一、報告題目：

 非線性多智能體系統(tǒng)的完全分布式最優(yōu)一致性

二、報 告 人：向崢嶸 教授

三、報告時間：2024年12月7日(星期六)上午9:30

四、報告地點：6A301

五、主辦單位： 西華大學(xué)電氣與電子信息學(xué)院

六、報告人簡介：

向崢嶸，南京理工大學(xué)自動化學(xué)院教授，博士生導(dǎo)師。1998年12月于南京理工大學(xué)控制理論與控制工程專業(yè)獲得博士學(xué)位。現(xiàn)任中國自動化學(xué)會委員、中國人工智能學(xué)會委員。獲得國防科技進步二等獎，主持多項國家自然科學(xué)基金、重點研發(fā)等項目。擔(dān)任多個國家及部級項目評審專家和國內(nèi)外重要Top期刊的審稿人。目前主要從事非線性系統(tǒng)、切換控制系統(tǒng)、多智能體系統(tǒng)等方面的理論及應(yīng)用研究。

七、報告內(nèi)容簡介：

In this talk, an optimal consensus protocol is proposed for a class of leaderless multi-agent systems. Any global information, including the eigenvalues of the Laplacian matrix, is unavailable in the control scheme development. The reference trajectory is designed for each agent, and the corresponding performance function, which reflects the off-track error evolution and control cost, is proposed. The sufficient condition for the synchronization of reference trajectories, which does not rely on the topology dwell time, is established by constructing an appropriate current topology independent Lyapunov function. Due to the fact that the nonlinear function in the system dynamical equation of each agent is unknown, an equation termed integral reinforcement learning equation is provided, and it is strictly proven that the provided IRL equation is equivalent to the given Hamilton–Jacobi–Bellman equation. The model-free optimal feedback control law is then derived based on the IRL technique. In the implementation of the developed control scheme, the neural network approximation tool is adopted, and the scheme is applied to a numerical system to show its effectiveness.

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