Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 11, Pages 3444-3457Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2892327
Keywords
Autonomous automobiles; Automobiles; Control systems; Process control; Adaptation models; Mathematical model; Data models; Autonomous car; dual successive projection (DuSP); lateral path tracking control; model-free adaptive control (MFAC)
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Funding
- National Natural Science Foundation of China [61433002, 61833001, 61703019]
- Beijing Natural Science Foundation [L161007]
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In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car Ruilong for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016.
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