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.
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).
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.
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