【Paper】2018_Nonlinear Consensus-Based Connected Vehicle Platoon Control Incorporating Car-Following

[Y. Li, C. Tang, S. Peeta and Y. Wang, “Nonlinear Consensus-Based Connected Vehicle Platoon Control Incorporating Car-Following Interactions and Heterogeneous Time Delays,” in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 6, pp. 2209-2219, June 2019, doi: 10.1109/TITS.2018.2865546.]

文章目录

2 Preliminaries and Problem Statement

2.1 Graph Theory

2.2 Mathematical Preliminaries

2.3 Consensus Problem Statement

系统采用的是二阶积分器模型:
{ x ˙ i ( t ) = v i ( t ) v ˙ i ( t ) = u i ( t ) ,     i = L , 1 , 2 , ⋯   , n , (9) \left\{\begin{aligned} &\dot{x}_i(t) = v_i(t) \\ &\dot{v}_i(t) = u_i(t), ~~~ i=L,1,2,\cdots, n, \\ \end{aligned}\right.\tag{9} {​x˙i​(t)=vi​(t)v˙i​(t)=ui​(t),   i=L,1,2,⋯,n,​(9)

控制目标是保持安全距离 h c h_c hc​ 的一字型 Leader 编队控制

3 Nonlinear Consensus Algorithm

分布式非线性时滞控制算法为
u i ( t ) = ∑ j = 1 n a i , j [ α ( V i ( h i , j ( t ) ) − v i ( t ) ) + β ( v j ( t − τ i j ( t ) ) − v i ( t ) ) + γ ( x j ( t − τ i j ( t ) ) − x i ( t ) ) + v L ( t − τ i L ( t ) ) τ i j ( t ) − r i , j ) ] + k i , L [ β ( v L ( t − τ i L ( t ) ) − v i ( t ) ) + γ ( x L ( t − τ i L ( t ) ) + v L ( t − τ i L ) τ i L ( t ) − x i ( t ) − r i , L ) ] \begin{aligned} u_i(t) =& \sum_{j=1}^{n} \red{a_{i,j}} [ \green{\alpha} (V_i(h_{i,j}(t)) - v_i(t)) \\ &+ \green{\beta} (v_j(t-\tau_{ij}(t)) - v_i(t)) \\ &+ \green{\gamma} (x_j(t-\tau_{ij}(t)) - x_i(t)) \\ &+ \green{v_L} (t-\tau_{iL}(t)) \tau_{ij}(t) - r_{i,j}) ] \\ &+ \red{k_{i,L}} [\beta (v_L(t-\tau_{iL}(t)) - v_i(t)) \\ &+ \gamma (x_L(t-\tau_{iL}(t)) + v_L(t-\tau_{iL}) \tau_{iL}(t) - x_i(t) - r_{i,L})] \end{aligned} ui​(t)=​j=1∑n​ai,j​[α(Vi​(hi,j​(t))−vi​(t))+β(vj​(t−τij​(t))−vi​(t))+γ(xj​(t−τij​(t))−xi​(t))+vL​(t−τiL​(t))τij​(t)−ri,j​)]+ki,L​[β(vL​(t−τiL​(t))−vi​(t))+γ(xL​(t−τiL​(t))+vL​(t−τiL​)τiL​(t)−xi​(t)−ri,L​)]​

4 Convergence Analysis

4.1 Convergence Analysis

4.2 Convergence Speed Analysis

5 Numerical Experiments

Nodes:

  1. one leader
  2. nine follower

Three conditions:

  1. no time delays
  2. heterogeneous time delays
  3. homogeneous time delays

5.1 Simulation Setting

Sampling interval Δ t = 0.01 s \Delta t = 0.01s Δt=0.01s

The initial positions are x ( 0 ) = [ 0 10 20.5 31.5 43 55 67.5 80.5 94 108 ] T x(0) = \left[\begin{matrix} 0 & 10 & 20.5 & 31.5 & 43 & 55 & 67.5 & 80.5 & 94 & 108 \end{matrix}\right]^\text{T} x(0)=[0​10​20.5​31.5​43​55​67.5​80.5​94​108​]T m on a lane.

The initial velocities are set as = [ 7 7 7 7 7 7 7 7 7 7 ] T = \left[\begin{matrix} 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 & 7 \end{matrix}\right]^\text{T} =[7​7​7​7​7​7​7​7​7​7​]T m/s.

The heterogeneous time delays are selected as τ = [ 0 0.15 0.18 0.19 0.20 0.21 0.22 0.23 0.27 0.30 ] T \tau = \left[\begin{matrix} 0 & 0.15 & 0.18 & 0.19 & 0.20 & 0.21 & 0.22 & 0.23 & 0.27 & 0.30 \end{matrix}\right]^\text{T} τ=[0​0.15​0.18​0.19​0.20​0.21​0.22​0.23​0.27​0.30​]T.

The homogeneous time delays are set as 0.20 s 0.20s 0.20s.

The desired gaps between the followers and the leader are set as [ 45 40 35 30 25 20 15 10 5 ] T \left[\begin{matrix} 45 & 40 & 35 & 30 & 25 & 20 & 15 & 10 & 5 \end{matrix}\right]^\text{T} [45​40​35​30​25​20​15​10​5​]T.

relevant parameters: α = 3.5 s − 1 \alpha = 3.5 s^{-1} α=3.5s−1

5.2 Discussion of Results

5.3 Comparison to Existing Approaches

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