LTVModels

What is this package for?

Estimating linear dynamical models with time-varying parameters (LTV models)

What can LTV models be used for?

They can be used to model, simulate and predict the behaviour of time-varying systems.
Change-point detection, by estimating specific time points when the dynamics of a signal or system changes.
Tell your mother about.

Background and references

This repository implements the system-identification methods presented in
Bagge Carlson, F., Robertsson, A. & Johansson, R. "Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization" (IEEE ICCA 2018).
And the thesis
Bagge Carlson, F., "Machine Learning and System Identification for Estimation in Physical Systems" (PhD Thesis 2018).

@thesis{bagge2018,
  title        = {Machine Learning and System Identification for Estimation in Physical Systems},
  author       = {Bagge Carlson, Fredrik},
  keyword      = {Machine Learning,System Identification,Robotics,Spectral estimation,Calibration,State estimation},
  month        = {12},
  type         = {PhD Thesis},
  number       = {TFRT-1122},
  institution  = {Dept. Automatic Control, Lund University, Sweden},
  year         = {2018},
  url          = {},
}

Installation

using Pkg
pkg"add LinearTimeVaryingModelsBase"
pkg"add https://github.com/baggepinnen/LTVModels.jl"
using LTVModels

Usage

Overview

The package implements a number of models and methods to fit them. The models are

KalmanModel
LTVAutoRegressive
SimpleLTVModel

Any model can be instantiated by calling it with an identification-data object and some model-specific parameters, like this

y = randn(100)
d = iddata(y)
modelorder = 2
n  = 6
R1 = I(modelorder)
R2 = [1e5] # Increase for more smoothing/regularization
P0 = 1e4R1
model = LTVAutoRegressive(d, R1, R2, P0, extend=true)

Details

Usage of many of the functions is demonstrated in tests/runtests.jl

To fit a model by solving
minimize ||y-ŷ||² + λ²||Dₓ k||
and to reproduce Fig. 1 in the paper

using LTVModels, Plots
gr(size=(400,300))
T_ = 400
x,xm,u,n,m = LTVModels.testdata(T_)

d = iddata(x,u,x)
dm = iddata(xm,u,xm)

anim = Plots.Animation()
function callback(k)
    model = LTVModels.statevec2model(SimpleLTVModel,k,n,m,true)
    fig = plot(LTVModels.flatten(model.At), l=(2,:auto), xlabel="Time index", ylabel="Model coefficients", show=true, ylims=(-0.05, 1))
    frame(anim, fig)
end

λ = 17
model = SimpleLTVModel(dm, extend=true)
@time model = LTVModels.fit_admm(model, dm, λ, extend=true,
                                                iters    = 10000,
                                                D        = 1,
                                                zeroinit = true,
                                                tol      = 1e-5,
                                                ridge    = 0,
                                                cb       = callback)
gif(anim, "admm.gif", fps = 10)
y = predict(model,d)
e = x[:,2:end] - y[:,1:end-1]
println("RMS error: ", LTVModels.rms(e))

At,Bt = model.At,model.Bt
plot(flatten(At), l=(2,:auto), xlabel="Time index", ylabel="Model coefficients")
plot!([1,T_÷2-1], [0.95 0.1; 0 0.95][:]'.*ones(2), l=(:dash,:black, 1), primary=false)
plot!([T_÷2,T_], [0.5 0.05; 0 0.5][:]'.*ones(2), l=(:dash,:black, 1), grid=false, primary=false)
gui()

The animation shows the estimated model coefficients k[t] = A[t],B[t] as a function of time t converge as the optimization procedure is running. The final result is Fig. 1 in the paper.

Fit model using Kalman smoother

Code to fit a model by solving (7) using a Kalman smoother:

The code generates an LTV model A[t], B[t] and time series x,u governed by the model. A model is then fit using a Kalman smoother and the true model coefficients as well as the estimated are plotted. The gif below illustrates how the choice of covariance parameter influences the estimated time-evolution of the model parameters. As R2→∞, the result approaches that of standard least-squares estimation of an LTI model.

using LTVModels, Plots, LinearAlgebra
T = 2_000
A,B,d,n,m,N = LTVModels.testdata(T=T, σ_state_drift=0.001, σ_param_drift=0.001)

gr(size=(400,300))
eye(n) = Matrix{Float64}(I,n,n)
anim = @animate for r2 = exp10.(range(-3, stop=3, length=10))
    R1          = 0.001*eye(n^2+n*m)
    R2          = r2*eye(n)
    P0          = 10000R1
    model = KalmanModel(d,R1,R2,P0,extend=true, D=1)

    plot(flatten(A), l=(2,), xlabel="Time index", ylabel="Model coefficients", lab="True", c=:red)
    plot!(flatten(model.At), l=(2,), lab="Estimated", c=:blue, legend=false)
end
gif(anim, "kalman.gif", fps = 5)

Dynamic programming solver

To solve the optimization problem in section IID, see the function fit_statespace_dp with usage example in the function benchmark_ss

Two-step procedure

See functions in files peakdetection.jl and function fit_statespace_constrained

Figs. 2-3

The simulation of the two-link robot presented in figures 2-3 in the paper is generated using the code in two_link.jl

Fig. 4

To appear

DifferentialDynamicProgramming.jl

LTI estimation

Estimation of standard LTI systems is provided by ControlSystemIdentification.jl

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