search

JuliaControl / ModelPredictiveControl.jl

An open source model predictive control package for Julia.
https://juliacontrol.github.io/ModelPredictiveControl.jl/stable
MIT License
68 stars 0 forks source link

plot not working #119

Open 1-Bart-1 opened 3 hours ago

1-Bart-1 commented 3 hours ago

I am using MPC.jl in a module. When running ModelPredictiveControl.plot(res; plotx=true, ploty=true, plotu=true, plotxwithx̂=true) I get the error: 

ERROR: MethodError: no method matching plot(::ModelPredictiveControl.SimResult{Float64, ModelPredictiveControl.LinMPC{…}}; plotx::Bool, ploty::Bool, plotu::Bool, plotxwithx̂::Bool)

Closest candidates are:
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.PredictiveController}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:325
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.StateEstimator}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:324
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.SimModel}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:323

Stacktrace:
 [1] plot(ci::ControlInterface)
   @ KitePredictiveControl ~/Code/KitePredictiveControl.jl/src/KitePredictiveControl.jl:209
 [2] top-level scope
   @ ./REPL[17]:5
Some type information was truncated. Use `show(err)` to see complete types.

I don't know what could cause this. It worked before, but after some code reformatting, it stopped working.

julia> typeof(res)
SimResult{Float64, LinMPC{Float64, KalmanFilter{Float64, LinModel{Float64}}, JuMP.Model}}

julia> res.obj
LinMPC controller with a sample time Ts = 0.05 s, DAQP optimizer, KalmanFilter estimator and:
 20 prediction steps Hp
  4 control steps Hc
  1 slack variable ϵ (control constraints)
  3 manipulated inputs u (3 integrating states)
 78 estimated states x̂
 26 measured outputs ym (23 integrating states)
  0 unmeasured outputs yu
  0 measured disturbances d

  julia> res.X_data
52×800 Matrix{Float64}:
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  12.1372       12.4948    12.1372       12.1184
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   0.420671      0.398937   0.420671      0.420346
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  26.1225       24.0023    26.1225       26.1313
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  12.1372       12.4948    12.1372       12.1184
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  -0.420671     -0.398937  -0.420671     -0.420346
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0  26.1225       24.0023    26.1225       26.1313
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   2.23948       2.17889    2.23948       2.23148
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   2.14705e-11   0.0        1.76796e-11   1.65288e-11
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  25.0963       24.9049    25.0963       25.102
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   0.14923       0.0        0.14923       0.15375
 ⋮                        ⋮                        ⋮                        ⋮              ⋱  ⋮                        ⋮                                           
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   0.308326      0.0        0.308326      0.210995
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  -0.338401      0.0       -0.338401     -0.269748
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   5.05014e-12   0.0       -7.45084e-12  -4.56091e-11
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0   0.307788      0.0        0.307788      0.209391
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  57.611        57.6066    57.611        57.611
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  57.611        57.6066    57.611        57.611
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0  50.1319       50.0       50.1319       50.1404
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   0.00112142    0.0        0.00112142    0.0011503
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  …  0.0  0.0  0.0  0.0  0.0  0.0   0.00112142    0.0        0.00112142    0.0011503
 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0     0.0  0.0  0.0  0.0  0.0  0.0   0.16879       0.0        0.16879       0.169753
baggepinnen commented 3 hours ago

I would guess that you do something wrong in your package, how is this method defined? is it using RecipesBase.jl or directly overloading Plots.plot?

plot(ci::ControlInterface)
   @ KitePredictiveControl ~/Code/KitePredictiveControl.jl/src/KitePredictiveControl.jl:209
1-Bart-1 commented 3 hours ago

It could very well be that I am doing something wrong in my package. But changing the package plot function to controlplot does not solve the issue:

ERROR: LoadError: MethodError: no method matching plot(::ModelPredictiveControl.SimResult{Float64, ModelPredictiveControl.LinMPC{…}}; plotx::Bool, ploty::Bool, plotu::Bool, plotxwithx̂::Bool)

Closest candidates are:
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.PredictiveController}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:325
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.StateEstimator}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:324
  plot(::Nothing, ::ModelPredictiveControl.SimResult{<:Real, <:ModelPredictiveControl.SimModel}) got unsupported keyword arguments "plotx", "ploty", "plotu", "plotxwithx̂"
   @ ModelPredictiveControl ~/.julia/packages/ModelPredictiveControl/0rhFm/src/plot_sim.jl:323

Stacktrace:
 [1] controlplot(ci::ControlInterface)
   @ KitePredictiveControl ~/Code/KitePredictiveControl.jl/src/KitePredictiveControl.jl:210
 [2] top-level scope
   @ ~/Code/KitePredictiveControl.jl/examples/simple.jl:16
 [3] include(fname::String)
   @ Base.MainInclude ./client.jl:489
 [4] top-level scope
   @ REPL[1]:1
in expression starting at /home/bart/Code/KitePredictiveControl.jl/examples/simple.jl:12
Some type information was truncated. Use `show(err)` to see complete types.

The method is defined in module KitePredictiveControl as:

function controlplot(ci::ControlInterface)
    res = SimResult(ci.mpc, ci.U_data, ci.Y_data; ci.Ry_data, ci.X̂_data, ci.X_data)
    # return ModelPredictiveControl.plot(res; plotx=true, ploty=ci.output_idxs, plotu=true, plotxwithx̂=ci.observed_idxs)

    return ModelPredictiveControl.plot(res; plotx=true, ploty=true, plotu=true, plotxwithx̂=true)
end
baggepinnen commented 3 hours ago

It looks like this is the culprit https://github.com/JuliaControl/ModelPredictiveControl.jl/blob/38333015ea653e601ed1955bed2f1142dddc64a7/src/plot_sim.jl#L323

As a fix for you, I would try calling Plots.plot instead of ModelPredictiveControl.plot

1-Bart-1 commented 3 hours ago

Thanks, that solved the issue. Maybe this should be documented to avoid future confusions?

baggepinnen commented 3 hours ago

see PR linekd above

1-Bart-1 commented 3 hours ago

Wow, that was fast :rocket: