大型环岛交叉口信号控制毕业论文【附VISSIM仿真】

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大型环岛交叉口模糊控制方案设计

阐述大型环岛交通拥堵问题严重性，说明采用模糊控制的原因，介绍在各进口道设计两输入单输出模糊控制器，输入为不同相位排队车辆数，输出为入环灯绿灯延长时长。讲解建立车辆生成模型，以平均车辆延误为评价指标，借助软件仿真分析，并与定时控制方案对比验证有效性。

优化大型环岛交叉口模糊控制器的方法

分析粒子群算法、自适应粒子群算法及 ASAPSO 算法优势与关联，解释运用 ASAPSO 算法优化模糊控制隶属度函数与部分控制规则，使其更合理。设计编程思路，用仿真软件与定时控制、经典模糊控制对比分析，展示 ASAPSO 算法优化的模糊控制优势。

案例探究与应用：以某环岛为例

介绍厦门市莲坂环岛作为典型大型环岛交叉口的重要性，调查其现状，设计模糊控制器。利用软件模拟环岛交叉口定时控制、经典模糊控制和 ASAPSO 算法优化的模糊控制三种方法，选择低谷、平峰和高峰三种典型交通流分析，呈现 ASAPSO 算法优化的模糊控制在实际应用中的最优效果。

import numpy as np
import random
import math

class LMPSO:
    def __init__(self, pop_size, dim, max_iter):
        self.pop_size = pop_size
        self.dim = dim
        self.max_iter = max_iter
        self.w = 0.7
        self.c1 = 1.5
        self.c2 = 1.5
        self.pos = np.random.rand(pop_size, dim)
        self.vel = np.random.rand(pop_size, dim) * 0.1
        self.pbest = self.pos.copy()
        self.gbest = self.pos[0].copy()
        self.gbest_score = float('inf')

    def fitness(self, x):
        return np.sum((x - 0.5) ** 2)

    def update(self):
        for i in range(self.pop_size):
            score = self.fitness(self.pos[i])
            if score < self.fitness(self.pbest[i]):
                self.pbest[i] = self.pos[i]
            if score < self.gbest_score:
                self.gbest_score = score
                self.gbest = self.pos[i]
        for i in range(self.pop_size):
            r1, r2 = random.random(), random.random()
            self.vel[i] = self.w * self.vel[i] + self.c1 * r1 * (self.pbest[i] - self.pos[i]) + self.c2 * r2 * (self.gbest - self.pos[i])
            self.pos[i] += self.vel[i]

    def LM_refine(self):
        J = np.eye(self.dim)
        delta = np.linalg.pinv(J.T @ J + 0.01 * np.eye(self.dim)) @ J.T @ (0.5 - self.gbest)
        self.gbest -= delta

    def run(self):
        for t in range(self.max_iter):
            self.update()
            if t % 10 == 0:
                self.LM_refine()
        return self.gbest, self.gbest_score

def simulate_error_compensation():
    model = LMPSO(pop_size=30, dim=6, max_iter=200)
    best, score = model.run()
    return best, score

def kinematics_forward(theta):
    T = np.eye(4)
    for i in range(3):
        c, s = math.cos(theta[i]), math.sin(theta[i])
        M = np.array([[c, -s, 0, 0.3 * i],
                      [s,  c, 0, 0.2 * i],
                      [0,  0, 1, 0.1 * i],
                      [0,  0, 0, 1]])
        T = np.dot(T, M)
    return T

def simulate_measurement():
    theta_true = np.random.rand(3) * np.pi / 2
    T_true = kinematics_forward(theta_true)
    noise = np.random.randn(4, 4) * 0.001
    return T_true + noise

def compensate_pose(T_meas, correction):
    T_comp = T_meas.copy()
    T_comp[:3, 3] -= correction[:3]
    return T_comp

def test_compensation():
    meas = simulate_measurement()
    model = LMPSO(20, 3, 100)
    best, score = model.run()
    T_corr = compensate_pose(meas, best)
    return T_corr

def evaluate():
    errors = []
    for i in range(50):
        T_meas = simulate_measurement()
        T_corr = test_compensation()
        diff = np.linalg.norm(T_meas[:3, 3] - T_corr[:3, 3])
        errors.append(diff)
    avg_err = np.mean(errors)
    return avg_err

if __name__ == "__main__":
    best, score = simulate_error_compensation()
    print("Optimal Parameters:", best)
    print("Error Score:", score)
    avg_error = evaluate()
    print("Average Compensation Error:", avg_error)

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