【Paper】2020_Event-Triggered Time-Varying Formation Control for Discrete-Time Multi-Agent Systems wit
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 (CA
文章目录
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=S−W
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)=K2j∈Ni∑wij[Ak−kmi(Ψi(kmi−τ)−hi(kmi−τ))−Ak−kmi(Ψj(kmj−τ)−hj(kmj−τ))]+[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)=K2j∈Ni∑wij[A(Ψi−hi)−A(Ψj−hj)]+[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)=Ak−kmi(Ψi(kmi−τ)−hi(kmi−τ))−(Ψi(k)−hi(k))
B. Stability analysis
IV. Simulation
t = 30s 时的仿真效果
t = 50s 时的仿真效果
V. Conclusion
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