rm JuliaSimCompiler

Project.toml

@@ -7,7 +7,6 @@ version = "0.3.2"
 CoordinateTransformations = "150eb455-5306-5404-9cee-2592286d6298"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-JuliaSimCompiler = "8391cb6b-4921-5777-4e45-fd9aab8cb88d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MeshIO = "7269a6da-0436-5bbc-96c2-40638cbb6118"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
@@ -17,42 +16,40 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
 [weakdeps]
-Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 LightXML = "9c8b4983-aa76-5018-a973-4c85ecc9e179"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377"
 
-
 [extensions]
 Render = ["Makie"]
 URDF = ["LightXML", "Graphs", "MetaGraphsNext", "JuliaFormatter"]
 
 [compat]
 CoordinateTransformations = "0.6"
-DataInterpolations = "5, 6"
+DataInterpolations = "8"
 FileIO = "1"
 Graphs = "1.0"
 JuliaFormatter = "1.0"
-JuliaSimCompiler = "0.1.12"
 LightXML = "0.9"
 LinearAlgebra = "1.6"
 MeshIO = "0.4"
 MetaGraphsNext = "0.7"
-ModelingToolkit = "9"
+ModelingToolkit = "10"
 ModelingToolkitStandardLibrary = "2.7.2"
 Rotations = "1.4"
 SparseArrays = "1"
 StaticArrays = "1"
 julia = "1"
 
 [extras]
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 LightXML = "9c8b4983-aa76-5018-a973-4c85ecc9e179"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377"
+OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
 test = ["OrdinaryDiffEq", "Test", "JuliaFormatter", "LightXML", "Graphs", "MetaGraphsNext"]

docs/Project.toml

@@ -3,7 +3,6 @@ ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 ControlSystemsMTK = "687d7614-c7e5-45fc-bfc3-9ee385575c88"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
-JuliaSimCompiler = "8391cb6b-4921-5777-4e45-fd9aab8cb88d"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 ModelingToolkitStandardLibrary = "16a59e39-deab-5bd0-87e4-056b12336739"

docs/src/examples/free_motion.md

@@ -5,7 +5,7 @@ This example demonstrates how a free-floating [`Body`](@ref) can be simulated. T
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 
 t = Multibody.t
@@ -18,7 +18,7 @@ world = Multibody.world
 eqs = [connect(world.frame_b, freeMotion.frame_a)
        connect(freeMotion.frame_b, body.frame_a)]
 
-@named model = ODESystem(eqs, t,
+@named model = System(eqs, t,
                          systems = [world;
                                     freeMotion;
                                     body])
@@ -60,7 +60,7 @@ eqs = [connect(bar2.frame_a, world.frame_b)
        connect(spring1.frame_a, world.frame_b)
        connect(body.frame_b, spring2.frame_b)]
 
-@named model = ODESystem(eqs, t,
+@named model = System(eqs, t,
                          systems = [
                              world,
                              body,

docs/src/examples/gearbox.md

@@ -10,7 +10,7 @@ The [`GearConstraint`](@ref) has two rotational axes which do not have to be par
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 
 t = Multibody.t
@@ -46,7 +46,7 @@ eqs = [connect(world.frame_b, gearConstraint.bearing)
        connect(mounting1D.flange_b, torque2.support)
        connect(fixed.frame_b, mounting1D.frame_a)]
 
-@named model = ODESystem(eqs, t, systems = [world; systems])
+@named model = System(eqs, t, systems = [world; systems])
 cm = complete(model)
 ssys = structural_simplify(multibody(model))
 prob = ODEProblem(ssys, [

docs/src/examples/gyroscopic_effects.md

@@ -11,7 +11,7 @@ The system consists of a pendulum suspended in a spherical joint, a joint withou
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 
 t = Multibody.t
@@ -36,7 +36,7 @@ connections = [
     connect(trans.frame_b, body2.frame_a)
 ]
 
-@named model = ODESystem(connections, t, systems = [world; systems])
+@named model = System(connections, t, systems = [world; systems])
 model = complete(model)
 ssys = structural_simplify(multibody(model))
 

docs/src/examples/kinematic_loops.md

@@ -29,7 +29,7 @@ import ModelingToolkitStandardLibrary.Mechanical.Rotational
 using Plots
 using OrdinaryDiffEq
 using LinearAlgebra
-using JuliaSimCompiler
+# using JuliaSimCompiler
 
 t = Multibody.t
 D = Differential(t)
@@ -73,7 +73,7 @@ connections = [
     connect(j2.support, damper2.flange_b)
 
 ]
-@named fourbar = ODESystem(connections, t, systems = [world; systems])
+@named fourbar = System(connections, t, systems = [world; systems])
 fourbar = complete(fourbar)
 ssys = structural_simplify(multibody(fourbar))
 prob = ODEProblem(ssys, [fourbar.j1.phi => 0.1], (0.0, 10.0))
@@ -144,7 +144,7 @@ connections = [connect(j2.frame_b, b2.frame_a)
                connect(b0.frame_a, world.frame_b)
                connect(b0.frame_b, j2.frame_a)
                ]
-@named fourbar2 = ODESystem(connections, t, systems = [world; systems])
+@named fourbar2 = System(connections, t, systems = [world; systems])
 fourbar2 = complete(fourbar2)
 ssys = structural_simplify(multibody(fourbar2))
 
@@ -185,7 +185,7 @@ connections = [connect(j2.frame_b, b2.frame_a)
                connect(b3.frame_b, j2.frame_a)
 ]
 
-@named fourbar_analytic = ODESystem(connections, t, systems = [world; systems])
+@named fourbar_analytic = System(connections, t, systems = [world; systems])
 fourbar_analytic = complete(fourbar_analytic)
 ssys_analytic = structural_simplify(multibody(fourbar_analytic))
 prob = ODEProblem(ssys_analytic, [], (0.0, 1.4399)) 

docs/src/examples/pendulum.md

@@ -4,7 +4,7 @@ This beginners tutorial will start by modeling a pendulum pivoted around the ori
 To start, we load the required packages
 ```@example pendulum
 using ModelingToolkit
-using Multibody, JuliaSimCompiler
+using Multibody
 using OrdinaryDiffEq # Contains the ODE solver we will use
 using Plots
 ```
@@ -39,13 +39,13 @@ connections = [
 nothing # hide
 ```
 
-With all components and connections defined, we can create an `ODESystem` like so:
+With all components and connections defined, we can create an `System` like so:
 ```@example pendulum
-@named model = ODESystem(connections, t, systems=[world, joint, body])
+@named model = System(connections, t, systems=[world, joint, body])
 model = complete(model)
 nothing # hide
 ```
-The `ODESystem` is the fundamental model type in ModelingToolkit used for multibody-type models.
+The `System` is the fundamental model type in ModelingToolkit used for multibody-type models.
 
 Before we can simulate the system, we must perform model check using the function [`multibody`](@ref) and compilation using [`structural_simplify`](@ref)
 ```@example pendulum
@@ -92,7 +92,7 @@ connections = [connect(world.frame_b, joint.frame_a)
                connect(joint.support, damper.flange_a)
                connect(body.frame_a, joint.frame_b)]
 
-@named model = ODESystem(connections, t, systems = [world, joint, body, damper])
+@named model = System(connections, t, systems = [world, joint, body, damper])
 model = complete(model)
 ssys = structural_simplify(multibody(model))
 
@@ -125,7 +125,7 @@ connections = [connect(world.frame_b, joint.frame_a)
                connect(joint.support, damper.flange_a, spring.flange_a)
                connect(body_0.frame_a, joint.frame_b)]
 
-@named model = ODESystem(connections, t, systems = [world, joint, body_0, damper, spring])
+@named model = System(connections, t, systems = [world, joint, body_0, damper, spring])
 model = complete(model)
 ssys = structural_simplify(multibody(model))
 
@@ -152,7 +152,7 @@ In the example above, we introduced a prismatic joint to model the oscillating m
 connections = [connect(world.frame_b, multibody_spring.frame_a)
                 connect(root_body.frame_a, multibody_spring.frame_b)]
 
-@named model = ODESystem(connections, t, systems = [world, multibody_spring, root_body])
+@named model = System(connections, t, systems = [world, multibody_spring, root_body])
 model = complete(model)
 ssys = structural_simplify(multibody(model))
 
@@ -170,7 +170,7 @@ Internally, the [`Multibody.Spring`](@ref) contains a `Translational.Spring`, at
 push!(connections, connect(multibody_spring.spring2d.flange_a, damper.flange_a))
 push!(connections, connect(multibody_spring.spring2d.flange_b, damper.flange_b))
 
-@named model = ODESystem(connections, t, systems = [world, multibody_spring, root_body, damper])
+@named model = System(connections, t, systems = [world, multibody_spring, root_body, damper])
 model = complete(model)
 ssys = structural_simplify(multibody(model))
 prob = ODEProblem(ssys, defs, (0, 10))
@@ -187,7 +187,7 @@ The systems we have modeled so far have all been _planar_ mechanisms. We now ext
 This pendulum, sometimes referred to as a _rotary pendulum_, has two joints, one in the "shoulder", which is typically configured to rotate around the gravitational axis, and one in the "elbow", which is typically configured to rotate around the axis of the upper arm. The upper arm is attached to the shoulder joint, and the lower arm is attached to the elbow joint. The tip of the pendulum is attached to the lower arm.
 
 ```@example pendulum
-using ModelingToolkit, Multibody, JuliaSimCompiler, OrdinaryDiffEq, Plots
+using ModelingToolkit, Multibody, OrdinaryDiffEq, Plots
 import ModelingToolkitStandardLibrary.Mechanical.Rotational.Damper as RDamper
 import Multibody.Rotations
 
@@ -426,7 +426,7 @@ lsys = named_ss(multibody(cp), inputs, outputs; op) # identical to linearize, bu
 ### LQR and LQG Control design
 With a linear statespace object in hand, we can proceed to design an LQR or LQG controller. We will design both an LQR and an LQG controller in order to demonstrate two possible workflows.
 
-The LQR controller is designed using the function `ControlSystemsBase.lqr`, and it takes the two cost matrices `Q1` and `Q2` penalizing state deviation and control action respectively. The LQG controller is designed using [`RobustAndOptimalControl.LQGProblem`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/#LQG-design), and this function additionally takes the covariance matrices `r1, R2` for a Kalman filter. Before we call `LQGProblem` we partition the linearized system into an [`ExtendedStateSpace`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/api/#RobustAndOptimalControl.ExtendedStateSpace) object, this indicates which inputs of the system are available for control and which are considered disturbances, and which outputs of the system are available for measurement. In this case, we assume that we have access to the cart position and the pendulum angle, and we control the cart position. The remaining two outputs are still important for the performance, but we cannot measure them and will rely on the Kalman filter to estimate them. When we call [`extended_controller`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/api/#RobustAndOptimalControl.extended_controller) we get a linear system that represents the combined state estimator and state feedback controller. This linear system is then converted to an `ODESystem` by the function `LQGSystem`.
+The LQR controller is designed using the function `ControlSystemsBase.lqr`, and it takes the two cost matrices `Q1` and `Q2` penalizing state deviation and control action respectively. The LQG controller is designed using [`RobustAndOptimalControl.LQGProblem`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/#LQG-design), and this function additionally takes the covariance matrices `r1, R2` for a Kalman filter. Before we call `LQGProblem` we partition the linearized system into an [`ExtendedStateSpace`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/api/#RobustAndOptimalControl.ExtendedStateSpace) object, this indicates which inputs of the system are available for control and which are considered disturbances, and which outputs of the system are available for measurement. In this case, we assume that we have access to the cart position and the pendulum angle, and we control the cart position. The remaining two outputs are still important for the performance, but we cannot measure them and will rely on the Kalman filter to estimate them. When we call [`extended_controller`](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/api/#RobustAndOptimalControl.extended_controller) we get a linear system that represents the combined state estimator and state feedback controller. This linear system is then converted to an `System` by the function `LQGSystem`.
 
 Since the function `lqr` operates on the state vector, and we have access to the specified output vector, we make use of the system ``C`` matrix to reformulate the problem in terms of the outputs. This relies on the ``C`` matrix being full rank, which is the case here since our outputs include a complete state realization of the system. This is of no concern when using the `LQGProblem` structure since we penalize outputs rather than the state in this case. 
 
@@ -449,7 +449,7 @@ z = [:x] # The output for which we want to have unit static gain
 Ce, cl = extended_controller(lqg, z = RobustAndOptimalControl.names2indices(z, Pn.y))
 dc_gain_compensation = inv((Pn[:x, :].C*dcgain(cl)')[]) # Multiplier that makes the static gain from references to cart position unity
 
-LQGSystem(args...; kwargs...) = ODESystem(Ce; kwargs...)
+LQGSystem(args...; kwargs...) = System(Ce; kwargs...)
 
 @mtkmodel CartWithFeedback begin
     @components begin

docs/src/examples/prescribed_pose.md

@@ -25,7 +25,7 @@ using ModelingToolkit
 using Plots
 using OrdinaryDiffEq
 using LinearAlgebra
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using Test
 
 t = Multibody.t

docs/src/examples/quad.md

@@ -12,7 +12,7 @@ using ModelingToolkit
 using ModelingToolkitStandardLibrary.Blocks
 using LinearAlgebra
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 using Test
 t = Multibody.t
@@ -183,7 +183,7 @@ function RotorCraft(; closed_loop = true, addload=true, L=nothing, outputs = not
         end
 
     end
-    @named model = ODESystem(connections, t; systems)
+    @named model = System(connections, t; systems)
     complete(model)
 end
 model = RotorCraft(closed_loop=true, addload=true, pid=true)

docs/src/examples/robot.md

@@ -6,7 +6,7 @@
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 using Test
 
@@ -109,7 +109,7 @@ The coordinates of any point on the mechanism may be obtained in the world coord
 
 ```@example robot
 output = collect(robot.mechanics.b6.frame_b.r_0)
-fkine = JuliaSimCompiler.build_explicit_observed_function(ssys, output)
+fkine = build_explicit_observed_function(ssys, output)
 fkine(prob.u0, prob.p, 0)
 ```
 
@@ -124,7 +124,7 @@ prob[output]
 
 or by building an explicit function `(state, parameters, time) -> output`
 ```@example robot
-fkine = JuliaSimCompiler.build_explicit_observed_function(ssys, output)
+fkine = build_explicit_observed_function(ssys, output)
 fkine(prob.u0, prob.p, 0)
 ```
 !!! note

docs/src/examples/ropes_and_cables.md

@@ -15,7 +15,7 @@ We start by simulating a stiff rope that is attached to the world in one end and
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 using Test
 t = Multibody.t
@@ -28,7 +28,7 @@ number_of_links = 6
 connections = [connect(world.frame_b, rope.frame_a)
                connect(rope.frame_b, body.frame_a)]
 
-@named stiff_rope = ODESystem(connections, t, systems = [world, body, rope])
+@named stiff_rope = System(connections, t, systems = [world, body, rope])
 
 stiff_rope = complete(stiff_rope)
 ssys = structural_simplify(multibody(stiff_rope))
@@ -52,7 +52,7 @@ number_of_links = 6
 connections = [connect(world.frame_b, rope.frame_a)
                connect(rope.frame_b, body.frame_a)]
 
-@named flexible_rope = ODESystem(connections, t, systems = [world, body, rope])
+@named flexible_rope = System(connections, t, systems = [world, body, rope])
 
 flexible_rope = complete(flexible_rope)
 ssys = structural_simplify(multibody(flexible_rope))
@@ -86,7 +86,7 @@ connections = [connect(world.frame_b, fixed.frame_a, chain.frame_a)
                connect(chain.frame_b, spring.frame_a)
                connect(spring.frame_b, fixed.frame_b)]
 
-@named mounted_chain = ODESystem(connections, t, systems = [systems; world])
+@named mounted_chain = System(connections, t, systems = [systems; world])
 mounted_chain = complete(mounted_chain)
 ssys = structural_simplify(multibody(mounted_chain))
 prob = ODEProblem(ssys, [

docs/src/examples/sensors.md

@@ -6,7 +6,7 @@ The example below adds a force and a torque sensor to the pivot point of a pendu
 ```@example sensor
 using Multibody
 using ModelingToolkit
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using Plots
 using OrdinaryDiffEq
 using LinearAlgebra
@@ -29,7 +29,7 @@ connections = [connect(world.frame_b, joint.frame_a)
                connect(torquesensor.frame_b, forcesensor.frame_a)
                connect(forcesensor.frame_b, body.frame_a)]
 
-@named model = ODESystem(connections, t,
+@named model = System(connections, t,
                          systems = [world, joint, body, torquesensor, forcesensor])
 ssys = structural_simplify(multibody(model))
 

docs/src/examples/space.md

@@ -18,7 +18,7 @@ In this example, we set ``\mu = 1``, `point_gravity = true` and let two masses o
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 
 t = Multibody.t

docs/src/examples/spherical_pendulum.md

@@ -9,7 +9,7 @@ This example models a spherical pendulum. The pivot point is modeled using a [`S
 using Multibody
 using ModelingToolkit
 using Plots
-using JuliaSimCompiler
+# using JuliaSimCompiler
 using OrdinaryDiffEq
 
 t = Multibody.t
@@ -25,7 +25,7 @@ connections = [connect(world.frame_b, joint.frame_a)
             connect(joint.frame_b, bar.frame_a)
             connect(bar.frame_b, body.frame_a)]
 
-@named model = ODESystem(connections, t, systems = [world; systems])
+@named model = System(connections, t, systems = [world; systems])
 model = complete(model)
 ssys = structural_simplify(multibody(model))