如何使用“ fmi2GetDirectionalDerivative”？

如何解决如何使用“ fmi2GetDirectionalDerivative”？

我正在尝试从FMU中获取模型Jacobian矩阵，根据以下文献，我可以使用fmi2GetDirectionalDerivative来做到这一点，但是我不确定我到底需要做些什么。

我的问题是：

1. 我可以在Dymola或MATLAB中调用此函数吗？
2. 截图或示例非常有帮助。

https://ep.liu.se/ecp/132/091/ecp17132831.pdf

解决方法

我不熟悉在MATLAB中调用DLL函数，但这是Python中的示例。 FMPy（https://github.com/CATIA-Systems/FMPy）具有这些包装程序，用于在python中运行FMU。

我已经针对我在此处编写的简单模型（How to access model jacobian from FMU or Dymola without analytical jacobian）进行了测试。在这种情况下，已知是状态或输入的值引用，未知是导数或输出的值引用。

当通过Dymola导出为模型交换FMU而不是协同仿真FMU时，我已经成功提取了Jacobian。

def get_jacobian(fmu,vr_knowns,vr_unknowns):
    """
    populates jacobian from list of knowns and unknowns
    can be only called after the current sim time and inputs are set
    """
    jacobian = []
    try:
        for vr_known in vr_knowns:
            for vr_unknown in vr_unknowns:
                jacobian.extend(
                    fmu.getDirectionalDerivative(
                        vUnknown_ref=[vr_unknown],vKnown_ref=[vr_known],dvKnown=[1.0]
                    ))
        print_status(f'Jacobian Elements: {jacobian}')
    except Exception as e:
        print("[ERROR] cannot compute jacobian at current timestep")
        print(f"[ERROR] {e}")

我使用此代码段使用FMPy收集状态和派生类的值引用：

# get FMU model description object
model_description = fmpy.read_model_description(
    os.path.join(fmu_path,fmu_filename)
)

# collect the value references
vrs = {}
for variable in model_description.modelVariables:
    vrs[variable.name] = variable.valueReference

# collect list of states and derivatives
states = []
derivatives = []
for derivative in model_description.derivatives:
    derivatives.append(derivative.variable.name)
    states.append(re.findall('^der\((.*)\)$',derivative.variable.name)[0])

# collect the value references for states and derivatives
vr_states = [vrs[x] for x in states]
vr_derivatives = [vrs[x] for x in derivatives]

,

在最近的2020年NAM Modelica会议上，三菱电机发表了一篇论文，可能与之相关。