双积分器
植绒(纹理)
多智能体系统
计算机科学
积分器
惯性
控制理论(社会学)
惯性参考系
动态规划
协议(科学)
共识
控制(管理)
人工智能
算法
医学
计算机网络
材料科学
物理
替代医学
带宽(计算)
经典力学
量子力学
病理
复合材料
作者
Qi Zhang,Yang Yang,She‐Sheng Xue,Xiaoran Xie,Naibo Zhu,Zhi Li
摘要
Abstract The purpose of this article is to utilize adaptive dynamic programming to solve an optimal consensus problem for double‐integrator multiagent systems with completely unknown dynamics. In double‐integrator multiagent systems, flocking algorithms that neglect agents' inertial effect can cause unstable group behavior. Despite the fact that an inertias‐independent protocol exists, the design of its control law is decided by dynamics and inertia. However, inertia in reality is difficult to measure accurately, therefore, the control gain in the consensus protocol was solved by developing adaptive dynamic programming to enable the double‐integrator systems to ensure the consensus of the agents in the presence of entirely unknown dynamics. Firstly, we demonstrate in a typical example how flocking algorithms that ignore the inertial effect of agents can lead to unstable group behavior. And even though the protocol is independent of inertia, the control gain depends quite strongly on the inertia and dynamic of the agent. Then, to address these shortcomings, an online policy iteration‐based adaptive dynamic programming is designed to tackle the challenge of double‐integrator multiagent systems without dynamics. Finally, simulation results are shown to prove how effective the proposed approach is.
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