up tuto mpc

Author: ocots

docs/extras/mpc.jl

@@ -1,7 +1,13 @@
 using Pkg
 Pkg.activate("docs") 
 
-using OptimalControl, NLPModelsIpopt, Plots, OrdinaryDiffEq, LinearAlgebra, Plots.PlotMeasures
+using LinearAlgebra
+using NLPModelsIpopt
+using OptimalControl
+using OrdinaryDiffEq
+using Plots
+using Plots.PlotMeasures
+using Printf
 
 t0 = 0.
 x0 = 0. 
@@ -255,7 +261,6 @@ function MPC(t0, x0, y0, θ0, xf, yf, θf, current)
             t2 = t1 + Δt
         else
             t2 = tf
-            println("t2=tf: ", t2)
             stop = true
         end
 
@@ -271,7 +276,15 @@ function MPC(t0, x0, y0, θ0, xf, yf, θf, current)
 
         # Calculate the distance to the target position
         distance = norm([x1, y1, θ1] - [xf, yf, θf])
-        println("N: ", N, "\t distance: ", distance, "\t iterations: ", iter, "\t constraints: ", cons, "\t tf: ", tf)
+        if N == 1
+            println("     N    Distance  Iterations   Constraints       tf")
+            println("------------------------------------------------------")
+        end
+        @printf("%6d", N)
+        @printf("%12.4f", distance)
+        @printf("%12d", iter)
+        @printf("%14.4e", cons)
+        @printf("%10.4f\n", tf)
         if !((distance > ε) && (N < Nmax))
             stop = true
         end
@@ -284,7 +297,7 @@ function MPC(t0, x0, y0, θ0, xf, yf, θf, current)
     return data
 end
 
-data = MPC(t0, x0, y0, θ0, xf, yf, θf, current)
+data = MPC(t0, x0, y0, θ0, xf, yf, θf, current);
 
 # Trajectory
 plt_q = plot(xlims=(-2, 6), ylims=(-1, 8), aspect_ratio=1, xlabel="x", ylabel="y")

docs/make.jl

@@ -8,7 +8,7 @@ cp("./docs/Project.toml", "./docs/src/assets/Project.toml"; force=true)
 repo_url = "github.com/control-toolbox/Tutorials.jl"
 
 makedocs(;
-    draft=true, # if draft is true, then the julia code from .md is not executed
+    draft=false, # if draft is true, then the julia code from .md is not executed
     warnonly=[:cross_references, :autodocs_block],
     sitename="Tutorials",
     format=Documenter.HTML(;

docs/src/tutorial-mpc.md

@@ -26,7 +26,13 @@ The angular velocity is limited and normalized: $\|u(t)\| \leq 1$. There are bou
 ## Data 
 
 ```@example main-mpc
-using OptimalControl, NLPModelsIpopt, Plots, OrdinaryDiffEq, LinearAlgebra, Plots.PlotMeasures
+using LinearAlgebra
+using NLPModelsIpopt
+using OptimalControl
+using OrdinaryDiffEq
+using Plots
+using Plots.PlotMeasures
+using Printf
 
 t0 = 0.
 x0 = 0. 
@@ -308,7 +314,6 @@ function MPC(t0, x0, y0, θ0, xf, yf, θf, current)
             t2 = t1 + Δt
         else
             t2 = tf
-            println("t2=tf: ", t2)
             stop = true
         end
 
@@ -324,7 +329,15 @@ function MPC(t0, x0, y0, θ0, xf, yf, θf, current)
 
         # Calculate the distance to the target position
         distance = norm([x1, y1, θ1] - [xf, yf, θf])
-        println("N: ", N, "\t distance: ", distance, "\t iterations: ", iter, "\t constraints: ", cons, "\t tf: ", tf)
+        if N == 1
+            println("     N    Distance  Iterations   Constraints       tf")
+            println("------------------------------------------------------")
+        end
+        @printf("%6d", N)
+        @printf("%12.4f", distance)
+        @printf("%12d", iter)
+        @printf("%14.4e", cons)
+        @printf("%10.4f\n", tf)
         if !((distance > ε) && (N < Nmax))
             stop = true
         end