以下是使用模糊控制算法优化PID的程序：
[code]
import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
# 设定输入和输出的变量范围
error = ctrl.Antecedent(np.arange(-10, 10, 0.1), \'error\')
change_error = ctrl.Antecedent(np.arange(-10, 10, 0.1), \'change_error\')
output = ctrl.Consequent(np.arange(-10, 10, 0.1), \'output\')
# 设定每个变量的隶属度函数
error.automf(3)
change_error.automf(3)
output.automf(3)
# 设定输出的规则
rule1 = ctrl.Rule(error[\'poor\'] & change_error[\'poor\'], output[\'negative\'])
rule2 = ctrl.Rule(error[\'poor\'] & change_error[\'average\'], output[\'zero\'])
rule3 = ctrl.Rule(error[\'poor\'] & change_error[\'good\'], output[\'positive\'])
rule4 = ctrl.Rule(error[\'average\'] & change_error[\'poor\'], output[\'negative\'])
rule5 = ctrl.Rule(error[\'average\'] & change_error[\'average\'], output[\'zero\'])
rule6 = ctrl.Rule(error[\'average\'] & change_error[\'good\'], output[\'positive\'])
rule7 = ctrl.Rule(error[\'good\'] & change_error[\'poor\'], output[\'negative\'])
rule8 = ctrl.Rule(error[\'good\'] & change_error[\'average\'], output[\'zero\'])
rule9 = ctrl.Rule(error[\'good\'] & change_error[\'good\'], output[\'positive\'])
# 设定控制系统并进行模拟
pid_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9])
pid_sim = ctrl.ControlSystemSimulation(pid_ctrl)
# 设定初始参数
kp = 1
ki = 0.1
kd = 0.01
dt = 0.1
error.value = 0
change_error.value = 0
for i in range(100):
# 计算误差及变化量
current_error = setpoint - process_value
change_error_value = (current_error - error.value) / dt
# 模糊控制
pid_sim.input[\'error\'] = current_error
pid_sim.input[\'change_error\'] = change_error_value
pid_sim.compute()
# 更新PID参数
output_value = pid_sim.output[\'output\']
kp += output_value * pid_sim.antecedent[\'error\'].mf[1]
ki += output_value * pid_sim.antecedent[\'error\'].mf[2]
kd += output_value * pid_sim.antecedent[\'change_error\'].mf[1]
# 计算新的输出值
output_value = kp * current_error + ki * error.value + kd * change_error_value
# 更新误差值
error.value = current_error
# 更新过程值
process_value += output_value * dt
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