Fix minor bugs with linear models

Project.toml

@@ -1,7 +1,7 @@
 name = "RobotDynamics"
 uuid = "38ceca67-d8d3-44e8-9852-78a5596522e1"
 authors = ["Brian Jackson <bjack205@gmail.com>"]
-version = "0.3.3"
+version = "0.3.4"
 
 [deps]
 FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"

src/linearmodel.jl

@@ -155,7 +155,7 @@ function discrete_jacobian!(::Type{PassThrough}, ∇f, model::LinearModel, z::Ab
     ∇f[ix,ix] .= model.A[k]
     ∇f[ix,iu] .= model.B[k]
 
-    nothing
+    true
 end
 
 
@@ -185,6 +185,7 @@ function discrete_jacobian!(::Type{Exponential}, F, model::LinearModel{n,m},
     matrix_exponential!(model, z)
 
     F .= model.E[1:n, 1:n+m]
+    return true
 end
 
 function matrix_exponential!(E::SizedMatrix, A, B, dt, 

src/model.jl

@@ -138,11 +138,14 @@ Create a sparse matrix representing the nonzero elements of the discrete dynamic
 Jacobian. Uses ForwardDiff to compute the Jacobian on `samples` randomly-sampled 
 states and controls.
 """
-function detect_sparsity(::Type{Q}, model::AbstractModel, samples=10) where Q
+function detect_sparsity(::Type{Q}, model::AbstractModel; dt=0.1, samples=10) where Q
     n,m = size(model)
     ∇f = spzeros(n,n+m)
+    if (model isa RobotDynamics.AbstractLinearModel)
+        return ∇f .== 0
+    end
     x,u = rand(model)
-    z = StaticKnotPoint(x,u,0.1)
+    z = StaticKnotPoint(x,u,dt)
     for i = 1:samples  # try several inputs to get the sparsity pattern
         _discrete_jacobian!(ForwardAD(), Q, ∇f, model, z, nothing)
     end