Multi-agent formation control method based on actor-critic reinforcement learning and fuzzy logic

Abstract

The present invention proposes a multi-agent formation control method based on actor-critic reinforcement learning and fuzzy logic, introduces the optimal control method into the multi-robot system pilot-follower formation control method, and utilizes the ability of the fuzzy logic system to approximate a continuous function, Solve the problem that the Hamilton-Jacobi-Bellman equation in optimal control is difficult to obtain an analytical solution; at the same time, combine the actor-critic reinforcement learning algorithm to form an actor fuzzy logic system module and a critic fuzzy logic system module, the former performs control behaviors , the latter evaluates the behavior selected by the former and feeds back the evaluation information to the former. This method can balance the control performance and resource consumption, and improve the adaptability of the multi-robot system to the environment by means of online learning.

Description

technical field [0001] The invention belongs to the field of robot formation control, and in particular relates to a multi-agent second-order linear system optimal formation control method based on actor-critic reinforcement learning and fuzzy logic. Background technique [0002] In the past ten years, due to its greater redundancy, multi-robot systems have better fault tolerance and robustness than single-robot systems, and can cooperate to complete many tasks that single robots cannot complete. In a multi-robot system, robot formation is one of the control methods for robots to perform tasks cooperatively. As one of the formation control techniques, the leader-follower method can realize the distributed control of multi-robot systems, and has the characteristics of high flexibility and easy use. Introducing the optimal control method into the multi-robot system formation control can achieve the control goal of balancing control performance and resource consumption by mini...

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Problems solved by technology

It mainly takes the second-order linear system as the research object. Firstly, the optimal control method is introduced into the leader-follower formation control method of the multi-robot system, and the ability of the fuzzy logic system to approximate the continuous function is used to solve the problem of Hamilton-Jacobi-Bell in optimal control. It is difficult to find an analytical solution to the Mann equation; secondly, combined with the actor-critic reinforcement learning algorithm, an actor fuzzy logic system module and a critic fuzzy logic system module are formed, the former executes the control behavior, and the latter evaluates the behavior selected by the former And feedback the evaluation information to the former; Finally, the Bellman residual error is minimized by the gradient descent method, and the parameter vector update law of the critic fuzzy logic system module and actor fuzzy logic system module is designed

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