Z. Yan, L. Han, X. Li, X. Dong, Q. Li and Z. Ren, “Event-Triggered Time-Varying Formation Control for Discrete-Time Multi-Agent Systems with Communication Delays,” 2020 Chinese Automation Congress (CAC), 2020, pp. 6707-6711, doi: 10.1109/CAC51589.2020.9326758.

I. Introduction

II. Preliminaries

A. Graph theory and notations

W = [ w i j ] ∈ R N × N W = [w_{ij}] \in \R^{N \times N} W=[wij]RN×N is adjacency matrix.

S S S is in-degree matrix.

Laplacian L = S − W L = S - W L=SW

B. Problem description

x i ( k + 1 ) = x i ( k ) + v i ( k ) T v i ( k + 1 ) = v i ( k ) + u i ( k ) T (1) \begin{aligned} x_i(k+1) &= x_i(k) + v_i(k) T \\ v_i(k+1) &= v_i(k) + u_i(k) T \\ \tag{1} \end{aligned} xi(k+1)vi(k+1)=xi(k)+vi(k)T=vi(k)+ui(k)T(1)

III. Main results

A. Event-triggered control protocol

u i ( k ) = K 2 ∑ j ∈ N i w i j [ A k − k m i ( Ψ i ( k m i − τ ) − h i ( k m i − τ ) ) − A k − k m i ( Ψ j ( k m j − τ ) − h j ( k m j − τ ) ) ] + [ h i v ( k + 1 ) − h i v ( k ) ] / T + K 1 ( Ψ i ( k ) − h i ( k ) ) (7) \begin{aligned} u_i(k) &= K_2 \sum_{j\in N_i} w_{ij} [A^{k - k_m^i} (\varPsi_i(k_m^i - \tau) - h_i(k_m^i - \tau)) - A^{k - k_m^i} (\varPsi_j(k_m^j - \tau) - h_j(k_m^j - \tau))] \\ &+ [h_{iv} (k+1) - h_{iv}(k)] / T \\ &+ K_1 (\varPsi_i(k) - h_i(k)) \tag{7} \end{aligned} ui(k)=K2jNiwij[Akkmi(Ψi(kmiτ)hi(kmiτ))Akkmi(Ψj(kmjτ)hj(kmjτ))]+[hiv(k+1)hiv(k)]/T+K1(Ψi(k)hi(k))(7)

先简化一下
u i ( k ) = K 2 ∑ j ∈ N i w i j [ A ( Ψ i − h i ) − A ( Ψ j − h j ) ] + [ h i v ( k + 1 ) − h i v ( k ) ] / T + K 1 ( Ψ i ( k ) − h i ( k ) ) (7) \begin{aligned} u_i(k) &= K_2 \sum_{j\in N_i} w_{ij} [A (\varPsi_i - h_i) - A (\varPsi_j - h_j)] \\ &+ [h_{iv} (k+1) - h_{iv}(k)] / T \\ &+ K_1 (\varPsi_i(k) - h_i(k)) \tag{7} \end{aligned} ui(k)=K2jNiwij[A(Ψihi)A(Ψjhj)]+[hiv(k+1)hiv(k)]/T+K1(Ψi(k)hi(k))(7)

本质上还是个普通的分布式协议,加上了个期望速度补偿项,还有个啥暂时还不知道。

写了下程序,明白了,回来再补充下,最后一项就是自身与期望编队的误差。


接下来看一下事件触发机制。

f i ( k , e i ( k ) ) = ∥ e i ( k ) ∥ − c α k (8) f_i(k, e_i(k)) = \| e_i(k) \| - c \alpha^k \tag{8} fi(k,ei(k))=ei(k)cαk(8)

e i ( k ) = A k − k m i ( Ψ i ( k m i − τ ) − h i ( k m i − τ ) ) − ( Ψ i ( k ) − h i ( k ) ) e_i(k) = A^{k - k_m^i} (\varPsi_i(k_m^i - \tau) - h_i(k_m^i - \tau)) - (\varPsi_i(k) - h_i(k)) ei(k)=Akkmi(Ψi(kmiτ)hi(kmiτ))(Ψi(k)hi(k))

B. Stability analysis

IV. Simulation

t = 30s 时的仿真效果

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t = 50s 时的仿真效果
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在这里插入图片描述

V. Conclusion

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