added : Hp and Hc printed for PredictiveController instances

Author: franckgaga

.gitignore

@@ -1,4 +1,5 @@
 /Manifest.toml
 docs/build/
 JuliaSysimage.so
-LocalPreferences.toml
+LocalPreferences.toml
+.vscode/

.vscode/JuliaSysimage.toml

@@ -37,29 +37,29 @@ We first construct the plant model with a sample time $T_s = 1$ s:
 
 ```julia
 using ModelPredictiveControl, ControlSystemsBase
-G = [
-    tf( 2 , [10, 1])*delay(20)
-    tf( 10, [4,  1])
-]
+G = [ tf( 2 , [10, 1])*delay(20)
+      tf( 10, [4,  1]) ]
 Ts = 1.0
 model = LinModel(G, Ts)
 ```
 
-Our goal is controlling the first output, but the second one should never exceed 35:
+Our goal is controlling the first output $y_1$, but the second one $y_2$ should never exceed
+35:
 
 ```julia
 mpc = LinMPC(model, Mwt=[1, 0], Nwt=[0.1])
 mpc = setconstraint!(mpc, ŷmax=[Inf, 35])
 ```
 
-The keyword arguments `Mwt` and `Nwt` are the setpoint tracking and move suppression
-weights, respectively. We can now test `mpc` controller with a setpoint step change and
-display the result using [`Plots.jl`](https://github.com/JuliaPlots/Plots.jl):
+The keyword arguments `Mwt` and `Nwt` are the output setpoint tracking and move suppression
+weights, respectively. A setpoint step change of five tests `mpc` controller in closed-loop.
+The result is displayed with [`Plots.jl`](https://github.com/JuliaPlots/Plots.jl):
 
 ```julia
 using Plots
 ry = [5, 0]
-plot(sim!(mpc, 40, ry), plotry=true, plotŷmax=true)
+res = sim!(mpc, 40, ry)
+plot(res, plotry=true, plotŷmax=true)
 ```
 
 ![StepChangeResponse](/docs/src/assets/readme_result.svg)

.vscode/precompile_control.jl

@@ -135,11 +135,13 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4);
 
 julia> mpc = LinMPC(model, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 LinMPC controller with a sample time Ts = 4.0 s, SteadyKalmanFilter estimator and:
- 1 manipulated inputs u
- 4 states x̂
- 2 measured outputs ym
- 0 unmeasured outputs yu
- 0 measured disturbances d
+ 30 prediction steps Hp
+ 1  control steps Hc
+ 1  manipulated inputs u
+ 4  states x̂
+ 2  measured outputs ym
+ 0  unmeasured outputs yu
+ 0  measured disturbances d
 ```
 
 # Extended Help
@@ -163,11 +165,13 @@ julia> estim = KalmanFilter(LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 4), i_ym=
 
 julia> mpc = LinMPC(estim, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 LinMPC controller with a sample time Ts = 4.0 s, KalmanFilter estimator and:
- 1 manipulated inputs u
- 3 states x̂
- 1 measured outputs ym
- 1 unmeasured outputs yu
- 0 measured disturbances d
+ 30 prediction steps Hp
+ 1  control steps Hc
+ 1  manipulated inputs u
+ 3  states x̂
+ 1  measured outputs ym
+ 1  unmeasured outputs yu
+ 0  measured disturbances d
 ```
 """
 function LinMPC(

.vscode/settings.json

@@ -131,15 +131,17 @@ This method uses the default state estimator :
 
 # Examples
 ```jldoctest
-julia> model = NonLinModel((x,u,_)->0.5x+u, (x,_)->2x, 10, 1, 1, 1);
+julia> model = NonLinModel((x,u,_)->0.5x+u, (x,_)->2x, 10.0, 1, 1, 1);
 
 julia> mpc = NonLinMPC(model, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, UnscentedKalmanFilter estimator and:
- 1 manipulated inputs u
- 2 states x̂
- 1 measured outputs ym
- 0 unmeasured outputs yu
- 0 measured disturbances d
+ 20 prediction steps Hp
+ 1  control steps Hc
+ 1  manipulated inputs u
+ 2  states x̂
+ 1  measured outputs ym
+ 0  unmeasured outputs yu
+ 0  measured disturbances d
 ```
 
 # Extended Help
@@ -170,11 +172,13 @@ julia> estim = UnscentedKalmanFilter(model, σQ_int=[0.05]);
 
 julia> mpc = NonLinMPC(estim, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, UnscentedKalmanFilter estimator and:
- 1 manipulated inputs u
- 2 states x̂
- 1 measured outputs ym
- 0 unmeasured outputs yu
- 0 measured disturbances d
+ 20 prediction steps Hp
+ 1  control steps Hc
+ 1  manipulated inputs u
+ 2  states x̂
+ 1  measured outputs ym
+ 0  unmeasured outputs yu
+ 0  measured disturbances d
 ```
 """
 function NonLinMPC(

README.md

@@ -151,7 +151,7 @@ The [`SteadyKalmanFilter`](@ref) updates it with the precomputed Kalman gain ``\
 
 # Examples
 ```jldoctest
-julia> kf = SteadyKalmanFilter(LinModel(ss(1, 1, 1, 0, 1)));
+julia> kf = SteadyKalmanFilter(LinModel(ss(1, 1, 1, 0, 1.0)));
 
 julia> x̂ = updatestate!(kf, [1], [0]) # x̂[2] is the integrator state (nint_ym argument)
 2-element Vector{Float64}:

src/controller/linmpc.jl

@@ -18,7 +18,7 @@ struct Luenberger <: StateEstimator
     Ĉm  ::Matrix{Float64}
     D̂dm ::Matrix{Float64}
     K::Matrix{Float64}
-    function Luenberger(model, i_ym, nint_ym, Asm, Csm, p)
+    function Luenberger(model, i_ym, nint_ym, Asm, Csm, p̂)
         nu, nx, ny = model.nu, model.nx, model.ny
         nym, nyu = length(i_ym), ny - length(i_ym)
         nxs = size(Asm,1)
@@ -27,9 +27,9 @@ struct Luenberger <: StateEstimator
         Â, B̂u, Ĉ, B̂d, D̂d = augment_model(model, As, Cs)
         Ĉm, D̂dm = Ĉ[i_ym, :], D̂d[i_ym, :] # measured outputs ym only
         K = try
-            place(Â, Ĉ, p, :o)
+            place(Â, Ĉ, p̂, :o)
         catch
-            error("Cannot compute the Luenberger gain L with specified poles p.")        
+            error("Cannot compute the Luenberger gain L with specified poles p̂.")        
         end
         i_ym = collect(i_ym)
         lastu0 = zeros(nu)

src/controller/nonlinmpc.jl

@@ -477,7 +477,6 @@ end
                 yguide    --> "\$y_$i\$"
                 color     --> 3
                 subplot   --> subplot_base + i
-                seriestype --> :steppost
                 linestyle --> :dash
                 linewidth --> 0.75
                 label     --> "\$\\mathbf{r_y}\$"