E:\Anaconda\envs\tf1\python.exe G:\My_RL_PID\5\pso.py Traceback (most recent call last): File "G:\My_RL_PID\5\pso.py", line 68, in <module> pso.optimize() File "G:\My_RL_PID\5\pso.py", line 32, in optimize fitness = self.evaluate_fitness(particle.position) File "G:\My_RL_PID\5\pso.py", line 49, in evaluate_fitness Kp = position[i][0] IndexError: invalid index to scalar variable.
时间: 2023-07-22 21:09:29 浏览: 49
这个错误是由于 `position[i]` 的索引超出了有效范围,导致无法访问到正确的值。
在你提供的代码中,`position` 是一个一维数组,因此应该使用 `position[i]` 来访问每个粒子的位置。但是,由于 `position` 是一个标量(scalar)变量,而不是一个数组,因此导致了这个错误。
要解决这个问题,你可以将 `position` 转换为一个二维数组,其中每一行表示一个粒子的位置。这样,你就可以使用 `position[i][0]`、`position[i][1]` 和 `position[i][2]` 来分别访问每个粒子的 PID 参数。以下是修改后的代码:
```python
import numpy as np
import matplotlib.pyplot as plt
import control as ctl
class Particle:
def __init__(self, dim):
self.position = np.random.uniform(-1, 1, (dim, 3)) # 修改此处为二维数组
self.velocity = np.zeros((dim, 3)) # 修改此处为二维数组
self.best_position = self.position.copy() # 修改此处为复制二维数组
self.best_fitness = np.inf # 修改此处为正无穷大
class PSO:
def __init__(self, num_particles, dim, max_iter, c1, c2, w):
self.num_particles = num_particles
self.dim = dim
self.max_iter = max_iter
self.c1 = c1
self.c2 = c2
self.w = w
self.particles = [Particle(dim) for _ in range(num_particles)]
self.global_best_position = np.zeros((dim, 3)) # 修改此处为二维数组
self.global_best_fitness = np.inf # 修改此处为正无穷大
# 定义系统传递函数
num = [1] # 分子
den = [1, 0, 0] # 分母
self.sys = ctl.tf(num, den)
def optimize(self):
for _ in range(self.max_iter):
for particle in self.particles:
fitness = self.evaluate_fitness(particle.position)
if np.all(fitness < particle.best_fitness): # 修改此处为逐元素比较
particle.best_position = particle.position.copy() # 修改此处为复制二维数组
particle.best_fitness = fitness.copy() # 修改此处为复制数组
if np.all(fitness < self.global_best_fitness): # 修改此处为逐元素比较
self.global_best_position = particle.position.copy() # 修改此处为复制二维数组
self.global_best_fitness = fitness.copy() # 修改此处为复制数组
particle.velocity = (self.w * particle.velocity +
self.c1 * np.random.random() * (particle.best_position - particle.position) +
self.c2 * np.random.random() * (self.global_best_position - particle.position))
particle.position += particle.velocity
def evaluate_fitness(self, position):
n_particles = position.shape[0] # 获取粒子的数量
j = np.zeros(n_particles) # 初始化误差数组
for i in range(n_particles):
Kp = position[i][0]
Ki = position[i][1]
Kd = position[i][2]
pid = ctl.TransferFunction([Kd, Kp, Ki], [1, 0])
closed_loop = ctl.feedback(pid * self.sys, 1)
t, y = ctl.step_response(closed_loop)
error = 1.0 - y
j[i] = np.sum(np.abs(error))
return j
# 使用示例
num_particles = 10
dim = 3 # PID参数的维度
max_iter = 100
c1 = 2.0
c2 = 2.0
w = 0.7
pso = PSO(num_particles, dim, max_iter, c1, c2, w)
pso.optimize()
best_pid_params = pso.global_best_position
print("Best PID parameters:\n", best_pid_params)
```
现在,`position` 变为一个二维数组,每个粒子的位置存储在不同的行中。通过这种方式,你应该能够正确访问每个粒子的 PID 参数,并且不再出现索引错误。希望对你有所帮助!