Tube-Based Cooperative Robust Control for Distributed Drive Electric Vehicles Suffering Actuator Faults
To effectively assist drivers to track the desired path and maintain vehicle stability in the event of different actuator failures, this article proposes a tube-based cooperative robust controller for distributed drive electric vehicles (DDEVs). A composite model, including 3-DoF vehicle dynamic model equipped with five actuators and vehicle kinematics, is first established for the controller design. The five actuators, namely, four in-wheel motors and a front-wheel steering motor, are treated as five agents to work cooperatively to achieve a better overall performance of the vehicle in the fault-tolerant control (FTC) process. Furthermore, the interaction among them is modeled by the distributed model predictive control (DMPC) scheme. Then, through analyzing phase plane, the relative weight of each agent is dynamically regulated according to the stability boundaries to strike a balance between path tracking and vehicle stability under different failure scenarios. Finally, integrated with the DMPC framework, a tube-based robust model predictive control (RMPC) method is utilized to confine the system state to a safe and stable range when facing the disturbance that the failure brings. Numerical simulations of three serious failure cases are conducted to verify the effectiveness of the proposed controller, proving that it can deal with the path tracking and stability performance problem of the failed vehicle better and more reasonably.
Authors
- Wanzhong Zhao (ORCID: https://orcid.org/0009-0008-8651-6011)
- Xiang Chen (ORCID: https://orcid.org/0000-0001-8254-8907)
- Chunyan Wang (ORCID: https://orcid.org/0000-0002-3913-8192)
- Rui Jiang (ORCID: https://orcid.org/0009-0000-4743-2749)
Institutions
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- IEEE Transactions on Transportation Electrification
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1109/tte.2024.3442572
- Citations
- 7
- Primary Topic
- Advanced Control Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 2.30
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Jiangsu Provincial Key Research and Development Program