Year: 2025
Type: article
Abstract: Aiming at the adaptability and the ability to coordinate different control objectives of the adaptive cruise control (ACC) system in complex urban driving environments, this paper proposes a novel ACC strategy based on model predictive control (MPC) with fuzzy variable weight coefficients of multi‐objective optimization. First, according to the requirements of vehicle performance indicators under different driving conditions, the weight coefficients in the MPC framework are updated online by a fuzzy control algorithm to adjust the priorities of safety, economics, and car‐following ability. Then, considering that quadratic programming (QP) may fall into a local optimum, the particle swarm optimization (PSO) algorithm is used to solve the optimal control policy online. The effectiveness and reliability of the proposed control strategy are validated on the PreScan/MATLAB/Simulink co‐simulation platform and the driver hardware in the loop platform (DHIL) compared to other existing control strategies.
Source: Asian Journal of Control
DOI: 10.1002/asjc.3698
Authors: Huiwen Liu, Xiaohong Jiao
Institution: Yanshan University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Award: G2975879347
OpenAlex ID: W4409825144