粒子群优化模糊pid控制代码

以下是一个简单的粒子群优化模糊PID控制的代码示例：

import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
import random

class Particle:
    def __init__(self, x0):
        self.position = x0
        self.velocity = np.zeros_like(x0)
        self.best_position = x0
        self.error = -1

    def evaluate(self, costFunc):
        self.error = costFunc(self.position)

        if self.error < costFunc(self.best_position):
            self.best_position = self.position

    def update_velocity(self, best_global_pos):
        w = 0.5
        c1 = 1
        c2 = 2

        r1 = random.random()
        r2 = random.random()

        cognitive_velocity = c1 * r1 * (self.best_position - self.position)
        social_velocity = c2 * r2 * (best_global_pos - self.position)
        self.velocity = w * self.velocity + cognitive_velocity + social_velocity

    def update_position(self, bounds):
        self.position = self.position + self.velocity

        for i in range(len(bounds)):
            if self.position[i] > bounds[i][1]:
                self.position[i] = bounds[i][1]

            if self.position[i] < bounds[i][0]:
                self.position[i] = bounds[i][0]

class PIDController:
    def __init__(self, Kp, Ki, Kd):
        self.Kp = Kp
        self.Ki = Ki
        self.Kd = Kd
        self.error = 0
        self.error_prev = 0
        self.integral = 0

    def compute(self, SP, PV):
        error = SP - PV
        self.integral = self.integral + error
        derivative = error - self.error_prev
        output = self.Kp * error + self.Ki * self.integral + self.Kd * derivative
        self.error_prev = error
        return output

def fuzzy_cost(x):
    Kp = x[0]
    Ki = x[1]
    Kd = x[2]

    error = 0
    SP = 50

    # 定义模糊变量和模糊集合
    error_fuzzy = ctrl.Antecedent(np.arange(-100, 100, 1), 'error')
    output_fuzzy = ctrl.Consequent(np.arange(-100, 100, 1), 'output')

    # 定义模糊集合
    error_fuzzy['negative'] = fuzz.trimf(error_fuzzy.universe, [-100, -100, 0])
    error_fuzzy['zero'] = fuzz.trimf(error_fuzzy.universe, [-100, 0, 100])
    error_fuzzy['positive'] = fuzz.trimf(error_fuzzy.universe, [0, 100, 100])

    output_fuzzy['negative'] = fuzz.trimf(output_fuzzy.universe, [-100, -100, 0])
    output_fuzzy['zero'] = fuzz.trimf(output_fuzzy.universe, [-100, 0, 100])
    output_fuzzy['positive'] = fuzz.trimf(output_fuzzy.universe, [0, 100, 100])

    # 定义模糊规则
    rule1 = ctrl.Rule(error_fuzzy['negative'], output_fuzzy['negative'])
    rule2 = ctrl.Rule(error_fuzzy['zero'], output_fuzzy['zero'])
    rule3 = ctrl.Rule(error_fuzzy['positive'], output_fuzzy['positive'])

    # 控制系统
    pid_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
    pid_sim = ctrl.ControlSystemSimulation(pid_ctrl)

    # 计算模糊PID控制器输出
    for i in range(len(PV)):
        pid_sim.input['error'] = SP - PV[i]
        pid_sim.compute()
        output = pid_sim.output['output']
        error = error + (SP - PV[i]) ** 2 + output ** 2

    return error

def PSO(costFunc, x0, bounds, num_particles, maxiter):
    global best_global_position
    global best_global_error

    best_global_error = -1
    best_global_position = None
    swarm = []

    for i in range(num_particles):
        x = Particle(x0)
        swarm.append(x)

    for i in range(maxiter):
        for j in range(num_particles):
            swarm[j].evaluate(costFunc)

            if swarm[j].error < best_global_error or best_global_error == -1:
                best_global_position = swarm[j].position
                best_global_error = swarm[j].error

        for j in range(num_particles):
            swarm[j].update_velocity(best_global_position)
            swarm[j].update_position(bounds)

    return best_global_position

# 测试代码
if __name__ == "__main__":
    # 设置PID控制器参数初值
    Kp = 1
    Ki = 0.1
    Kd = 0.01

    x0 = [Kp, Ki, Kd]
    bounds = [(0, 10), (0, 1), (0, 0.1)]
    num_particles = 100
    maxiter = 50

    # 运行PSO算法进行优化
    best_parameters = PSO(fuzzy_cost, x0, bounds, num_particles, maxiter)

    # 输出最优参数
    print("最优参数：Kp = %.2f, Ki = %.2f, Kd = %.2f" % (best_parameters[0], best_parameters[1], best_parameters[2]))

需要注意的是，上述代码仅为示例，实际应用中还需要根据具体的控制对象和控制要求进行适当的修改和调整。