Adapt controlsystems for use of parameter arrays

Author: AlCap23

src/systems/control/controlsystem.jl

@@ -160,8 +160,8 @@ function runge_kutta_discretize(sys::ControlSystem,dt,tspan;
     control_equality = reduce(vcat,[control_timeseries[i][end] .~ control_timeseries[i+1][1] for i in 1:n-1])
 
     # Create the loss function
-    losses = [Base.invokelatest(L,states_timeseries[i],control_timeseries[i][1],(ps,),(iv,)) for i in 1:n]
-    losses = vcat(losses,[Base.invokelatest(L,states_timeseries[n+1],control_timeseries[n][end],(ps,),(iv,))])
+    losses = [Base.invokelatest(L,states_timeseries[i],control_timeseries[i][1],ps,iv) for i in 1:n]
+    losses = vcat(losses,[Base.invokelatest(L,states_timeseries[n+1],control_timeseries[n][end],ps,iv)])
 
     # Calculate final pieces
     equalities = vcat(stages,updates,control_equality)

test/controlsystem.jl

@@ -1,19 +1,20 @@
 using ModelingToolkit
 
 @variables t x(t) v(t) u(t)
-@parameters p
+@parameters p[1:1]
 @derivatives D'~t
 
 loss = (4-x)^2 + 2v^2 + u^2
 eqs = [
-    D(x) ~ v
-    D(v) ~ p*u^3
+    D(x) ~ v #- p[2]*x
+    D(v) ~ p[1]*u^3 + v
 ]
 
-sys = ControlSystem(loss,eqs,t,[x,v],[u],[p])
+sys = ControlSystem(loss,eqs,t,[x,v],[u],p)
 dt = 0.1
 tspan = (0.0,1.0)
 sys = runge_kutta_discretize(sys,dt,tspan)
 
 u0 = rand(112) # guess for the state values
 prob = OptimizationProblem(sys,u0,[0.1],grad=true)
+