FMU Parameter Optimization

Tutorial by Tobias Thummerer

License

# Copyright (c) 2021 Tobias Thummerer, Lars Mikelsons
# Licensed under the MIT license. 
# See LICENSE (https://github.com/thummeto/FMI.jl/blob/main/LICENSE) file in the project root for details.

Introduction to the example

This example shows how a parameter optimization can be set up for a FMU. The goal is to fit FMU parameters (and initial states), so that a reference trajectory is fit as good as possible.

Note, that this tutorial covers optimization without gradient information. Basically, FMI.jl supports gradient based optimization, too.

Other formats

Besides, this Jupyter Notebook there is also a Julia file with the same name, which contains only the code cells and for the documentation there is a Markdown file corresponding to the notebook.

Getting started

Installation prerequisites

	Description	Command
1.	Enter Package Manager via	]
2.	Install FMI via	add FMI
3.	Install FMIZoo via	add FMIZoo
4.	Install Optim via	add Optim
5.	Install Plots via	add Plots

Code section

To run the example, the previously installed packages must be included.

# imports
using FMI
using FMIZoo
using Optim
using Plots
using DifferentialEquations

Simulation setup

Next, the start time and end time of the simulation are set.

tStart = 0.0
tStop = 5.0
tStep = 0.1
tSave = tStart:tStep:tStop

0.0:0.1:5.0

Import FMU

In the next lines of code the FMU model from FMIZoo.jl is loaded and the information about the FMU is shown.

# we use an FMU from the FMIZoo.jl
fmu = loadFMU("SpringPendulum1D", "Dymola", "2022x"; type=:ME)
info(fmu)

#################### Begin information for FMU ####################
	Model name:			SpringPendulum1D
	FMI-Version:			2.0
	GUID:				{fc15d8c4-758b-48e6-b00e-5bf47b8b14e5}
	Generation tool:		Dymola Version 2022x (64-bit), 2021-10-08
	Generation time:		2022-05-19T06:54:23Z
	Var. naming conv.:		structured
	Event indicators:		0
	Inputs:				0
	Outputs:			0
	States:				2


		33554432 ["mass.s"]
		33554433 ["mass.v"]
	Parameters:			7
		16777216 ["mass_s0"]
		16777217 ["mass_v0"]
		16777218 ["fixed.s0"]
		16777219 ["spring.c"]
		16777220 ["spring.s_rel0"]
		16777221 ["mass.m"]
		16777222 ["mass.L"]
	Supports Co-Simulation:		true
		Model identifier:	SpringPendulum1D
		Get/Set State:		true
		Serialize State:	true
		Dir. Derivatives:	true
		Var. com. steps:	true
		Input interpol.:	true
		Max order out. der.:	1
	Supports Model-Exchange:	true
		Model identifier:	SpringPendulum1D
		Get/Set State:		true
		Serialize State:	true
		Dir. Derivatives:	true
##################### End information for FMU #####################

Now, the optimization objective (the function to minimize) needs to be defined. In this case, we just want to do a simulation and compare it to a regular sin wave.

s_tar = 1.0 .+ sin.(tSave)

# a function to simulate the FMU for given parameters
function simulateFMU(p)
    s0, v0, c, m = p # unpack parameters: s0 (start position), v0 (start velocity), c (spring constant) and m (pendulum mass)

    # pack the parameters into a dictionary
    paramDict = Dict{String, Any}()
    paramDict["spring.c"] = c 
    paramDict["mass.m"] = m

    # pack the start state
    x0 = [s0, v0]

    # simulate with given start stae and parameters
    sol = simulate(fmu, (tStart, tStop); x0=x0, parameters=paramDict, saveat=tSave)

    # get state with index 1 (the position) from the solution
    s_res = getState(sol, 1; isIndex=true) 

    return s_res
end

# the optimization objective
function objective(p)
    s_res = simulateFMU(p)

    # return the position error sum between FMU simulation (s_res) and target (s_tar)
    return sum(abs.(s_tar .- s_res))    
end

objective (generic function with 1 method)

Now let's see how far we are away for our guess parameters:

s0 = 0.0 
v0 = 0.0
c = 1.0
m = 1.0 
p = [s0, v0, c, m]

obj_before = objective(p) # not really good!

54.432324541060666

Let's have a look on the differences:

s_fmu = simulateFMU(p); # simulate the position

plot(tSave, s_fmu; label="FMU")
plot!(tSave, s_tar; label="Optimization target")

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Not that good. So let's do a bit of optimization!

opt = Optim.optimize(objective, p; iterations=250) # do max. 250 iterations
obj_after = opt.minimum # much better!
p_res = opt.minimizer # the optimized parameters

4-element Vector{Float64}:
 1.000927423495889
 0.9780437253734777
 0.11212447550094248
 0.09761028417513656

Looks promising, let's have a look on the results plot:

s_fmu = simulateFMU(p_res); # simulate the position

plot(tSave, s_fmu; label="FMU")
plot!(tSave, s_tar; label="Optimization target")

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Actually a pretty fit! If you have higher requirements, check out the Optim.jl library.

unloadFMU(fmu)

Summary

This tutorial showed how a parameter (and start value) optimization can be performed on a FMU with a gradient free optimizer. This tutorial will be extended soon to further show how convergence for large parameter spaces can be improoved!