refactor: depend on MTKBase

Project.toml

@@ -1,14 +1,14 @@
 name = "ModelingToolkitStandardLibrary"
 uuid = "16a59e39-deab-5bd0-87e4-056b12336739"
-authors = ["Chris Rackauckas and Julia Computing"]
 version = "2.25.0"
+authors = ["Chris Rackauckas and Julia Computing"]
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
+ModelingToolkitBase = "7771a370-6774-4173-bd38-47e70ca0b839"
 PreallocationTools = "d236fae5-4411-538c-8e31-a6e3d9e00b46"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 
@@ -18,19 +18,21 @@ Aqua = "0.8"
 ChainRulesCore = "1.24"
 ControlSystemsBase = "1.4"
 DataFrames = "1.7"
-DataInterpolations = "6"
+DataInterpolations = "8"
 DiffEqBase = "6.152"
-ForwardDiff = "0.10"
+ForwardDiff = "0.10, 1"
 IfElse = "0.1"
 LinearAlgebra = "1.10"
-ModelingToolkit = "10"
+ModelingToolkit = "11"
+ModelingToolkitBase = "1"
 OrdinaryDiffEq = "6.87"
 OrdinaryDiffEqDefault = "1.1"
 PreallocationTools = "0.4.23"
 SafeTestsets = "0.1"
+SciCompDSL = "1"
 SciMLStructures = "1.4.2"
 SymbolicIndexingInterface = "0.3.28"
-Symbolics = "6.14"
+Symbolics = "7"
 Test = "1"
 julia = "1.10"
 
@@ -42,12 +44,14 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 OrdinaryDiffEqDefault = "50262376-6c5a-4cf5-baba-aaf4f84d72d7"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+SciCompDSL = "91a8cdf1-4ca6-467b-a780-87fda3fff15e"
 SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ADTypes", "Aqua", "LinearAlgebra", "OrdinaryDiffEqDefault", "OrdinaryDiffEq", "SafeTestsets", "Test", "ControlSystemsBase", "DataFrames", "DataInterpolations", "SciMLStructures", "SymbolicIndexingInterface", "ForwardDiff"]
+test = ["ADTypes", "Aqua", "LinearAlgebra", "OrdinaryDiffEqDefault", "OrdinaryDiffEq", "SafeTestsets", "Test", "ControlSystemsBase", "DataFrames", "DataInterpolations", "SciMLStructures", "SymbolicIndexingInterface", "ForwardDiff", "SciCompDSL", "ModelingToolkit"]

src/Blocks/Blocks.jl

@@ -2,10 +2,10 @@
 The module `Blocks` contains common input-output components, referred to as blocks.
 """
 module Blocks
-using ModelingToolkit, Symbolics
+using ModelingToolkitBase, Symbolics
 import IfElse: ifelse
 import ..@symcheck
-using ModelingToolkit: getdefault, t_nounits as t, D_nounits as D
+using ModelingToolkitBase: getdefault, t_nounits as t, D_nounits as D
 
 export RealInput, RealInputArray, RealOutput, RealOutputArray, SISO
 include("utils.jl")
@@ -28,7 +28,4 @@ export Integrator, Derivative, FirstOrder, SecondOrder, StateSpace, TransferFunc
 export PI, LimPI, PID, LimPID
 include("continuous.jl")
 
-export AnalysisPoint, get_sensitivity, get_comp_sensitivity,
-       get_looptransfer, open_loop
-
 end

src/Blocks/continuous.jl

@@ -17,18 +17,28 @@ Initial value of integrator state ``x`` can be set with `x`
 
   - `x`: State of Integrator. Defaults to 0.0.
 """
-@mtkmodel Integrator begin
-    @extend u, y = siso = SISO()
-    @variables begin
-        x(t) = 0.0, [description = "State of Integrator"]
+@component function Integrator(; name, k = 1, x = 0.0)
+    @named siso = SISO()
+    @unpack u, y = siso
+
+    pars = @parameters begin
+        k = k, [description = "Gain"]
     end
-    @parameters begin
-        k = 1, [description = "Gain"]
+
+    systems = @named begin
     end
-    @equations begin
-        D(x) ~ k * u
-        y ~ x
+
+    vars = @variables begin
+        x(t) = x, [description = "State of Integrator"]
     end
+
+    equations = Equation[
+        D(x) ~ k * u,
+        y ~ x
+    ]
+
+    sys = System(equations, t, vars, pars; name, systems)
+    return extend(sys, siso)
 end
 
 """
@@ -37,8 +47,8 @@ end
 Outputs an approximate derivative of the input. The transfer function of this block is
 
 ```
-k      k        ks  
-─ - ─────── = ────── 
+k      k        ks
+─ - ─────── = ──────
 T   sT² + T   sT + 1
 ```
 
@@ -62,23 +72,32 @@ Initial value of the state ``x`` can be set with `x`.
   - `input`
   - `output`
 """
-@mtkmodel Derivative begin
-    @extend u, y = siso = SISO()
-    @variables begin
-        x(t) = 0.0, [description = "Derivative-filter state"]
-    end
-    @parameters begin
+@component function Derivative(; name, k = 1, T = nothing, x = 0.0)
+    @symcheck T > 0 ||
+              throw(ArgumentError("Time constant `T` has to be strictly positive"))
+
+    @named siso = SISO()
+    @unpack u, y = siso
+
+    pars = @parameters begin
         T = T, [description = "Time constant"]
-        k = 1, [description = "Gain"]
+        k = k, [description = "Gain"]
     end
-    begin
-        @symcheck T > 0 ||
-                  throw(ArgumentError("Time constant `T` has to be strictly positive"))
+
+    systems = @named begin
     end
-    @equations begin
-        D(x) ~ (u - x) / T
-        y ~ (k / T) * (u - x)
+
+    vars = @variables begin
+        x(t) = x, [description = "Derivative-filter state"]
     end
+
+    equations = Equation[
+        D(x) ~ (u - x) / T,
+        y ~ (k / T) * (u - x)
+    ]
+
+    sys = System(equations, t, vars, pars; name, systems)
+    return extend(sys, siso)
 end
 
 """
@@ -116,26 +135,32 @@ Initial value of the state `x` can be set with `x`
 
 See also [`SecondOrder`](@ref)
 """
-@mtkmodel FirstOrder begin
-    @extend u, y = siso = SISO()
-    @structural_parameters begin
-        lowpass = true
-    end
-    @variables begin
-        x(t) = 0.0, [description = "State of FirstOrder filter"]
-    end
-    @parameters begin
+@component function FirstOrder(; name, lowpass = true, T = nothing, k = 1.0, x = 0.0)
+    @symcheck T > 0 ||
+              throw(ArgumentError("Time constant `T` has to be strictly positive"))
+
+    @named siso = SISO()
+    @unpack u, y = siso
+
+    pars = @parameters begin
         T = T, [description = "Time constant"]
-        k = 1.0, [description = "Gain"]
+        k = k, [description = "Gain"]
     end
-    begin
-        @symcheck T > 0 ||
-                  throw(ArgumentError("Time constant `T` has to be strictly positive"))
+
+    systems = @named begin
     end
-    @equations begin
-        D(x) ~ (k * u - x) / T
-        lowpass ? y ~ x : y ~ k * u - x
+
+    vars = @variables begin
+        x(t) = x, [description = "State of FirstOrder filter"]
     end
+
+    equations = Equation[
+        D(x) ~ (k * u - x) / T,
+        lowpass ? (y ~ x) : (y ~ k * u - x)
+    ]
+
+    sys = System(equations, t, vars, pars; name, systems)
+    return extend(sys, siso)
 end
 
 """
@@ -165,22 +190,32 @@ Initial value of the state `x` can be set with `x`, and of derivative state `xd`
   - `input`
   - `output`
 """
-@mtkmodel SecondOrder begin
-    @extend u, y = siso = SISO()
-    @variables begin
-        x(t), [description = "State of SecondOrder filter", guess = 0.0]
-        xd(t), [description = "Derivative state of SecondOrder filter", guess = 0.0]
+@component function SecondOrder(; name, k = 1.0, w = 1.0, d = 1.0, x = nothing, xd = nothing)
+    @named siso = SISO()
+    @unpack u, y = siso
+
+    pars = @parameters begin
+        k = k, [description = "Gain"]
+        w = w, [description = "Bandwidth (angular frequency)"]
+        d = d, [description = "Relative damping"]
     end
-    @parameters begin
-        k = 1.0, [description = "Gain"]
-        w = 1.0, [description = "Bandwidth (angular frequency)"]
-        d = 1.0, [description = "Relative damping"]
+
+    systems = @named begin
     end
-    @equations begin
-        D(x) ~ xd
-        D(xd) ~ w * (w * (k * u - x) - 2 * d * xd)
-        y ~ x
+
+    vars = @variables begin
+        x(t) = x, [description = "State of SecondOrder filter", guess = 0.0]
+        xd(t) = xd, [description = "Derivative state of SecondOrder filter", guess = 0.0]
     end
+
+    equations = Equation[
+        D(x) ~ xd,
+        D(xd) ~ w * (w * (k * u - x) - 2 * d * xd),
+        y ~ x
+    ]
+
+    sys = System(equations, t, vars, pars; name, systems)
+    return extend(sys, siso)
 end
 
 """
@@ -206,29 +241,35 @@ U(s) = k (1 + \\dfrac{1}{sT}) E(S)
 
 See also [`LimPI`](@ref)
 """
-@mtkmodel PI begin
-    @parameters begin
-        k = 1.0, [description = "Proportional gain"]
-        T = 1.0, [description = "Integrator time constant"]
-    end
-    begin
-        @symcheck T > 0 ||
-                  throw(ArgumentError("Time constant `T` has to be strictly positive"))
+@component function PI(; name, k = 1.0, T = 1.0, gainPI__k = nothing)
+    @symcheck T > 0 ||
+              throw(ArgumentError("Time constant `T` has to be strictly positive"))
+
+    pars = @parameters begin
+        k = k, [description = "Proportional gain"]
+        T = T, [description = "Integrator time constant"]
     end
-    @components begin
+
+    systems = @named begin
         err_input = RealInput() # control error
         ctr_output = RealOutput() # control signal
-        gainPI = Gain(; k)
+        gainPI = Gain(; k = gainPI__k)
         addPI = Add()
         int = Integrator(k = 1 / T, x = 0.0)
     end
-    @equations begin
-        connect(err_input, addPI.input1)
-        connect(addPI.output, gainPI.input)
-        connect(gainPI.output, ctr_output)
-        connect(err_input, int.input)
-        connect(int.output, addPI.input2)
+
+    vars = @variables begin
     end
+
+    equations = Equation[
+        connect(err_input, addPI.input1),
+        connect(addPI.output, gainPI.input),
+        connect(gainPI.output, ctr_output),
+        connect(err_input, int.input),
+        connect(int.output, addPI.input2)
+    ]
+
+    return System(equations, t, vars, pars; name, systems)
 end
 
 """
@@ -337,7 +378,7 @@ The simplified expression above is given without the anti-windup protection.
   - `err_input`
   - `ctr_output`
 """
-@component function LimPI(; name, k = 1, T, u_max, u_min = -u_max, Ta, int__x = 0.0)
+@component function LimPI(; name, k = 1, T = nothing, u_max = nothing, u_min = -u_max, Ta = nothing, int__x = 0.0)
     @symcheck Ta > 0 ||
               throw(ArgumentError("Time constant `Ta` has to be strictly positive"))
     @symcheck T > 0 || throw(ArgumentError("Time constant `T` has to be strictly positive"))
@@ -610,7 +651,7 @@ To set the initial state, it's recommended to set the initial condition for `x`,
   - `input`
   - `output`
 
-See also [`StateSpace`](@ref) which handles MIMO systems, as well as [ControlSystemsMTK.jl](https://juliacontrol.github.io/ControlSystemsMTK.jl/stable/) for an interface between [ControlSystems.jl](https://juliacontrol.github.io/ControlSystems.jl/stable/) and ModelingToolkit.jl for advanced manipulation of transfer functions and linear statespace systems. For linearization, see [`linearize`](@ref) and [Linear Analysis](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/linear_analysis/).
+See also [`StateSpace`](@ref) which handles MIMO systems, as well as [ControlSystemsMTK.jl](https://juliacontrol.github.io/ControlSystemsMTK.jl/stable/) for an interface between [ControlSystems.jl](https://juliacontrol.github.io/ControlSystems.jl/stable/) and ModelingToolkitBase.jl for advanced manipulation of transfer functions and linear statespace systems. For linearization, see [`linearize`](@ref) and [Linear Analysis](https://docs.sciml.ai/ModelingToolkitStandardLibrary/stable/API/linear_analysis/).
 """
 @component function TransferFunction(; b = [1], a = [1, 1], name)
     nb = length(b)