CO-DMPC-BASED CHASSIS MULTI-AGENT SYSTEM (MAS) COOPERATIVE CONTROL METHOD FOR AUTONOMOUS VEHICLES, CONTROLLER, AND STORAGE MEDIUM
개요
발명자
Yingfeng CAI; Yuxing LI; Yubo LIAN; Zhaozhi DONG; Xiaoqiang SUN; Long CHEN
IPC 분류
CPC 분류
The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.
원문 (중국어)
The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.