Sumo和模型预测控制结合的完整代码
时间: 2023-12-07 14:04:46 浏览: 66
以下是一个基于Sumo和模型预测控制(MPC)结合的交通流控制的完整代码示例:
```python
import traci
import numpy as np
from casadi import *
from collections import deque
# 定义模型预测控制的参数
N = 10
T = 1
L = 100
v_max = 10
a_max = 2
j_max = 3
# 定义交通流控制的参数
MAX_SPEED = 13.9
MIN_SPEED = 0
ACCEL = 2.5
DECEL = 4.5
DISTANCE = 150
# 定义模型预测控制的变量
x = MX.sym('x', N)
v = MX.sym('v', N)
a = MX.sym('a', N)
# 定义目标函数
J = 0
for i in range(N-1):
J += (v[i+1]-v[i])**2 + a[i]**2
# 定义约束
g = []
for i in range(N-1):
g.append(x[i+1]-x[i]-v[i]*T)
g.append(v[i+1]-v[i]-a[i]*T)
g.append(a[i+1]-a[i]-j_max*T)
g.append(x[0])
g.append(v[0])
g.append(a[0])
g.append(x[N-1]-L)
g.append(v[N-1])
# 构建NLP问题
nlp = {'x':vertcat(x,v,a), 'f':J, 'g':vertcat(*g)}
solver = nlpsol('solver', 'ipopt', nlp)
# 定义交通流控制的变量
vehicles = []
positions = deque(maxlen=1000)
# 初始化SUMO
sumoBinary = "sumo"
sumoCmd = [sumoBinary, "-c", "sumo_config.sumocfg"]
traci.start(sumoCmd)
# 开始交通流控制
while traci.simulation.getMinExpectedNumber() > 0:
# 获取车辆信息
for veh_id in traci.vehicle.getIDList():
if veh_id not in vehicles:
vehicles.append(veh_id)
positions.append([traci.vehicle.getPosition(veh_id)[0] for veh_id in vehicles])
# 构建模型预测控制问题
x0 = positions[-1]
v0 = [traci.vehicle.getSpeed(veh_id) for veh_id in vehicles]
a0 = [0]*len(vehicles)
lbx = [-inf]*N + [MIN_SPEED]*N + [-a_max]*N
ubx = [inf]*N + [MAX_SPEED]*N + [a_max]*N
lbg = [0]*(3*(N-1)) + x0 + v0 + [0]
ubg = [0]*(3*(N-1)) + x0 + v0 + [0]
sol = solver(x0=vertcat(x0,v0,a0), lbx=lbx, ubx=ubx, lbg=lbg, ubg=ubg)
x_opt = sol['x'][:N]
v_opt = sol['x'][N:2*N]
a_opt = sol['x'][2*N:]
# 更新车辆速度和位置
for i in range(len(vehicles)):
traci.vehicle.setSpeed(vehicles[i], v_opt[i])
traci.vehicle.moveTo(vehicles[i], x_opt[i])
# 更新车辆列表
for veh_id in vehicles:
if traci.vehicle.getSpeed(veh_id) < MIN_SPEED:
vehicles.remove(veh_id)
# 控制车辆的加速度和减速度
for veh_id in vehicles:
front_vehicle = traci.vehicle.getLeader(veh_id)
if front_vehicle is None:
traci.vehicle.setAcceleration(veh_id, ACCEL)
else:
distance = traci.vehicle.getDistance(veh_id, front_vehicle)
if distance > DISTANCE:
traci.vehicle.setAcceleration(veh_id, ACCEL)
elif distance < 2:
traci.vehicle.setAcceleration(veh_id, -DECEL)
else:
front_vehicle_speed = traci.vehicle.getSpeed(front_vehicle)
if front_vehicle_speed < traci.vehicle.getSpeed(veh_id):
traci.vehicle.setAcceleration(veh_id, -DECEL)
else:
traci.vehicle.setAcceleration(veh_id, ACCEL)
# 更新SUMO
traci.simulationStep()
# 结束SUMO
traci.close()
```
此代码将每辆车视为一个模型预测控制问题的变量,并将其作为NLPSOL的输入。每次迭代,它将计算每辆车的最佳速度和加速度,并将其设置为SUMO的速度和加速度。此外,它还将检查前方车辆的距离,并相应地调整每辆车的加速度和减速度,以确保交通流的平稳和安全。