Description

The state estimation example in docs/src/tutorials/disturbance_modeling.md is disabled due to compatibility issues with generate_control_function and analysis points.

Problem

The example showing how to generate functions f and g for state estimation using generate_control_function and build_explicit_observed_function fails when used with models containing analysis points.

Location

docs/src/tutorials/disturbance_modeling.md - lines 206-235

Expected behavior

Should be able to generate control functions from a model with analysis points for use in state estimators like Kalman filters or particle filters.

Related code

inputs = [ssys.u]
disturbance_inputs = [ssys.d1, ssys.d2]
P = ssys.system_model
outputs = [P.inertia1.phi, P.inertia2.phi, P.inertia1.w, P.inertia2.w]

(f_oop, f_ip), x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
    model_with_disturbance, inputs; known_disturbance_inputs = disturbance_inputs)

g = ModelingToolkit.build_explicit_observed_function(io_sys, outputs; inputs)

op = ModelingToolkit.inputs(io_sys) .=> 0
x0 = ModelingToolkit.get_u0(io_sys, op)
p = MTKParameters(io_sys, op)
u = zeros(1)
w = zeros(length(disturbance_inputs))
@test f_oop(x0, u, p, t, w) == zeros(5)
@test g(x0, u, p, 0.0) == [0, 0, 0, 0]

Context

This is important for users who want to use ModelingToolkit models with state estimation libraries like LowLevelParticleFiltersMTK.jl.