptive control method is extended to the unmanned vehicle trajectory tracking control, and the model-free controller is designed and applied to the driverless vehicle trajectory tracking control. Aiming at the problem that the general compact form dynamic linearization model-free adaptive control (CFDL-MFAC) algorithm cannot converge in vehicle trajectory tracking control, combined with the dynamic characteristics of unmanned vehicles, an improved model-free adaptive control algorithm is proposed in this paper. The simulation results verify the effectiveness and feasibility of the algorithm. Mathematical simulation results show that the improved model-free adaptive algorithm of the designed unmanned vehicle is effective and can effectively implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the controller does not depend on the kinematics and dynamics models of the unmanned vehicle, and it has high control accuracy.

AB - In order to solve the dynamic modeling and parameter identification problems of unmanned vehicles trajectory tracking control, a mathematical model of unmanned vehicle trajectory tracking is designed based on the data-driven model-free adaptive control method, which does not depend on the precise dynamic model of the unmanned vehicle. The model-free adaptive control method is extended to the unmanned vehicle trajectory tracking control, and the model-free controller is designed and applied to the driverless vehicle trajectory tracking control. Aiming at the problem that the general compact form dynamic linearization model-free adaptive control (CFDL-MFAC) algorithm cannot converge in vehicle trajectory tracking control, combined with the dynamic characteristics of unmanned vehicles, an improved model-free adaptive control algorithm is proposed in this paper. The simulation results verify the effectiveness and feasibility of the algorithm. Mathematical simulation results show that the improved model-free adaptive algorithm of the designed unmanned vehicle is effective and can effectively implement the trajectory tracking control of the unmanned vehicle. At the same time, the design of the controller does not depend on the kinematics and dynamics models of the unmanned vehicle, and it has high control accuracy.

KW - Data-driven

KW - Improved Model-free Adaptive Control Algorithm

KW - Trajectory Tracking

KW - Unmanned Vehicle

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BT - Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020

A2 - Sun, Mingxuan

A2 - Zhang, Huaguang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020

Y2 - 20 November 2020 through 22 November 2020

ER -

Yuan D, Wang Y. An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm. In Sun M, Zhang H, editors, Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 996-1002. 9275050. (Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020). doi: 10.1109/DDCLS49620.2020.9275050

An Unmanned Vehicle Trajectory Tracking Method based on Improved Model-free Adaptive Control Algorithm

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