期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 83, 期 -, 页码 61-76出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2017.07.011
关键词
Stochastic optimal control; ACC; CACC; Rolling horizon; Separation principle; Linearly constrained linear quadratic; Gaussian
资金
- National Science Foundation - United States [CMMI 1536599]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1536599] Funding Source: National Science Foundation
This paper presents a rolling horizon stochastic optimal control strategy for both Adaptive Cruise Control and Cooperative Adaptive Cruise Control under uncertainty based on the constant time gap policy. Specifically, uncertainties that can arise in vehicle control systems and vehicle sensor measurements are represented as normally-distributed disturbances to state and measurement equations in a state-space formulation. Then, acceleration sequence of a controlled vehicle is determined by optimizing an objective function that captures control efficiency and driving comfort over a predictive horizon, constrained by bounded acceleration deceleration and collision protection. The optimization problem is formulated as a linearly constrained linear quadratic Gaussian problem and solved using a separation principle, Lagrangian relaxation, and Kalman filter. A sensitivity analysis and a scenario-based analysis via simulations demonstrate that the proposed control strategy can generate smoother vehicle control and perform better than a deterministic feedback controller, particularly under small system disturbances and large measurement disturbances. Published by Elsevier Ltd.
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