Lateral Control of Autonomous Vehicles with Data Dropout via an Enhanced Data-driven Model-free Adaptive Control Algorithm

Abstract
Figure/Table
References
Related Citation (7)

Abstract Addressing the lateral path tracking control issue of autonomous vehicles during data dropout, an improved model-free adaptive control system with data compensation (DC-EMFAC) is introduced. First, the method introduces a dynamic linearization technique with a time-varying factor pseudo gradient (PG) to linearize the dynamic process of an autonomous vehicle, and then designs a model-free adaptive controller. Moreover, addressing the issue of data dropout in the actual system, this paper employs an estimation algorithm to estimate the data loss at the present time based on the system’s input and output (I/O) from the past and PG. The advantage of the DC-EMFAC is that the controller design process is based on the I/O data of the controlled object, without the need for an accurate mathematical model. The effectiveness of the proposed algorithm is verified through a series of simulations on the Panosim platform.

Key words： automotive engineering autonomous vehicle model-free adaptive control (MFAC) data dropout data compensation data driven control

Received: 15 March 2023

Fund:This paper is supported by the Beijing Nature Fund (412035 and 4222045), the China Nature Fund (62173002), the North China Institute of Science and Technology Scientific Research Fund, and the North China Institute of Science and Technology Talent Training Project. This project is a special fund of the General Scientific Research Program (KM201911232015) of the Beijing Municipal Education Commission. R&D Program of the Beijing Municipal Education Commission (KM202310009010 and KM202210009011).

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Shi-da
	YAN Yu-hao
	JI Hong-hai
	WANG Li

Cite this article:

LIU Shi-da,YAN Yu-hao,JI Hong-hai, et al. Lateral Control of Autonomous Vehicles with Data Dropout via an Enhanced Data-driven Model-free Adaptive Control Algorithm[J]. Journal of Highway and Transportation Research and Development, 2024, 18(1): 38-45.

URL:

http://journal10.magtechjournal.com/Jwk_gljtkj_en/EN/ OR http://journal10.magtechjournal.com/Jwk_gljtkj_en/EN/Y2024/V18/I1/38

[1] SHEN D, ZHANG C, XU Y. Two Updating Schemes of Iterative Learning Control for Networked Control Systems with Random Data Dropouts[J]. Information Sciences, 2017, 381:352-370.
[2] GE C, YANG L, ZHAO Z W, et al. Stochastic Switched Sampled-Data Control for Uncertain Fuzzy Systems with Packet Dropout[J]. International Journal of Fuzzy Systems, 2021, 23(1):145-157.
[3] WANG R R, JING H, HU C, et al. Robust H-infinity Path Following Control for Autonomous Ground Vehicles With Delay and Data Dropout[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7):2042-2050.
[4] LIU S D, HOU Z S, TIAN T T, et al. A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11):3444-3457.
[5] HOU Z S, BU X H. Model Free Adaptive Control with Data Dropouts[J]. Expert Systems with Applications, 2011, 38(8):10709-10717.
[6] WANG Q, JIN S T, HOU Z S. Compensation-Based Cooperative MFAILC for Multiple Subway Trains Under Asynchronous Data Dropouts[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12):23750-23760.
[7] LUCIANI S, BONFITTO A, AMATI N, et al. Model Predictive Control for Comfort Optimization in Assisted and Driverless Vehicles[J]. Advances in Mechanical Engineering, 2020, 12(11):1-14.
[8] ZHANG W. A Robust Lateral Tracking Control Strategy for Autonomous Driving Vehicles[J]. Mechanical Systems and Signal Processing, 2021, 150:107238.
[9] LIU S, HOU Z, TIAN T, et al. Path Tracking Control of a Self-Driving Wheel Excavator via an Enhanced Data-driven Model-Free Adaptive Control Approach[J]. Iet Control Theory and Applications, 2020, 14(2):220-232.
[10] FALCONE P, BORRELLI F, ASGARI J, et al. Predictive Active Steering Control for Autonomous Vehicle Systems[J]. IEEE Transactions on Control Systems Technology, 2007, 15(3):566-580.
[11] HOU Z S, JIN S T. A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems[J]. IEEE Transactions on Control Systems Technology, 2011, 19(6):1549-1558.
[12] LIN N, CHI R H, HUANG B A. Event-Triggered Model-Free Adaptive Control[J]. IEEE Transactions on Systems Man Cybernetics-Systems, 2021, 51(6):3358-3369.
[13] HOU Z S, WANG Z. From Model-based Control to Data-Driven Control:Survey, Classification and Perspective[J]. Information Sciences, 2013, 235:3-35.
[14] LI X Q, XU Z G. Pattern-Moving-Based Partial Form Dynamic Linearization Model Free Adaptive Control for a Class of Nonlinear Systems[J]. Actuators, 2021, 10:223-243.
[15] BU X H, YU F S, HOU Z S, et al. Model-Free Adaptive Control Algorithm with Data Dropout Compensation[J]. Mathematical Problems in Engineering,2012,2012(1):1-14.
[16] ZHU B, LIU Z P, ZHAO J, et al. Driver Behavior Characteristics Identification Strategies Based on Bionic Intelligent Algorithms[J]. IEEE Transactions on Human-Machine Systems, 2018, 48(6):572-581.
[17] PAN F, BAO H. Preceding Vehicle Following Algorithm with Human Driving Characteristics[J]. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 2021, 235(7):1825-1834.