added : show integrators info when printing StateEstimator and PredictiveController

Author: franckgaga

docs/src/index.md

@@ -10,8 +10,8 @@ The objective is to provide a simple and clear framework to quickly design model
 controllers (MPCs) in Julia, while preserving the flexibility for advanced real-time
 optimization. Modern MPCs based on closed-loop state estimators are the main focus of the
 package, but classical approaches that rely on internal models are also possible. The
-`JuMP.jl` interface allows to easily test different solvers if the performance of the
-default settings is not satisfactory.
+`JuMP.jl` interface allows the user to test different solvers easily if the performance of
+the default settings is not satisfactory.
 
 The documentation is divided in two parts:
 

src/controller/explicitmpc.jl

@@ -105,9 +105,9 @@ julia> mpc = ExplicitMPC(model, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 ExplicitMPC controller with a sample time Ts = 4.0 s, SteadyKalmanFilter estimator and:
  30 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   4 states x̂
-  2 measured outputs ym
+  2 measured outputs ym (2 integrators)
   0 unmeasured outputs yu
   0 measured disturbances d
 ```
@@ -130,9 +130,9 @@ julia> mpc = ExplicitMPC(estim, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 ExplicitMPC controller with a sample time Ts = 4.0 s, KalmanFilter estimator and:
  30 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   3 states x̂
-  1 measured outputs ym
+  1 measured outputs ym (1 integrators)
   1 unmeasured outputs yu
   0 measured disturbances d
 ```

src/controller/linmpc.jl

@@ -121,9 +121,9 @@ julia> mpc = LinMPC(model, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SteadyKalmanFilter estimator and:
  30 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   4 states x̂
-  2 measured outputs ym
+  2 measured outputs ym (2 integrators)
   0 unmeasured outputs yu
   0 measured disturbances d
 ```
@@ -168,9 +168,9 @@ julia> mpc = LinMPC(estim, Mwt=[0, 1], Nwt=[0.5], Hp=30, Hc=1)
 LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, KalmanFilter estimator and:
  30 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   3 states x̂
-  1 measured outputs ym
+  1 measured outputs ym (1 integrators)
   1 unmeasured outputs yu
   0 measured disturbances d
 ```

src/controller/nonlinmpc.jl

@@ -135,9 +135,9 @@ julia> mpc = NonLinMPC(model, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedKalmanFilter estimator and:
  20 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   2 states x̂
-  1 measured outputs ym
+  1 measured outputs ym (1 integrators)
   0 unmeasured outputs yu
   0 measured disturbances d
 ```
@@ -172,9 +172,9 @@ julia> mpc = NonLinMPC(estim, Hp=20, Hc=1, Cwt=1e6)
 NonLinMPC controller with a sample time Ts = 10.0 s, Ipopt optimizer, UnscentedKalmanFilter estimator and:
  20 prediction steps Hp
   1 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   2 states x̂
-  1 measured outputs ym
+  1 measured outputs ym (1 integrators)
   0 unmeasured outputs yu
   0 measured disturbances d
 ```

src/estimator/internal_model.jl

@@ -236,5 +236,16 @@ function evaloutput(estim::InternalModel, ym, d=Float64[])
     return ŷ
 end
 
+"Print InternalModel information without i/o integrators."
+function print_estim_dim(io::IO, estim::InternalModel, n)
+    nu, nd = estim.model.nu, estim.model.nd
+    nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
+    println(io, "$(lpad(nu, n)) manipulated inputs u")
+    println(io, "$(lpad(nx̂, n)) states x̂")
+    println(io, "$(lpad(nym, n)) measured outputs ym")
+    println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
+    print(io,   "$(lpad(nd, n)) measured disturbances d")
+end
+
 (estim::InternalModel)(ym, d=Float64[]) = evaloutput(estim::InternalModel, ym, d)
 

src/estimator/kalman.jl

@@ -108,9 +108,9 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = SteadyKalmanFilter(model, i_ym=[2], σR=[1], σQint_ym=[0.01])
 SteadyKalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
- 1 manipulated inputs u
+ 1 manipulated inputs u (0 integrators)
  3 states x̂
- 1 measured outputs ym
+ 1 measured outputs ym (1 integrators)
  1 unmeasured outputs yu
  0 measured disturbances d
 ```
@@ -260,9 +260,9 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = KalmanFilter(model, i_ym=[2], σR=[1], σP0=[100, 100], σQint_ym=[0.01])
 KalmanFilter estimator with a sample time Ts = 0.5 s, LinModel and:
- 1 manipulated inputs u
+ 1 manipulated inputs u (0 integrators)
  3 states x̂
- 1 measured outputs ym
+ 1 measured outputs ym (1 integrators)
  1 unmeasured outputs yu
  0 measured disturbances d
 ```
@@ -418,9 +418,9 @@ julia> model = NonLinModel((x,u,_)->0.1x+u, (x,_)->2x, 10.0, 1, 1, 1);
 
 julia> estim = UnscentedKalmanFilter(model, σR=[1], nint_ym=[2], σP0int_ym=[1, 1])
 UnscentedKalmanFilter estimator with a sample time Ts = 10.0 s, NonLinModel and:
- 1 manipulated inputs u
+ 1 manipulated inputs u (0 integrators)
  3 states x̂
- 1 measured outputs ym
+ 1 measured outputs ym (2 integrators)
  0 unmeasured outputs yu
  0 measured disturbances d
 ```
@@ -646,9 +646,9 @@ julia> model = NonLinModel((x,u,_)->0.2x+u, (x,_)->-3x, 5.0, 1, 1, 1);
 
 julia> estim = ExtendedKalmanFilter(model, σQ=[2], σQint_ym=[2], σP0=[0.1], σP0int_ym=[0.1])
 ExtendedKalmanFilter estimator with a sample time Ts = 5.0 s, NonLinModel and:
- 1 manipulated inputs u
+ 1 manipulated inputs u (0 integrators)
  2 states x̂
- 1 measured outputs ym
+ 1 measured outputs ym (1 integrators)
  0 unmeasured outputs yu
  0 measured disturbances d
 ```

src/estimator/luenberger.jl

@@ -70,9 +70,9 @@ julia> model = LinModel([tf(3, [30, 1]); tf(-2, [5, 1])], 0.5);
 
 julia> estim = Luenberger(model, nint_ym=[1, 1], p̂=[0.61, 0.62, 0.63, 0.64])
 Luenberger estimator with a sample time Ts = 0.5 s, LinModel and:
- 1 manipulated inputs u
+ 1 manipulated inputs u (0 integrators)
  4 states x̂
- 2 measured outputs ym
+ 2 measured outputs ym (2 integrators)
  0 unmeasured outputs yu
  0 measured disturbances d
 ```

src/predictive_control.jl

@@ -96,9 +96,9 @@ julia> mpc = setconstraint!(mpc, Δumin=[-10], Δumax=[+10], c_Δumin=[1.0], c_
 LinMPC controller with a sample time Ts = 4.0 s, OSQP optimizer, SteadyKalmanFilter estimator and:
  10 prediction steps Hp
   2 control steps Hc
-  1 manipulated inputs u
+  1 manipulated inputs u (0 integrators)
   2 states x̂
-  1 measured outputs ym
+  1 measured outputs ym (1 integrators)
   0 unmeasured outputs yu
   0 measured disturbances d
 ```

src/state_estim.jl

@@ -46,9 +46,9 @@ end
 function print_estim_dim(io::IO, estim::StateEstimator, n)
     nu, nd = estim.model.nu, estim.model.nd
     nx̂, nym, nyu = estim.nx̂, estim.nym, estim.nyu
-    println(io, "$(lpad(nu, n)) manipulated inputs u")
+    println(io, "$(lpad(nu, n)) manipulated inputs u ($(sum(estim.nint_u)) integrators)")
     println(io, "$(lpad(nx̂, n)) states x̂")
-    println(io, "$(lpad(nym, n)) measured outputs ym")
+    println(io, "$(lpad(nym, n)) measured outputs ym ($(sum(estim.nint_ym)) integrators)")
     println(io, "$(lpad(nyu, n)) unmeasured outputs yu")
     print(io,   "$(lpad(nd, n)) measured disturbances d")
 end