基于BP神经网络的PID控制算法 python

以下是一个基于BP神经网络的PID控制算法Python代码示例：

import numpy as np

class BP_PID:
    def __init__(self, input_size, hidden_size, output_size, Kp, Ki, Kd, learning_rate):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.Kp = Kp
        self.Ki = Ki
        self.Kd = Kd
        self.learning_rate = learning_rate
        self.W1 = np.random.randn(input_size, hidden_size)
        self.b1 = np.zeros((1, hidden_size))
        self.W2 = np.random.randn(hidden_size, output_size)
        self.b2 = np.zeros((1, output_size))
        self.I = 0
        self.D = 0
        self.error_sum = 0
        self.last_error = 0

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def forward(self, x):
        self.hidden = self.sigmoid(np.dot(x, self.W1) + self.b1)
        self.output = np.dot(self.hidden, self.W2) + self.b2

    def backward(self, x, y, output):
        error = y - output
        self.error_sum += error
        derivative = error - self.last_error
        self.I = self.error_sum * self.Ki
        self.D = derivative * self.Kd
        self.last_error = error
        d_output = error
        d_hidden = np.dot(d_output, self.W2.T) * self.sigmoid_derivative(self.hidden)
        self.W2 += self.learning_rate * np.dot(self.hidden.T, d_output)
        self.b2 += self.learning_rate * np.sum(d_output, axis=0, keepdims=True)
        self.W1 += self.learning_rate * np.dot(x.T, d_hidden)
        self.b1 += self.learning_rate * np.sum(d_hidden, axis=0, keepdims=True)

    def train(self, x, y):
        self.forward(x)
        self.backward(x, y, self.output)

    def control(self, x):
        self.forward(x)
        error = 0 - self.output
        control = self.Kp * error + self.I + self.D
        return control

这个类实现了一个具有输入层、隐藏层和输出层的BP神经网络，可以用于PID控制问题。在训练时，我们使用反向传播算法来更新神经网络的权重和偏置，同时计算积分项和微分项。在控制时，我们使用当前输出与期望输出之间的误差来计算比例项，使用累积误差来计算积分项，使用当前误差与上一次误差之差来计算微分项，最终得到控制量。