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Model unknown multi-agent consistency control method based on reinforcement learning

A reinforcement learning, multi-agent technology, applied in the field of intelligence, to achieve the effect of distributed consistency control

Active Publication Date: 2021-06-11
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

By establishing a model-state-based multi-agent input structure and designing a corresponding reinforcement learning algorithm to solve the HJB equation, the multi-agent optimality is solved when the leader model is unknown and the follower state is unmeasurable. Feedback Control Problems and Optimal Controller Design Problems

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  • Model unknown multi-agent consistency control method based on reinforcement learning
  • Model unknown multi-agent consistency control method based on reinforcement learning
  • Model unknown multi-agent consistency control method based on reinforcement learning

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Embodiment Construction

[0207] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0208] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a model unknown multi-agent consistency control method based on reinforcement learning, and belongs to the field of intellectualization. According to the method, the scheme adopted when an adaptive distributed observer is designed comprises three steps; firstly, the adaptive distributed observer is designed to estimate the states of a system matrix and a leader system; secondly, after the adaptive distributed observer is designed, a method for calculating the equation solution of the observer on line is provided; and thirdly, in order to eliminate extremely few extreme conditions, under the condition that each follower is assumed not to know a leader system matrix, adaptive state feedback and adaptive measurement output feedback control are integrated to solve the problem of distributed consistency output adjustment of the system. According to the estimated state, a method based on reinforcement learning is adopted to design a controller, an optimal solution is obtained through an iteration method, and optimal control over the multi-agent system is achieved.

Description

technical field [0001] The invention belongs to the field of intelligence, and relates to a model-unknown multi-agent consistency control method based on reinforcement learning. Background technique [0002] The research on the consensus control problem of multi-agent systems can be traced back to the 1980s, and the research on related multi-agent technology started from the study of mobile robots. In the past fifteen years, the research field of consensus control of multi-agent systems has developed rapidly, and many new systems have been proposed in fields ranging from military operations to mobile sensor networks, commercial highways, air transportation, and disaster relief. . However, with the constraints of control quality, the distributed optimal consistency problem has always been a major challenge in the field of control today. The distributed consistency of the multi-agent system not only needs to meet the consistency of the behavior of each agent, but also needs ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042Y02P90/02
Inventor 陈刚林卓龙
Owner CHONGQING UNIV
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