update other files

Author: baggepinnen

docs/src/batch_linearization.md

@@ -25,7 +25,7 @@ eqs = [D(x) ~ v
        y.u ~ x]
 
 
-@named duffing = ODESystem(eqs, t, systems=[y, u])
+@named duffing = System(eqs, t, systems=[y, u])
 ```
 
 ## Batch linearization
@@ -111,13 +111,13 @@ plot(layout=2)
 
 # Simulate each individual controller
 for C in Cs
-    @named Ci = ODESystem(C)
+    @named Ci = System(C)
     eqs = [
         closed_loop_eqs
         connect(fb.output, Ci.input)
         connect(Ci.output, duffing.u)
     ]
-    @named closed_loop = ODESystem(eqs, t, systems=[duffing, Ci, fb, ref, F])
+    @named closed_loop = System(eqs, t, systems=[duffing, Ci, fb, ref, F])
     prob = ODEProblem(structural_simplify(closed_loop), [F.xd => 0], (0.0, 8.0))
     sol = solve(prob, Rodas5P(), abstol=1e-8, reltol=1e-8)
     plot!(sol, idxs=[duffing.y.u, duffing.u.u], layout=2, lab="")
@@ -131,7 +131,7 @@ eqs = [
     connect(Cgs.output, duffing.u)
     connect(duffing.y, Cgs.scheduling_input) # Don't forget to connect the scheduling variable!
 ]
-@named closed_loop = ODESystem(eqs, t, systems=[duffing, Cgs, fb, ref, F])
+@named closed_loop = System(eqs, t, systems=[duffing, Cgs, fb, ref, F])
 prob = ODEProblem(structural_simplify(closed_loop), [F.xd => 0], (0.0, 8.0))
 sol = solve(prob, Rodas5P(), abstol=1e-8, reltol=1e-8, initializealg=SciMLBase.NoInit())
 plot!(sol, idxs=[duffing.y.u, duffing.u.u], l=(2, :red), lab="Gain scheduled")

docs/src/index.md

@@ -21,7 +21,7 @@ pkg> add ControlSystemsMTK
 
 
 ## From ControlSystems to ModelingToolkit
-Simply calling `ODESystem(sys)` converts a `StateSpace` object from ControlSystems into the corresponding [`ModelingToolkitStandardLibrary.Blocks.StateSpace`](http://mtkstdlib.sciml.ai/dev/API/blocks/#ModelingToolkitStandardLibrary.Blocks.StateSpace). If `sys` is a [named statespace object](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/#Named-systems), the names of inputs and outputs will be retained in the `ODESystem` as connectors, that is, if `my_input` is an input variable in the named statespace object, `my_input` will be a connector of type `RealInput` in the resulting ODESystem. Names of state variables are currently ignored.
+Simply calling `System(sys)` converts a `StateSpace` object from ControlSystems into the corresponding [`ModelingToolkitStandardLibrary.Blocks.StateSpace`](http://mtkstdlib.sciml.ai/dev/API/blocks/#ModelingToolkitStandardLibrary.Blocks.StateSpace). If `sys` is a [named statespace object](https://juliacontrol.github.io/RobustAndOptimalControl.jl/dev/#Named-systems), the names of inputs and outputs will be retained in the `System` as connectors, that is, if `my_input` is an input variable in the named statespace object, `my_input` will be a connector of type `RealInput` in the resulting System. Names of state variables are currently ignored.
 
 ### Example:
 
@@ -41,7 +41,7 @@ D =
 
 Continuous-time state-space model
 
-julia> @named P = ODESystem(P0)
+julia> @named P = System(P0)
 Model P with 2 equations
 States (3):
   x[1](t) [defaults to 0.0]
@@ -65,15 +65,15 @@ using ControlSystemsMTK, ControlSystemsBase, ModelingToolkit, RobustAndOptimalCo
 P = named_ss(DemoSystems.double_mass_model(outputs = [1,3]), u=:torque, y=[:motor_angle, :load_angle])
 ```
 
-When we convert this system to an ODESystem, we get a system with connectors `P.torque` and `P.motor_angle`, in addition to the standard connectors `P.input` and `P.output`:
+When we convert this system to a `System`, we get a system with connectors `P.torque` and `P.motor_angle`, in addition to the standard connectors `P.input` and `P.output`:
 ```@example CONNECT
-@named P_ode = ODESystem(P)
+@named P_ode = System(P)
 ```
 Here, `P.torque` is equal to `P.input`, so you may choose to connect to either of them. However, since the output is multivariable, the connector `P.output` represents both outputs, while `P.motor_angle` and `P.load_angle` represent the individual scalar outputs.
 
 
 ## From ModelingToolkit to ControlSystems
-An `ODESystem` can be converted to a named statespace object from [RobustAndOptimalControl.jl](https://github.com/JuliaControl/RobustAndOptimalControl.jl) by calling [`named_ss`](@ref)
+A `System` can be converted to a named statespace object from [RobustAndOptimalControl.jl](https://github.com/JuliaControl/RobustAndOptimalControl.jl) by calling [`named_ss`](@ref)
 
 ```julia
 named_ss(ode_sys, inputs, outputs; op)
@@ -167,9 +167,9 @@ function SystemModel(u=nothing; name=:model)
     ]
     if u !== nothing 
         push!(eqs, connect(u.output, :u, torque.tau))
-        return @named model = ODESystem(eqs, t; systems = [sens, torque, inertia1, inertia2, spring, damper, u])
+        return @named model = System(eqs, t; systems = [sens, torque, inertia1, inertia2, spring, damper, u])
     end
-    ODESystem(eqs, t; systems = [sens, torque, inertia1, inertia2, spring, damper], name)
+    System(eqs, t; systems = [sens, torque, inertia1, inertia2, spring, damper], name)
 end
 
 model = SystemModel() |> complete

src/ControlSystemsMTK.jl

@@ -3,7 +3,7 @@ using RobustAndOptimalControl: NamedStateSpace
 #=
 Ideas: All connections handled by ModelingToolkit.
 Names: 
-- handled either by named system, or directly in constructor to ODESystem. 
+- handled either by named system, or directly in constructor to System. 
 Functions: 
 - Give me linear system from [u1, u3] to [qm, a]
 If the linearization of a full system produces a named system, one could implement getindex for vectors of names and obtain the desired transfer functions.
@@ -20,7 +20,7 @@ using ModelingToolkit, ControlSystemsBase
 using ControlSystemsBase: ssdata, AbstractStateSpace, Continuous, nstates, noutputs, ninputs
 # using ControlSystemIdentification
 using RobustAndOptimalControl, MonteCarloMeasurements
-import ModelingToolkit: ODESystem, FnType, Symbolics
+import ModelingToolkit: System, FnType, Symbolics
 using ModelingToolkit: unknowns, observed, isdifferential
 using Symbolics
 using Symbolics: jacobian, solve_for
@@ -29,7 +29,7 @@ using UnPack
 
 # using SymbolicControlSystems
 
-export feedback, ODESystem, unknowns, observed, named_ss
+export feedback, System, unknowns, observed, named_ss
 export batch_ss, trajectory_ss, GainScheduledStateSpace
 export build_quadratic_cost_matrix
 

test/runtests.jl

@@ -2,8 +2,8 @@ using ControlSystemsMTK
 using Test
 
 @testset "ControlSystemsMTK.jl" begin
-    @testset "ODESystem" begin
-        @info "Testing ODESystem"        
+    @testset "System" begin
+        @info "Testing System"        
         include("test_ODESystem.jl")
     end
 

test/test_batchlin.jl

@@ -19,7 +19,7 @@ eqs = [D(x) ~ v
        y.u ~ x]
 
 
-@named duffing = ODESystem(eqs, t, systems=[y, u])
+@named duffing = System(eqs, t, systems=[y, u])
 
 bounds = getbounds(duffing, unknowns(duffing))
 sample_within_bounds((l, u)) = (u - l) * rand() + l
@@ -39,7 +39,7 @@ Ps, ssys = batch_ss(duffing, inputs, outputs , ops)
 
 using DataInterpolations
 @named Cgs = GainScheduledStateSpace(Ps, xs, interpolator=LinearInterpolation)
-@test Cgs isa ODESystem
+@test Cgs isa System
 # This is tested better in the docs
 
 ## C-code generation
@@ -53,7 +53,7 @@ import ModelingToolkitStandardLibrary.Blocks
 @named fb = Blocks.Add(k2=-1)
 @named ref = Blocks.Square(frequency=1/6, amplitude=0.5, offset=0.5, start_time=1)
 @named F = Blocks.SecondOrder(w=10, d=0.7)
-@named C = ODESystem(pid(1,1,0; state_space=true, Tf=0.01))
+@named C = System(pid(1,1,0; state_space=true, Tf=0.01))
 
 
 closed_loop_eqs = [
@@ -64,7 +64,7 @@ closed_loop_eqs = [
     ModelingToolkit.connect(C.output, duffing.u)
 ]
 
-@named closed_loop = ODESystem(closed_loop_eqs, t, systems=[duffing, C, fb, ref, F])
+@named closed_loop = System(closed_loop_eqs, t, systems=[duffing, C, fb, ref, F])
 
 ssys = structural_simplify(closed_loop)
 prob = ODEProblem(ssys, [F.xd => 0], (0.0, 8.0))