由于代码较长，我将为您提供一个简化版的模糊PID温度控制算法的Python实现。您可以根据需要进行扩展和修改。 ```python import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl # 定义输入变量 temperature = ctrl.Antecedent(np.arange(0, 101, 1), 'temperature') error = ctrl.Antecedent(np.arange(-50, 51, 1), 'error') output = ctrl.Consequent(np.arange(0, 101, 1), 'output') # 定义模糊集和隶属度函数 temperature['low'] = fuzz.trimf(temperature.universe, [0, 0, 50]) temperature['high'] = fuzz.trimf(temperature.universe, [50, 100, 100]) error['negative'] = fuzz.trimf(error.universe, [-50, -50, 0]) error['zero'] = fuzz.trimf(error.universe, [-50, 0, 50]) error['positive'] = fuzz.trimf(error.universe, [0, 50, 50]) output['low'] = fuzz.trimf(output.universe, [0, 0, 50]) output['medium'] = fuzz.trimf(output.universe, [0, 50, 100]) output['high'] = fuzz.trimf(output.universe, [50, 100, 100]) # 定义模糊规则 rule1 = ctrl.Rule(temperature['low'] & error['negative'], output['low']) rule2 = ctrl.Rule(temperature['low'] & error['zero'], output['low']) rule3 = ctrl.Rule(temperature['low'] & error['positive'], output['medium']) rule4 = ctrl.Rule(temperature['high'] & error['negative'], output['medium']) rule5 = ctrl.Rule(temperature['high'] & error['zero'], output['medium']) rule6 = ctrl.Rule(temperature['high'] & error['positive'], output['high']) # 创建控制系统 control_system = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6]) # 创建模糊控制器 fuzzy_controller = ctrl.ControlSystemSimulation(control_system) # 设置输入值 fuzzy_controller.input['temperature'] = 30 fuzzy_controller.input['error'] = -10 # 计算输出值 fuzzy_controller.compute() print("Output:", fuzzy_controller.output['output']) ``` 这个简化版的模糊PID温度控制算法包括了输入变量（温度和误差）和输出变量（输出）。您需要根据您的工业生产线的具体需求来调整模糊集和隶属度函数以及模糊规则。您还可以考虑添加更多的输入变量和输出变量，以使控制策略更加复杂和精确。 [2024-05-31 13:09:30 | AI写代码神器 | 752点数解答]