return named systems

Author: baggepinnen

docs/src/batch_linearization.md

@@ -226,13 +226,11 @@ using Test
 
 However, if we only open at `y` we get controller linearizations that _still contain the closed loop through the scheduling connection_ `v`. We can verify this by looking at what variables are present in the input-output map
 ```@example BATCHLIN
-Cy = named_ss(controllersy[end], x=Symbol.(unknowns(ssy)))
-sminreal(Cy).x
+sminreal(controllersy[end]).x
 ```
 notice how the state of the plant is included in the controller, this is an indication that we didn't fully isolate the controller during the linearizaiton. If we do the same thing for the controller with the loop opened at `u`, we see that the state of the plant is not included in the controller:
 ```@example BATCHLIN
-Cu = named_ss(controllersu[end], x=Symbol.(unknowns(ssu)))
-sminreal(Cu).x
+sminreal(controllersu[end]).x
 ```
 The call to `sminreal` is important here, it removes the states that are not needed to represent the input-output map of the system. The state of the full model, including the plant state, is present in the linearization before this call. 
 

src/ode_system.jl

@@ -54,6 +54,8 @@ function ControlSystemsBase.ss(
     named_ss(sys, inputs, outputs; kwargs...).sys # just discard the names
 end
 
+symstr(x) = Symbol(x isa AnalysisPoint ? x.name : string(x))
+
 
 """
     RobustAndOptimalControl.named_ss(sys::ModelingToolkit.AbstractSystem, inputs, outputs; descriptor=true, kwargs...)
@@ -105,7 +107,6 @@ function RobustAndOptimalControl.named_ss(
         end
     end
     matrices, ssys, xpt = ModelingToolkit.linearize(sys, inputs, outputs; kwargs...)
-    symstr(x) = Symbol(x isa AnalysisPoint ? x.name : string(x))
     unames = symstr.(inputs)
     if nu > 0 && size(matrices.B, 2) == 2nu
         # This indicates that input derivatives are present
@@ -437,7 +438,11 @@ function trajectory_ss(sys, inputs, outputs, sol; t = _max_100(sol.t), allow_inp
 
     # TODO: The value of the output (or input) of the input analysis points should be mapped to the perturbation vars
     perturbation_vars = ModelingToolkit.inputs(ssys)
-    # @show original_inputs = reduce(vcat, unnamespace(ap) for ap in vcat(inputs)) # assuming all inputs are analysis points for now
+    # original_inputs = reduce(vcat, unnamespace(ap) for ap in vcat(inputs)) # assuming all inputs are analysis points for now
+
+    input_names = reduce(vcat, getproperty.(ap.outputs, :u) for ap in vcat(inputs)) 
+    output_names = reduce(vcat, ap.input.u for ap in vcat(outputs)) 
+
     op_nothing = Dict(unknowns(sys) .=> nothing) # Remove all defaults present in the original system
     defs = ModelingToolkit.defaults(sys)
     ops = map(t) do ti
@@ -464,7 +469,11 @@ function trajectory_ss(sys, inputs, outputs, sol; t = _max_100(sol.t), allow_inp
     end
     lins = first.(lins_ops)
     resolved_ops = last.(lins_ops)
-    (; linsystems = [ss(l...) for l in lins], ssys, ops, resolved_ops)
+    named_linsystems = map(lins) do l
+        # Convert to a NamedStateSpace with the same names as the original system
+        named_ss(ss(l.A, l.B, l.C, l.D); name = string(Base.nameof(sys)), x = symstr.(unknowns(ssys)), u = symstr.(input_names), y = symstr.(output_names))
+    end
+    (; linsystems = named_linsystems, ssys, ops, resolved_ops)
 end
 
 "_max_100(t) = length(t) > 100 ? range(extrema(t)..., 100) : t"