Multiple intelligent vehicle formation method based on distributed finite-time state observers

Abstract

The invention relates to a multiple-intelligent-vehicle formation method based on distributed finite-time state observers. The multiple-intelligent-vehicle formation method comprises the steps of: S1,describing kinematic features of intelligent vehicles according to an intelligent vehicle kinematics model; S2, constructing a master-slave architecture of multiple intelligent vehicles, and establishing a communication network structure among the multiple intelligent vehicles based on a topological structure of a directed spanning tree; S3, designing the distributed finite-time state observers that does not depend on global coordinate information, so as to obtain observed values; S4, and inputting the observed values into a multiple-intelligent-vehicle distributed formation controller, so asto realize automatic formation of the multiple intelligent vehicles. Compared with the prior art, the multiple-intelligent-vehicle formation method obtains relative positions among the vehicles by means of a laser radar and a gyroscope, controls the error within a millimeter range, ensures that the observed values are closer to actual values, guarantees the convergence of the observer through constructing the communication network structure by using the directed spanning tree, and finally can effectively improve the precision and safety of intelligent vehicle formation.

Description

technical field [0001] The invention relates to the field of coordinated control of multiple intelligent vehicles, in particular to a multi-intelligent vehicle formation method based on a distributed finite-time state observer. Background technique [0002] With the continuous deepening of research on intelligent driving vehicles, the limitations of the performance of a single intelligent vehicle (such as load capacity, work efficiency, detection field of view, etc.) Overall system performance, compared with a single agent, a multi-agent system has irreplaceable advantages: [0003] For complex tasks and environments, the multi-agent system can decompose complex tasks into multiple simple sub-tasks, and then use multiple agents to work simultaneously in different areas, thereby improving work efficiency; [0004] Multiple agents are distributed in an environment with a large area, each perceives its surrounding environment, and shares the information perceived by each agent...

AI Technical Summary

Problems solved by technology

[0011] In the existing distributed finite-time state observers, the slave often directly observes the global position information of the master, but since the global position information provided by the master itself is generally obtained by GPS, its accuracy can only reach the meter level. The positioning error of the upper-slave itself and the influence of environmental factors (such as tunnels and high-rise buildings) on the positioning accuracy will lead to poor observation accuracy of the existing distributed finite-time state observers. When applied to the distributed formation control of multi-intelligent vehicles, it may cause vehicle collisions and fail to achieve the expected formation goal

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