Merge branch 'main' into custom_constraints

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "1.0.1"
+version = "1.0.2"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"

docs/Project.toml

@@ -16,4 +16,4 @@ JuMP = "1"
 LinearAlgebra = "1.6"
 Logging = "1.6"
 Plots = "1"
-ModelingToolkit = "~9.41"
+ModelingToolkit = "9.50"

docs/src/manual/mtk.md

@@ -23,7 +23,7 @@ the last section.
     will work for all corner cases.
 
 !!! compat
-    The example only work with `ModelingToolkit.jl` v9.41 releases.
+    The example relies on features and bugfixes of `ModelingToolkit.jl` v9.50.
 
 We first construct and instantiate the pendulum model:
 
@@ -79,16 +79,14 @@ function generate_f_h(model, inputs, outputs)
         end
         return nothing
     end
-    return_inplace = true
     (_, h_ip) = ModelingToolkit.build_explicit_observed_function(
-        io_sys, outputs; inputs, return_inplace
+        io_sys, outputs; inputs, return_inplace = true
     )
-    println(h_ip)
     u_nothing = fill(nothing, nu)
     function h!(y, x, _ , p)
         try
-            # MTK.jl supports a `u` argument in `h_` function but not this package. We set
-            # `u` as a vector of nothing and `h_` function will presumably throw an
+            # MTK.jl supports a `u` argument in `h_ip` function but not this package. We set
+            # `u` as a vector of nothing and `h_ip` function will presumably throw an
             # MethodError it this argument is used inside the function
             h_ip(y, x, u_nothing, p, nothing)
         catch err

docs/src/manual/nonlinmpc.md

@@ -402,9 +402,8 @@ savefig("plot11_NonLinMPC.svg"); nothing # hide
 
 ![plot11_NonLinMPC](plot11_NonLinMPC.svg)
 
-The computations of the successive linearization MPC are about 125 times faster than the
-nonlinear MPC (0.00012 s per time steps versus 0.015 s per time steps, on average), an
-impressive gain for similar closed-loop performances!
+The computations of the successive linearization MPC are about 75 times faster than the
+nonlinear MPC on average, an impressive gain for similar closed-loop performances!
 
 ```@setup 1
 global_logger(old_logger);

src/sim_model.jl

@@ -187,7 +187,7 @@ detailstr(model::SimModel) = ""
 @doc raw"""
     initstate!(model::SimModel, u, d=[]) -> x
 
-Init `model.x0` with manipulated inputs `u` and meas. dist. `d` steady-state and reset time.
+Init `model.x0` with manipulated inputs `u` and meas. dist. `d` steady-state.
 
 The method tries to initialize the model state ``\mathbf{x}`` at steady-state. It removes
 the operating points on `u` and `d` and calls [`steadystate!`](@ref):
@@ -316,7 +316,7 @@ ms. Can be used to implement simple soft real-time simulations, see the example
 
 # Examples
 ```jldoctest
-julia> model = LinModel(tf(2, [0.3, 1]), 0.1);
+julia> model = LinModel(tf(2, [0.3, 1]), 0.25);
 
 julia> function sim_realtime!(model)
            t_0 = time()
@@ -330,8 +330,8 @@ julia> function sim_realtime!(model)
 
 julia> sim_realtime!(model)
 0.0
-0.1
-0.2
+0.25
+0.5
 ```
 """
 function periodsleep(model::SimModel, busywait=false)

test/runtests.jl

@@ -24,5 +24,6 @@ DocMeta.setdocmeta!(
 )
 doctest(ModelPredictiveControl, testset="DocTest")
 
+end;
 
-end;
+nothing