Update to use CSBase

Author: baggepinnen

Project.toml

@@ -5,20 +5,18 @@ version = "0.1.0"
 
 [deps]
 ControlSystemIdentification = "3abffc1c-5106-53b7-b354-a47bfc086282"
-ControlSystems = "a6e380b2-a6ca-5380-bf3e-84a91bcd477e"
+ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 ModelingToolkitStandardLibrary = "16a59e39-deab-5bd0-87e4-056b12336739"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 RobustAndOptimalControl = "21fd56a4-db03-40ee-82ee-a87907bee541"
 SymbolicControlSystems = "886cb795-8fd3-4b11-92f6-8071e46f71c5"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [compat]
 ControlSystemIdentification = "2"
-ControlSystems = "1"
 ModelingToolkit = "8.18"
 ModelingToolkitStandardLibrary = "1.5"
 Optim = "1"
@@ -29,9 +27,10 @@ Symbolics = "4.10"
 UnPack = "1"
 julia = "1.6"
 
-
 [extras]
+ControlSystems = "a6e380b2-a6ca-5380-bf3e-84a91bcd477e"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["Test", "ControlSystems", "OrdinaryDiffEq"]

src/ControlSystemsMTK.jl

@@ -16,13 +16,12 @@ A third idea: just use named systems with named indexing to obtain any system yo
 
 =#
 using LinearAlgebra
-using ModelingToolkit, ControlSystems
-using ControlSystems: ssdata, AbstractStateSpace, Continuous, nstates, noutputs, ninputs
+using ModelingToolkit, ControlSystemsBase
+using ControlSystemsBase: ssdata, AbstractStateSpace, Continuous, nstates, noutputs, ninputs
 using ControlSystemIdentification
 using RobustAndOptimalControl
 import ModelingToolkit: ODESystem, FnType, Symbolics
 using ModelingToolkit: states, observed, isdifferential
-using OrdinaryDiffEq
 using ModelingToolkit.Symbolics
 using ModelingToolkit.Symbolics: jacobian, solve_for
 using UnPack

src/ode_system.jl

@@ -25,7 +25,7 @@ function ModelingToolkit.ODESystem(
     u_names = sys.nu == 1 ? [:u] : [Symbol("u$i") for i = 1:sys.nu],
     y_names = sys.ny == 1 ? [:y] : [Symbol("y$i") for i = 1:sys.ny],
 )
-    ControlSystems.isdiscrete(sys) && error(
+    ControlSystemsBase.isdiscrete(sys) && error(
         "Discrete systems not yet supported due to https://github.com/SciML/ModelingToolkit.jl/issues?q=is%3Aopen+is%3Aissue+label%3Adiscrete-time",
     )
     A, B, C, D = ssdata(sys)
@@ -107,14 +107,14 @@ function sconnect(
 end
 
 """
-    G = ControlSystems.feedback(loopgain::T; name)
+    G = ControlSystemsBase.feedback(loopgain::T; name)
 
 Form the feedback-interconnection
 \$G = L/(1+L)\$
 
 The system `G` will be a new system with `input` and `output` connectors.
 """
-function ControlSystems.feedback(
+function ControlSystemsBase.feedback(
     loopgain::T;
     name = Symbol("feedback $(loopgain.name)"),
 ) where {T<:ModelingToolkit.AbstractTimeDependentSystem}
@@ -154,7 +154,7 @@ end
 numeric(x::Num) = x.val
 
 
-function ControlSystems.ss(
+function ControlSystemsBase.ss(
     sys::ModelingToolkit.AbstractTimeDependentSystem,
     inputs,
     outputs,
@@ -205,10 +205,15 @@ function RobustAndOptimalControl.named_ss(
     end
     matrices, ssys = ModelingToolkit.linearize(sys, inputs, outputs; kwargs...)
     symstr(x) = Symbol(string(x))
+    unames = symstr.(inputs)
+    if size(matrices.B, 2) == 2length(inputs)
+         # This indicates that input derivatives are present
+         unames = [unames; Symbol.("der_" .* string.(unames))]
+    end
     named_ss(
         ss(matrices...);
         x = symstr.(states(ssys)),
-        u = symstr.(inputs),
+        u = unames,
         y = symstr.(outputs),
     )
 end

src/symbolic_optimization.jl

@@ -125,7 +125,7 @@ end
 
 function enforce_zero_noD!(P, ω)
     A, B, C, D = ssdata(P)
-    Y = ControlSystems.isdiscrete(P) ? (cis(ω * P.Ts) * I - A) \ B : (ω * I - A) \ B
+    Y = ControlSystemsBase.isdiscrete(P) ? (cis(ω * P.Ts) * I - A) \ B : (ω * I - A) \ B
     X = C # dcgain = X*Y
     # find smalles dX such that (X + dX)*Y = 0
     # dX*Y = -X*Y

test/test_ODESystem.jl

@@ -1,5 +1,5 @@
 using ControlSystemsMTK,
-    ControlSystems, ModelingToolkit, OrdinaryDiffEq, RobustAndOptimalControl
+    ControlSystemsBase, ModelingToolkit, OrdinaryDiffEq, RobustAndOptimalControl
 import ModelingToolkitStandardLibrary.Blocks as Blocks
 conn = ModelingToolkit.connect
 ## Test SISO (single input, single output) system
@@ -137,7 +137,7 @@ x0 = Pair[
 p = Pair[]
 
 prob = ODEProblem(simplified_sys, x0, (0.0, 20.0), p, jac = true)
-sol = solve(prob, OrdinaryDiffEq.Rodas5(), rtol = 1e-8, atol = 1e-8, saveat = 0:0.01:20)
+sol = solve(prob, OrdinaryDiffEq.Rodas5(), saveat = 0:0.01:20)
 if isinteractive()
     @show sol.retcode
     plot(sol, layout = length(states(simplified_sys)) + 1)