As a critical technology to improve vehicle safety and handling stability, chassis coordinated control technology faces challenges in multi-system coordination, multi-variable solutions, and multi-task allocation. This paper combines the advantages of decentralized and distributed architectures and proposes a scenario-driven extension Hybrid-DMPC algorithm with variable topology and distributed modeling. Firstly, a two-dimensional extension coordinate is constructed with the $\beta - \dot {\beta } $ phase plane and LTR as feature states. The correlation function is then solved to divide the vehicle states into classical domain, extension domain, and non-domain, forming a mapping relationship with the control architecture. Secondly, the distributed state space equations with state coupling and input coupling are constructed. The proposed algorithm is designed to employ decentralized, distributed, and hybrid architectures in the classical domain, non-domain, and extension domain, respectively. The correlation function determines the fusion rules for multiple control inputs in the extension domain. Thirdly, a cost-coupled weighted optimization function is designed, enabling local agents to coordinate global performance goals through iterative optimization. Finally, co-simulation and HIL test results verified that the proposed algorithm effectively coordinates multiple control objectives, achieving high-precision trajectory tracking, excellent handling stability, and anti-roll performance.
Distributed Modeling and Scenario-Driven Extension Hybrid-DMPC Coordinated Control of Autonomous Vehicle Chassis
IEEE Transactions on Intelligent Transportation Systems ; 26 , 5 ; 7186-7196
2025-05-01
6113060 byte
Article (Journal)
Electronic Resource
English
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