轨迹优化，gpops

# 以下是您使用gpops2进行轨迹优化的代码
import gpops2

# 定义目标函数和约束
def myfunc(costate, state, t, controls, parameters):
    # 目标函数代码（省略）
    pass

def mycon(costate, state, t, controls, parameters):
    # 禁飞区约束
    x1, y1 = (x1, y1)  # 禁飞区1的坐标
    x2, y2 = (x2, y2)  # 禁飞区1的坐标

    x3, y3 = (x3, y3)  # 禁飞区2的坐标
    x4, y4 = (x4, y4)  # 禁飞区2的坐标

    # 添加禁飞区惩罚项
    penalty = 1000  # 惩罚系数，您可以根据实际情况进行调整
    con1 = penalty * (x1 - state[:, 0]) * (state[:, 0] - x2)  # 禁飞区1的约束
    con2 = penalty * (y1 - state[:, 1]) * (state[:, 1] - y2)  # 禁飞区1的约束

    con3 = penalty * (x3 - state[:, 0]) * (state[:, 0] - x4)  # 禁飞区2的约束
    con4 = penalty * (y3 - state[:, 1]) * (state[:, 1] - y4)  # 禁飞区2的约束

    # 返回约束
    return np.concatenate([con1, con2, con3, con4])

# 定义初始值和边界条件
initial_guess = np.zeros((N, 1))

bounds = gpops2.Bounds()
bounds.phase(0).initialtime.lower = 0
bounds.phase(0).initialtime.upper = 0
bounds.phase(0).finaltime.lower = 0
bounds.phase(0).finaltime.upper = 1
# 其他边界条件（省略）

# 定义设置
setup = gpops2.SetUp()
setup.bounds = bounds
setup.funcs.cost = myfunc
setup.funcs.eventgroup = mycon
setup.derivatives = 'fd'

# 进行轨迹优化
solution = gpops2.solve(setup)

# 优化结果（省略）